A Review of 40 Years of Cognitive Architecture Research: Focus on Perception, Attention, Learning and Applications.
---
paper_title: Toward a Unified Catalog of Implemented Cognitive Architectures

paper_content:

This work is a review of the online Comparative Table of Cognitive Architectures (the version that was available at http://bicasymposium.com/cogarch on September 20, 2010). This continuously updated online resource is a collective product of many researchers and developers of cognitive architectures. Names of its contributors (sorted alphabetically by the represented architecture name) are: James S. Albus (4D/RCS), Christian Lebiere and Andrea Stocco (ACT-R), Stephen Grossberg (ART), Brandon Rohrer (BECCA), Balakrishnan Chandrasekaran and Unmesh Kurup (biSoar), Raul Arrabales (CERA-CRANIUM), Fernand Gobet and Peter Lane (CHREST), Ron Sun (CLARION), Ben Goertzel (CogPrime), Frank Ritter and Rick Evertsz (CoJACK), George Tecuci (Disciple), Shane Mueller (EPIC), Susan L. Epstein (FORR), Stuart C. Shapiro (GLAIR), Alexei Samsonovich (GMU BICA), Jeff Hawkins (HTM), David C. Noelle (Leabra), Stan Franklin (LIDA), Pei Wang (NARS), Akshay Vashist and Shoshana Loeb (Nexting), Cyril Brom (Pogamut), Nick Cassimatis (Polyscheme), L. Andrew Coward (Recommendation Architecture), Ashok Goel, J. William Murdock and Spencer Rugaber (REM), John Laird (Soar), and Kristinn Thorisson (Ymir). All these contributions are summarized in this work in a form that makes the described architectures easy to compare against each other.
---
paper_title: Physical symbol systems

paper_content:

On the occasion of a first conference on Cognitive Science, it seems appropriate to review the basis of common understanding between the various disciplines. In my estimate, the most fundamental contribution so far of artificial intelligence and computer science to the joint enterprise of cognitive science has been the notion of a physical symbol system, i.e., the concept of a broad class of systems capable of having and manipulating symbols, yet realizable in the physical universe. The notion of symbol so defined is internal to this concept, so it becomes a hypothesis that this notion of symbols includes the symbols that we humans use every day of our lives. In this paper we attempt systematically, but plainly, to lay out the nature of physical symbol systems. Such a review is in ways familiar, but not thereby useless. Restatement of fundamentals is an important exercise.The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency, or the U.S. Government.Herb Simon would be a co-author of this paper, except that he is giving his own paper at this conference. The key ideas are entirely joint, as the references indicate.
---
paper_title: Précis of Unified theories of cognition.

paper_content:

The book presents the case that cognitive science should turn its attention to developing theories of human cognition that cover the full range of human perceptual, cognitive, and action phenomena. Cognitive science has now produced a massive number of high-quality regularities with many microtheories that reveal important mechanisms. The need for integration is pressing and will continue to increase. Equally important, cognitive science now has the theoretical concepts and tools to support serious attempts at unified theories. The argument is made entirely by presenting an exemplar unified theory of cognition both to show what a real unified theory would be like and to provide convincing evidence that such theories are feasible. The exemplar is SOAR, a cognitive architecture, which is realized as a software system. After a detailed discussion of the architecture and its properties, with its relation to the constraints on cognition in the real world and to existing ideas in cognitive science, SOAR is used as theory for a wide range of cognitive phenomena: immediate responses (stimulus-response compatibility and the Sternberg phenomena); discrete motor skills (transcription typing); memory and learning (episodic memory and the acquisition of skill through practice); problem solving (cryptarithmetic puzzles and syllogistic reasoning); language (sentence verification and taking instructions); and development (transitions in the balance beam task). The treatments vary in depth and adequacy, but they clearly reveal a single, highly specific, operational theory that works over the entire range of human cognition, SOAR is presented as an exemplar unified theory, not as the sole candidate. Cognitive science is not ready yet for a single theory - there must be multiple attempts. But cognitive science must begin to work toward such unified theories.
---
paper_title: A Hybrid Architecture for Situated Learning of Reactive Sequential Decision Making

paper_content:

In developing autonomous agents, one usually emphasizes only (situated) procedural knowledge, ignoring more explicit declarative knowledge. On the other hand, in developing symbolic reasoning models, one usually emphasizes only declarative knowledge, ignoring procedural knowledge. In contrast, we have developed a learning model CLARION, which is a hybrid connectionist model consisting of both localist and distributed representations, based on the two-level approach proposed in [40]. CLARION learns and utilizes both procedural and declarative knowledge, tapping into the synergy of the two types of processes, and enables an agent to learn in situated contexts and generalize resulting knowledge to different scenarios. It unifies connectionist, reinforcement, and symbolic learning in a synergistic way, to perform on-line, bottom-up learning. This summary paper presents one version of the architecture and some results of the experiments.
---
paper_title: Micro-Level Hybridization in the Cognitive Architecture DUAL

paper_content:

After a long and exhausting war between the representatives of the symbolic and connectionist approaches (this war stimulated, however, the clarification of the limitations and advantages of both approaches) a growing group of peace-makers emerged who tried to integrate the advantages of both approaches and to fill in the gap between them (Hendler, 1989a, Hinton, 1990, Barnden & Pollack, 1991, Thornton, 1991, Sun & Bookman, 1992, 1994, Dinsmore, 1992, Holyoak & Barnden, 1994). However, a mini-war started between the peace-makers themselves on the issue how to sign the peace treaty: with the surrender of one of the approaches or with their parity. Some researchers supported the connectionist-to-the-top view that symbol structures and symbol processing should emerge from the work of a neural network (called a unified approach in chapters 2 and 4 of this volume and connectionist symbol processing in (Pollack, 1990, Smolensky, 1990, Touretzky, 1990, Smolensky et al., 1992, Smolensky, 1995)), while others supported the synergistic hybrid approach bringing together connectionist and symbolic machines in a single system or model (Hendler, 1989b, 1991, Lange & Dyer, 1989, Sun, 1992). Strange enough no one suggested to build up connectionist systems on the top of a symbolic system.
---
paper_title: Cognitive Architectures : Choreographing the Dance of Mental Operations With the Task Environment

paper_content:

Objective: In this article, I present the ideas and trends that have given rise to the use of cognitive architectures in human factors and provide a cognitive engineering—oriented taxonomy of these architectures and a snapshot of their use for cognitive engineering. Background: Architectures of cognition have had a long history in human factors but a brief past. The long history entails a 50-year preamble, whereas the explosion of work in the current decade reflects the brief past. Understanding this history is key to understanding the current and future prospects for applying cognitive science theory to human factors practice. Method: The review defines three formative eras in cognitive engineering research: the 1950s, 1980s, and now. Results: In the first era, the fledging fields of cognitive science and human factors emphasized characteristics of the dancer, the limited capacity or bounded rationality view of the mind, and the ballroom, the task environment. The second era emphasized the dance (i.e., t...
---
paper_title: Physical symbol systems

paper_content:

On the occasion of a first conference on Cognitive Science, it seems appropriate to review the basis of common understanding between the various disciplines. In my estimate, the most fundamental contribution so far of artificial intelligence and computer science to the joint enterprise of cognitive science has been the notion of a physical symbol system, i.e., the concept of a broad class of systems capable of having and manipulating symbols, yet realizable in the physical universe. The notion of symbol so defined is internal to this concept, so it becomes a hypothesis that this notion of symbols includes the symbols that we humans use every day of our lives. In this paper we attempt systematically, but plainly, to lay out the nature of physical symbol systems. Such a review is in ways familiar, but not thereby useless. Restatement of fundamentals is an important exercise.The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency, or the U.S. Government.Herb Simon would be a co-author of this paper, except that he is giving his own paper at this conference. The key ideas are entirely joint, as the references indicate.
---
paper_title: Cognitive architectures: Research issues and challenges

paper_content:

In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area.
---
paper_title: Précis of Unified theories of cognition.

paper_content:

The book presents the case that cognitive science should turn its attention to developing theories of human cognition that cover the full range of human perceptual, cognitive, and action phenomena. Cognitive science has now produced a massive number of high-quality regularities with many microtheories that reveal important mechanisms. The need for integration is pressing and will continue to increase. Equally important, cognitive science now has the theoretical concepts and tools to support serious attempts at unified theories. The argument is made entirely by presenting an exemplar unified theory of cognition both to show what a real unified theory would be like and to provide convincing evidence that such theories are feasible. The exemplar is SOAR, a cognitive architecture, which is realized as a software system. After a detailed discussion of the architecture and its properties, with its relation to the constraints on cognition in the real world and to existing ideas in cognitive science, SOAR is used as theory for a wide range of cognitive phenomena: immediate responses (stimulus-response compatibility and the Sternberg phenomena); discrete motor skills (transcription typing); memory and learning (episodic memory and the acquisition of skill through practice); problem solving (cryptarithmetic puzzles and syllogistic reasoning); language (sentence verification and taking instructions); and development (transitions in the balance beam task). The treatments vary in depth and adequacy, but they clearly reveal a single, highly specific, operational theory that works over the entire range of human cognition, SOAR is presented as an exemplar unified theory, not as the sole candidate. Cognitive science is not ready yet for a single theory - there must be multiple attempts. But cognitive science must begin to work toward such unified theories.
---
paper_title: Cognitive Architectures: Where do we go from here?

paper_content:

Cognitive architectures play a vital role in providing blueprints for building future intelligent systems supporting a broad range of capabilities similar to those of humans. How useful are existing architectures for creating artificial general intelligence? A critical survey of the state of the art in cognitive architectures is presented providing a useful insight into the possible frameworks for general intelligence. Grand challenges and an outline of the most promising future directions are described.
---
paper_title: Glocal memory: A critical design principle for artificial brains and minds

paper_content:

The concept of glocal memory (i.e. memory involving systematic coordination between localized memory traces and globalized dynamical-attractor-based memory traces) is reviewed, and is argued to be a critical principle for the design of artificial brains and artificial general intelligence systems. Some exploratory experiments are reviewed, involving introduction of glocal memory into Hopfield neural networks, and also into economic attention networks as utilized in the OpenCog and Novamente integrated AI architectures.
---
paper_title: A neural approach to adaptive behavior and multi-sensor action selection in a mobile device

paper_content:

Sampling multisensory information and taking the appropriate motor action is critical for a biological organism's survival, but a difficult task for robots. We present a Neurally Organized Mobile Adaptive Device (NOMAD), whose behavior is controlled by a simulated nervous system based on the anatomy and physiology of the vertebrate brain, that is capable of action selection in a real world environment. NOMAD's nervous system consists of an auditory system, a visual system, a taste system, sets of motor neurons capable of triggering behavior, a tracking system driven by visual stimuli, and a value system. The device itself, which moves autonomously, has a CCD camera for vision, microphones for hearing, and a gripper manipulator to pick up and taste objects by measuring the object's conductivity. Similar to a biological organism, NOMAD learns to categorize sensory information from its environment with no prior instruction, associate positive and negative value with this sensory information, and then learn to select the appropriate motor actions. We suggest that this neurobiological approach to action selection may be generalized to other robot systems.
---
paper_title: Stereo vision–based navigation for autonomous surface vessels

paper_content:

This paper describes a stereo vision–based system for autonomous navigation in maritime environments. The system consists of two key components. The Hammerhead vision system detects geometric hazards (i.e., objects above the waterline) and generates both grid-based hazard maps and discrete contact lists (objects with position and velocity). The R4SA (robust, real-time, reconfigurable, robotic system architecture) control system uses these inputs to implement sensor-based navigation behaviors, including static obstacle avoidance and dynamic target following. As far as the published literature is concerned, this stereo vision–based system is the first fielded system that is tailored for high-speed, autonomous maritime operation on smaller boats. In this paper, we present a description and experimental analysis of the Hammerhead vision system, along with key elements of the R4SA control system. We describe the integration of these systems onto a number of high-speed unmanned surface vessels and present experimental results for the combined vision-based navigation system. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.
---
paper_title: A cognitive approach to vision for a mobile robot

paper_content:

We describe a cognitive vision system for a mobile robot. This system works in a manner similar to the human vision ::: system, using saccadic, vergence and pursuit movements to extract information from visual input. At each fixation, ::: the system builds a 3D model of a small region, combining information about distance, shape, texture and motion. ::: These 3D models are embedded within an overall 3D model of the robot's environment. This approach turns the ::: computer vision problem into a search problem, with the goal of constructing a physically realistic model of the entire ::: environment. ::: At each step, the vision system selects a point in the visual input to focus on. The distance, shape, texture and motion ::: information are computed in a small region and used to build a mesh in a 3D virtual world. Background knowledge is ::: used to extend this structure as appropriate, e.g. if a patch of wall is seen, it is hypothesized to be part of a large wall ::: and the entire wall is created in the virtual world, or if part of an object is recognized, the whole object's mesh is ::: retrieved from the library of objects and placed into the virtual world. The difference between the input from the real ::: camera and from the virtual camera is compared using local Gaussians, creating an error mask that indicates the main ::: differences between them. This is then used to select the next points to focus on. ::: This approach permits us to use very expensive algorithms on small localities, thus generating very accurate models. It ::: also is task-oriented, permitting the robot to use its knowledge about its task and goals to decide which parts of the ::: environment need to be examined. ::: The software components of this architecture include PhysX for the 3D virtual world, OpenCV and the Point Cloud ::: Library for visual processing, and the Soar cognitive architecture, which controls the perceptual processing and robot ::: planning. The hardware is a custom-built pan-tilt stereo color camera. ::: We describe experiments using both static and moving objects.
---
paper_title: A developing sensory mapping for robots

paper_content:

A sensory mapping method, called staggered hierarchical mapping (SHM), and its developmental algorithm are described in this paper SHM is a model motivated by human early, visual pathways including processing performed by the retina, lateral geniculate nucleus (LGN) and the primary visual cortex. The work reported here concerns not only the design of such a series of processors but also their autonomous development. A new incremental principal component analysis (IPCA) method is used to automatically develop orientation sensitive and other needed filters. A set of staggered receptive fields model the pattern of positioning of processing cells. From sequentially sensed video frames, the proposed algorithm develops a hierarch, of filters, whose outputs are uncorrelated within each layer but with increasing scale of receptive fields from low to high layers. To study, the completeness of the representation generated by, the SHM, we experimentally, show that the response produced at any layer is sufficient to reconstruct the corresponding "retinal" image to a great degree. In our experiment, we show that SHM can be used to perform local analysis.
---
paper_title: The Role of Competitive Inhibition and Top-Down Feedback in Binding during Object Recognition

paper_content:

How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain's binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. Top-down feedback contributes to binding in a similar manner, but by reinforcing relevant features. Together, inhibition and top-down feedback contribute to a competitive environment that ensures only the most appropriate features are bound together. We demonstrate this overall proposal using a biologically realistic neural model of vision that processes features across a hierarchy of interconnected brain areas. Finally, we argue that temporal synchrony plays only a limited role in binding -- it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features. Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes.
---
paper_title: Visual Binding Through Reentrant Connectivity and Dynamic Synchronization in a Brain-based Device

paper_content:

Effective visual object recognition requires mechanisms to bind object features (e.g. color, shape and motion) while distinguishing distinct objects. Synchronously active neuronal circuits among reentrantly connected cortical areas may provide a basis for visual binding. To assess the potential of this mechanism, we have constructed a mobile brain-based device, Darwin VIII, which is guided by simulated analogues of cortical and sub-cortical areas required for visual processing, decision-making, reward and motor responses. These simulated areas are reentrantly connected and each area contains neuronal units representing both the mean activity level and the relative timing of the activity of groups of neurons. Darwin VIII learns to discriminate among multiple objects with shared visual features and associates ‘target’ objects with innately preferred auditory cues. We observed the co-activation of globally distributed neuronal circuits that corresponded to distinct objects in Darwin VIII’s visual field. These circuits, which are constrained by a reentrant neuroanatomy and modulated by behavior and synaptic plasticity, are necessary for successful discrimination. By situating Darwin VIII in a rich real-world environment involving continual changes in the size and location of visual stimuli due to self-generated movement, and by recording its behavioral and neuronal responses in detail, we were able to show that reentrant connectivity and dynamic synchronization provide an effective mechanism for binding the features of visual objects.
---
paper_title: Fast, reliable, adaptive, bimodal people tracking for indoor environments

paper_content:

We present a real-time system for a mobile robot that can reliably detect and track people in uncontrolled indoor environments. The system uses a combination of leg detection based on distance information from a laser range sensor and visual face detection based on an analogical algorithm implemented on specialized hardware (the CNN universal machine). Results from tests in a variety of environments with different lighting conditions, a different number of appearing and disappearing people, and different obstacles are reported to demonstrate that the system can find and subsequently track several, possibly people simultaneously in indoor environments. Applications of the system include in particular service robots for social events.
---
paper_title: Global symbolic maps from local navigation

paper_content:

In order to navigate autonomously, most robot systems are provided with some sort of global terrain map. To make storage practical, these maps usually have a high-level symbolic representation of the terrain. The robot's symbolic map is then used to plan a local path. This paper describes a system which uses the reverse (and perhaps more natural) process. This system processes local sensor data in such a way as to allow efficient, reactive local navigation. A byproduct of this navigation process is an abstraction of the terrain information which forms a global symbolic terrain map of the terrain through which the robot has passed. Since this map is in the same format as that used by the local navigation system, the map is easy for the system to use, augment, or correct. Compared with the data from which the maps are created, the maps are very space efficient, and can be modified, or used for navigation in real-time. Experiments with this system, both in simulation, and with a real robot operating in natural terrain, are described.
---
paper_title: Integrating Deep Learning Based Perception with Probabilistic Logic via Frequent Pattern Mining

paper_content:

The bridging of the gap between 1) subsymbolic pattern recognition and learning algorithms and 2) symbolic reasoning algorithms, has been a major issue for AI since the early days of the field. One class of approaches involves integrating subsymbolic and symbolic systems, but this raises the question of how to effectively translate between the very different languages involved. In the approach described here, a frequent subtree mining algorithm is used to identify recurrent patterns in the state of a hierarchical deep learning system (DeSTIN) that is exposed to visual stimuli. The relationships between state-subtrees and percepts are then input to a probabilistic logic system (OpenCog's Probabilistic Logic Networks), which conducts uncertain inferences using them as axioms. The core conceptual idea is to use patterns in the states inferred by a perceptual hierarchy, as inputs to an uncertain logic system. Simple illustrative examples are presented based on the presentation of images of typed letters to DeSTIN. This work forms a component of a larger project to integrate perceptual, motoric and cognitive processing within the integrative OpenCog cognitive architecture.
---
paper_title: The cognitive architecture of a robotic salesman

paper_content:

This paper describes a robotics cognitive architecture for social robots named CORTEX. This architecture integrates different levels of abstraction (from basic geometry to high-level predicates) into a unique Deep Space Representation (DSR) that different agents interface. These agents update the contents of the DSR with new data from the outer world, and execute, plan and design behaviours. The design of CORTEX as an unified deep representation allows to fit both the subsymbolic processing and flexibility requirements of robot control. In this paper a first implementation of CORTEX has been integrated into Gualzru, a robotic salesman, and tested in real scenarios. Results show that this cognitive architecture allows this robot to adequately execute its use case, and that it has a promising adaptability to achieve new tasks and be used in new scenarios.
---
paper_title: Adaptive learning application of the MDB evolutionary cognitive architecture in physical agents

paper_content:

This work is concerned with the study of the application of the MDB (Multilevel Darwinist Brain) evolution based Cognitive Architecture in real robots performing adaptive learning tasks The experiments described here display the capabilities of this architecture when dealing with tasks that involve real time learning from a teacher and real time adaptation to changes in the goals provided or the communication pattern used by the teacher One of the consequences of the interaction of the robot with the environment through the MDB is the generation of induced behaviors that allow the robot to continue its operation when no teacher is present The experiments were carried out using a Sony AIBO robot and a Pioneer 2 robot with the same mechanism running on both just to demonstrate the robustness of the approach.
---
paper_title: Evolution of gamebots project

paper_content:

GameBots is a project started in early 2000s by A. N. Marshall and G. A. Kaminka. The project aims at providing researchers a real-time virtual environment testbed for their agents. GameBots utilized environment of Unreal Tournament first-person shooter game providing several scenarios for the agents. GameBots project was continued by several research groups resulting in many interesting applications. In this paper we summarize evolution of the GameBots project and contributions made since the first appearance. We focus on Pogamut GameBots branch which has been steadily developed for six years with many improvements and optimizations.
---
paper_title: Encyclopedia of Machine Learning and Data Mining

paper_content:

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic. Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature. The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.
---
paper_title: An embodied biologically constrained model of foraging: from classical and operant conditioning to adaptive real-world behavior in DAC-X

paper_content:

Animals successfully forage within new environments by learning, simulating and adapting to their surroundings. The functions behind such goal-oriented behavior can be decomposed into 5 top-level objectives: 'how', 'why', 'what', 'where', 'when' (H4W). The paradigms of classical and operant conditioning describe some of the behavioral aspects found in foraging. However, it remains unclear how the organization of their underlying neural principles account for these complex behaviors. We address this problem from the perspective of the Distributed Adaptive Control theory of mind and brain (DAC) that interprets these two paradigms as expressing properties of core functional subsystems of a layered architecture. In particular, we propose DAC-X, a novel cognitive architecture that unifies the theoretical principles of DAC with biologically constrained computational models of several areas of the mammalian brain. DAC-X supports complex foraging strategies through the progressive acquisition, retention and expression of task-dependent information and associated shaping of action, from exploration to goal-oriented deliberation. We benchmark DAC-X using a robot-based hoarding task including the main perceptual and cognitive aspects of animal foraging. We show that efficient goal-oriented behavior results from the interaction of parallel learning mechanisms accounting for motor adaptation, spatial encoding and decision-making. Together, our results suggest that the H4W problem can be solved by DAC-X building on the insights from the study of classical and operant conditioning. Finally, we discuss the advantages and limitations of the proposed biologically constrained and embodied approach towards the study of cognition and the relation of DAC-X to other cognitive architectures.
---
paper_title: Online multiple instance learning applied to hand detection in a humanoid robot

paper_content:

We propose an algorithm for the visual detection and localisation of the hand of a humanoid robot. This algorithm imposes low requirements on the type of supervision required to achieve good performance. In particular the system performs feature selection and adaptation using images that are only labelled as containing the hand or not, without any explicit segmentation. Our algorithm is an online variant of Multiple Instance Learning based on boosting. Experiments in real-world conditions on the iCub humanoid robot confirm that the algorithm can learn the visual appearance of the hand, reaching an accuracy comparable with its off-line version. This remains true when supervision is generated by the robot itself in a completely autonomous fashion. Algorithms with weak supervision requirements like the one we describe are useful for autonomous robots that learn and adapt online to a changing environment. The algorithm is not hand-specific and could be easily applied to wide range of problems involving visual recognition of generic objects.
---
paper_title: Populating VBS2 with Realistic Virtual Actors

paper_content:

Synthetic environments, particularly those that are built upon video games engines, offer impressive photo-realistic rendering of 3D environments. In parallel, human behaviour representations are becoming increasingly rich, building upon results from the cognitive and affective sciences. This paper reports on the integration of the cognitive architecture, CoJACK™ with the 3D training environment, VBS2™. CoJACK is a BDI (Beliefs/Desire/ Intentions) cognitive architecture that has a tightly integrated moderator layer, supporting the investigation of the effects of affect on decision making. A suicide bomber scenario is described in which "fear" and "morale" moderators significantly influence the behaviour of the virtual actors. The resulting behaviour is compellingly realistic and human observers readily attribute and believe in an account of the motivational underpinnings of the actors' actions. The integration of CoJACK with VBS2 offers the prospect of much richer training environments where virtual actors behave in a variable though repeatable fashion, providing trainees with a truly challenging situation.
---
paper_title: Model-Free Learning and Control in a Mobile Robot

paper_content:

A model-free, biologically-motivated learning and control algorithm called S-learning is described as implemented in an Surveyor SRV-1 mobile robot. S-learning demonstrated learning of robotic and environmental structure sufficient to allow it to achieve its goals (finding high- or low-contrast views in its environment). No modeling information about the task or calibration information about the robot’s actuators and sensors were used in S-learning’s planning. The ability of S-learning to make movement plans was completely dependent on experience it gained as it explored. Initially it had no experience and was forced to wander randomly. With increasing exposure to the task, S-learning achieved its goals with more nearly optimal paths. The fact that this approach is model-free implies that it may be applied to many other systems, perhaps even to systems of much greater complexity.
---
paper_title: Stereo vision for planetary rovers: stochastic modeling to near real-time implementation

paper_content:

NASA scenarios for lunar and planetary missions include robotic vehicles that function in both teleoperated and semi-autonomous modes. Under teleoperation, on-board stereo cameras may provide 3-D scene information to human operators via stereographic displays; likewise, under semi-autonomy, machine stereo vision may provide 3-D information for obstacle avoidance. In the past, the slow speed of machine stereo vision systems has posed a hurdle to the semi- autonomous scenario; however, recent work at JPL and other laboratories has produced stereo vision systems with high reliability and near real-time performance for low-resolution image pairs. In particular, JPL has taken a significant step by achieving the first autonomous, cross- country robotic traverses (of up to 100 meters to use stereo vision, with all computing on- board the vehicle. This paper describes the stereo vision system, including the underlying statistical model and the details of the implementation. The statistical and algorithmic aspects employ random field models of the disparity map, Bayesian formulations of single-scale matching, and area-based image comparisons. The implementations builds Laplacian image pyramids and produces disparity maps from the 60X64 level of the pyramids at rates of up to two seconds per image pair. All vision processing is done on one 68020 augmented with Datacube image processing boards. The author argues that the overall approach provides a unifying paradigm for practical, domain-independent stereo ranging.
---
paper_title: Toward Social Cognition in Robotics: Extracting and Internalizing Meaning from Perception

paper_content:

One of the long-term objectives of artificial cognition is that robots will increas- ingly be capable of interacting with their hu- man counterparts in open-ended tasks that can change over time. To achieve this end, the robot should be able to acquire and internalize new knowledge from human-robot interaction, on- line. This implies that the robot should attend and perceive the available cues, both verbal and nonverbal, that contain information about the inner qualities of the human counterparts. Social cognition focuses on the perceiver's ability to build cognitive representations of actors (emotions, intentions, . . . ) and their contexts. These representations should provide meaning to the sensed inputs and mediate the behavioural responses of the robot within this social scenario. This paper describes how the abilities for building such as cognitive representations are currently endowing in the cognitive software architecture RoboCog. It also presents a first set of complete experi- ments, involving different user profiles. These experiments show the promising possibilities of the proposal, and reveal the main future improvements to be addressed. Index Terms—Perception, verbal interac- tion, human-oriented perception, inner model.
---
paper_title: An Integrative Methodology for Teaching Embodied Non-Linguistic Agents, Applied to Virtual Animals in Second Life

paper_content:

A teaching methodology called Imitative-Reinforcement-Corrective (IRC) learning is described, and proposed as a general approach for teaching embodied non-linguistic AGI systems. IRC may be used with a variety of different learning algorithms, but it is particularly easily described in EC lingo. In these terms, it is a framework for automatically learning a procedure that generates a desired type of behavior, in which: a set of exemplars of the target behavior-type are utilized for fitness estimation; reinforcement signals from a human teacher are used for fitness evaluation; and the execution of candidate procedures may be modified by the teacher via corrections delivered in real-time. An example application of IRC to teach behaviors to AI-controlled artificial animals embodied in the Second Life virtual world is described in detail, including a review of the overall virtual-animal-control software architecture and how the integrative teaching/learning methodology fits into it. In this example application architecture, the learning algorithm may be toggled between hillclimbing and probabilistic evolutionary learning. Envisioned future applications are also discussed, including an application to embodied language learning applicable to agents in Second Life and other virtual worlds.
---
paper_title: Challenges in Building Robots That Imitate People

paper_content:

Abstract : Human (and some other animals) acquire new skills socially through direct tutelage, observational conditioning, goal emulation, imitation, and other methods (Galef, 1988; Hauser, 1996). These social learning skills provide a powerful mechanism for an observer to acquire behaviors and knowledge from a skilled individual (the model). In particular, imitation is an extremely powerful mechanism for social learning which has received a great deal of interest from researchers in the fields of animal behavior and child development.
---
paper_title: DIARC: A Testbed for Natural Human-Robot Interaction

paper_content:

DIARC, a distributed integrated affect, reflection, cognition architecture for robots, provides many features that are critical to successful natural human-robot interaction. As such, DIARC is an ideal platform for experimentation in HRI. In this paper we describe the architecture and and its implementation in ADE, paying particular attention to its interaction capabilities and features that allow robust operation. These features are evaluated in the context of the 2006 AAAI Robot Competition.
---
paper_title: Developmental Robots - A New Paradigm

paper_content:

It has been proved to be extremely challenging for humans to program a robot to such a sufficient degree that it acts properly in a typical unknown human environment. This is especially true for a humanoid robot due to the very large number of redundant degrees of freedom and a large number of sensors that are required for a humanoid to work safely and effectively in the human environment. How can we address this fundamental problem? Motivated by human mental development from infancy to adulthood, we present a theory, an architecture, and some experimental results showing how to enable a robot to develop its mind automatically, through online, real time interactions with its environment. Humans mentally “raise” the robot through “robot sitting” and “robot schools” instead of task-specific robot programming.
---
paper_title: Recognition of affective communicative intent in robot-directed speech

paper_content:

Human speech provides a natural and intuitive interface for both communicating with humanoid robots as well as for teaching them. In general, the acoustic pattern of speech contains three kinds of information: who the speaker is, what the speaker said, and how the speaker said it. This paper focuses on the question of recognizing affective communicative intent in robot-directed speech without looking into the linguistic content. We present an approach for recognizing four distinct prosodic patterns that communicate praise, prohibition, attention, and comfort to preverbal infants. These communicative intents are well matched to teaching a robot since praise, prohibition, and directing the robot's attention to relevant aspects of a task, could be used by a human instructor to intuitively facilitate the robot's learning process. We integrate this perceptual ability into our robot's “emotion” system, thereby allowing a human to directly manipulate the robot's affective state. This has a powerful organizing influence on the robot's behavior, and will ultimately be used to socially communicate affective reinforcement. Communicative efficacy has been tested with people very familiar with the robot as well as with naïve subjects.
---
paper_title: Learning a Song : an ACT-R Model

paper_content:

The way music is interpreted by the human brain is a very interesting topic, but also an intricate one. Although this domain has been studied for over a century, many gray areas remain in the understanding of music. Recent advances have enabled us to perform accurate measurements of the time taken by the human brain to interpret and assimilate a sound. Cognitive computing provides tools and development environments that facilitate human cognition simulation. ACT-R is a cognitive architecture which offers an environment for implementing human cognitive tasks. This project combines our understanding of the music interpretation by a human listener and the ACT-R cognitive architecture to build SINGER, a computerized simulation for listening and recalling songs. The results are similar to human experimental data. Simulation results also show how it is easier to remember short melodies than long melodies which require more trials to be recalled correctly. Keywords—Computational Model; Cognitive Modeling, Simula- tion; Learning; Song; Music.
---
paper_title: Visual Binding Through Reentrant Connectivity and Dynamic Synchronization in a Brain-based Device

paper_content:

Effective visual object recognition requires mechanisms to bind object features (e.g. color, shape and motion) while distinguishing distinct objects. Synchronously active neuronal circuits among reentrantly connected cortical areas may provide a basis for visual binding. To assess the potential of this mechanism, we have constructed a mobile brain-based device, Darwin VIII, which is guided by simulated analogues of cortical and sub-cortical areas required for visual processing, decision-making, reward and motor responses. These simulated areas are reentrantly connected and each area contains neuronal units representing both the mean activity level and the relative timing of the activity of groups of neurons. Darwin VIII learns to discriminate among multiple objects with shared visual features and associates ‘target’ objects with innately preferred auditory cues. We observed the co-activation of globally distributed neuronal circuits that corresponded to distinct objects in Darwin VIII’s visual field. These circuits, which are constrained by a reentrant neuroanatomy and modulated by behavior and synaptic plasticity, are necessary for successful discrimination. By situating Darwin VIII in a rich real-world environment involving continual changes in the size and location of visual stimuli due to self-generated movement, and by recording its behavioral and neuronal responses in detail, we were able to show that reentrant connectivity and dynamic synchronization provide an effective mechanism for binding the features of visual objects.
---
paper_title: The PlayMate System

paper_content:

Research in CoSy was scenario driven. Two scenarios were created, the Play- Mate and the Explorer. One of the integration goals of the project was to build integrated systems that addressed the tasks in these two scenarios. This chapter concerns the integrated system for the PlayMate scenario.
---
paper_title: The role of knowledge and certainty in understanding for dialogue

paper_content:

As people engage in increasingly complex conversations with computers, the need for generality and flexibility in spoken dialogue systems becomes more apparent. This paper describes how three different spoken dialogue systems for the same task reason with knowledge and certainty as they seek to understand what people want. It advocates systems that exploit partial understanding, consider credibility, and are aware both of what they know and of their certainty that it matches their users’ intent.
---
paper_title: The cognitive architecture of a robotic salesman

paper_content:

This paper describes a robotics cognitive architecture for social robots named CORTEX. This architecture integrates different levels of abstraction (from basic geometry to high-level predicates) into a unique Deep Space Representation (DSR) that different agents interface. These agents update the contents of the DSR with new data from the outer world, and execute, plan and design behaviours. The design of CORTEX as an unified deep representation allows to fit both the subsymbolic processing and flexibility requirements of robot control. In this paper a first implementation of CORTEX has been integrated into Gualzru, a robotic salesman, and tested in real scenarios. Results show that this cognitive architecture allows this robot to adequately execute its use case, and that it has a promising adaptability to achieve new tasks and be used in new scenarios.
---
paper_title: ISAC Humanoid: An Architecture for Learning and Emotion

paper_content:

A report on the development of a software architecture to facilitate interactions between a person and a humanoid robot is given. The architecture employs independent, asynchronous software agents that run in parallel on a set of PCs in a local area network. It comprises four major components: a Self Agent which controls the robot’s general interactions with the world and monitors its own status, a human agent which models the person and controls the robot’s interaction with her or him, a spatiotemporal short-term memory called the Sensory EgoSphere, and a long-term memory called the DataBase Associative Memory. The structures of these components and a description of their interactions is given. Current research efforts in learning and emotion are described. Outstanding problems are listed. Keywords– Robotics, Humanoids, Software agents, Affective Computing, Kansei, Sensory Ego-Sphere.
---
paper_title: Enabling Basic Normative HRI in a Cognitive Robotic Architecture

paper_content:

Collaborative human activities are grounded in social and moral norms, which humans consciously and subconsciously use to guide and constrain their decision-making and behavior, thereby strengthening their interactions and preventing emotional and physical harm. This type of norm-based processing is also critical for robots in many human-robot interaction scenarios (e.g., when helping elderly and disabled persons in assisted living facilities, or assisting humans in assembly tasks in factories or even the space station). In this position paper, we will briefly describe how several components in an integrated cognitive architecture can be used to implement processes that are required for normative human-robot interactions, especially in collaborative tasks where actions and situations could potentially be perceived as threatening and thus need a change in course of action to mitigate the perceived threats.
---
paper_title: Enabling effective human-robot interaction using perspective-taking in robots

paper_content:

We propose that an important aspect of human-robot interaction is perspective-taking. We show how perspective-taking occurs in a naturalistic environment (astronauts working on a collaborative project) and present a cognitive architecture for performing perspective-taking called Polyscheme. Finally, we show a fully integrated system that instantiates our theoretical framework within a working robot system. Our system successfully solves a series of perspective-taking problems and uses the same frames of references that astronauts do to facilitate collaborative problem solving with a person.
---
paper_title: Salience-driven Contextual Priming of Speech Recognition for Human-Robot Interaction

paper_content:

The paper presents an implemented model for priming speech recognition, using contextual information about salient entities. The underlying hypothesis is that, in human-robot interaction, speech recognition performance can be improved by exploiting knowledge about the immediate physical situation and the dialogue history. To this end, visual salience (objects perceived in the physical scene) and linguistic salience (objects, events already mentioned in the dialogue) are integrated into a single cross-modal salience model. The model is dynamically updated as the environment changes. It is used to establish expectations about which words are most likely to be heard in the given context. The update is realised by continuously adapting the word-class probabilities specified in a statistical language model. The paper discusses the motivations behind the approach, and presents the implementation as part of a cognitive architecture for mobile robots. Evaluation results on a test suite show a statistically significant improvement of salience-driven priming speech recognition (WER) over a commercial baseline system.
---
paper_title: Mind model for multimodal communicative creatures and humanoids

paper_content:

This paper presents a computational model of real - time task - oriented dialog skills . The model , termed Ymir, bridges multimodal perception and multimodal action and supports the creation of autonomous computer characters that afford full - duplex , real - time face - to - face interaction with a human . Ymir has been prototyped in software , and a humanoid created , called Gandalf, capable of fluid multimodal dialog . Ymir demonstrates several new ideas in the creation of communicative computer agents , including perceptual integration of multimodal events, distributed planning and decision making, an explicit handling of real time, and perceptuo-motor system layered and motor control with human characteristics. This paper describes the model's architecture and explains its main elements . Examples of implementation and performance are given , and the architecture's limitations and possibilities are discussed .
---
paper_title: A Cognitive Architectural Account of Two-Channel Speech Processing

paper_content:

An important application of cognitive architectures is to provide human performance models that capture psychological mechanisms in a form that can be “programmed” to predict task performance of human-machine system designs. While many aspects of human performance have been successfully modeled in this approach, accounting for multi-talker speech task performance is a novel problem. This paper presents a model for performance in a two-talker task that incorporates concepts from the psychoacoustic study of speech perception, in particular, masking effects and stream formation.
---
paper_title: Workload Analysis of the Crew of the Abrams V2 SEP: Phase I Baseline IMPRINT Model

paper_content:

Abstract : Using the Improved Performance Research Integration Tool (IMPRINT), the U.S. Army Research Laboratory (ARL) Human Research and Engineering Directorate, (HRED) analysts developed a model to represent the tasks performed by each member of the Abrams version 2 with System Enhancement Package (V2 SEP) crew. They then used this model to predict the mental workload of the crew and its impacts on the performance of the crew of the Abrams V2 SEP. The phase I analysis and its workload predictions will serve as a baseline for a phase II analysis. The phase II analysis will predict the impact of specific additional technologies added to the Abrams on Soldier workload and performance.
---
paper_title: Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark

paper_content:

Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations, examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. Rewarding these characteristics is formalized in NAB, using a scoring algorithm designed for streaming data. NAB evaluates detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and give results and analyses for several open source, commercially-used algorithms. The goal for NAB is to provide a standard, open source framework with which the research community can compare and evaluate different algorithms for detecting anomalies in streaming data.
---
paper_title: ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network

paper_content:

Summary form only given. The authors introduced a neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of adaptive resonance theory modules (ART/sub a/ and ART/sub b/) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. Tested on a benchmark machine learning database in both online and offline simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half of the input patterns in the database. >
---
paper_title: Supporting the Commander's information requirements: Automated support for Battle Drill processes using R-CAST

paper_content:

This paper discusses a novel approach that addresses the problem of supporting the Commander's dynamic information requirements through automation of the Military Decision-Making Process (MDMP) for time-constrained environments and training purposes, as part of the Tactical Human Integration of Networked Knowledge (THINK) Army Technology Objective - Research (ATO-R) initiative. We demonstrate this capability with automated user support for the execution of battle drills. Our approach is based on adapting the R-CAST cognitively-inspired agent architecture towards a context-aware anticipation of information requirements. R-CAST is a computational model of the Recognition-Primed Decision (RPD) model, which models human decision making under time stress. R-CAST agents support and collaborate with human decision making teams as both “smart aids” and “effective teammates” by anticipating, investigating, seeking, and interpreting information relevant to decision making. A key feature of R-CAST is that the proactive sharing of information relevant to decision making is automatically generated by the computational RPD model. The fundamental research question being addressed is whether the inclusion of R-CAST in Army staff processes improves said staff understanding and execution of battle tasks. We adapted R-CAST to Battle Drill #26 (i.e., responding to an IED event) as a proof of concept for team decision making under stress and constant switching of modalities. We demonstrate that the use of R-CAST cognitive agents effectively assists the Battle Manager in the S3 cell with auto-filling certain forms required by doctrine in response to the dynamism of the current state of the environment, improving cognitive performance in this task. Our novel approach integrates relevant context in communication, information, and socio-cognitive networks, coupled with cognitive modeling. We report initial findings that we can use the R-CAST cognitive framework to effectively and efficiently develop individual intelligent training tools that understand and support the dynamic information requirements of Commanders.
---
paper_title: Omar Human Performance Modeling in a Decision Support Experiment

paper_content:

Human performance models that simulate the multiple task behaviors of the operators of complex systems are now being developed that can, with approporiate discretion, bu used to complement the numan players in real world like simulation experiment. The paper describes the development of human performance models for an air traffic control simulation that was the basis for a decision support system experiment with human subjects. The experiment is described and the roles played by the human performance models for air traffic controllers and flight crews are discussed. Future directions for research based, in part, on the experiment results are outlined.
---
paper_title: RCS: A Cognitive Architecture for Intelligent MultiAgent Systems

paper_content:

Abstract RCS (Real-time Control System) is a cognitive architecture designed to enable any level of intelligent behavior, up to and including human levels of performance. RCS was inspired 30 years ago by a theoretical model of the cerebellum, the portion of the brain responsible for fine motor coordination and control of conscious motions. It was originally designed for sensory-interactive goal-directed control of laboratory manipulators. Over three dec des, it has evolved into a real-time control architecture for intelligent machine tools, factory automation systems, and intelligent autonomous vehicles. RCS consists of a multi-layered multi-resolutional hierarchy of computational agents each containing elements of sensory processing (SP), world modeling (WM), value judgment (VJ), behavior generation (BG), and a knowledge database (KD). At the lower levels, these agents generate goal-seeking reactive behavior. At higher levels, they enable decision making, planning, and deliberative behavior. This paper is a product of U.S. Government employees in the course of their assigned duties, and therefore not subject to copyright.
---
paper_title: A Taxonomy of External and Internal Attention

paper_content:

Attention is a core property of all perceptual and cognitive operations. Given limited capacity to process competing options, attentional mechanisms select, modulate, and sustain focus on information most relevant for behavior. A significant problem, however, is that attention is so ubiquitous that it is unwieldy to study. We propose a taxonomy based on the types of information that attention operates over—the targets of attention. At the broadest level, the taxonomy distinguishes between external attention and internal attention. External attention refers to the selection and modulation of sensory information. External attention selects locations in space, points in time, or modality-specific input. Such perceptual attention can also select features defined across any of these dimensions, or object representations that integrate over space, time, and modality. Internal attention refers to the selection, modulation, and maintenance of internally generated information, such as task rules, responses, long-term memory, or working memory. Working memory, in particular, lies closest to the intersection between external and internal attention. The taxonomy provides an organizing framework that recasts classic debates, raises new issues, and frames understanding of neural mechanisms.
---
paper_title: Learning a Mixture of Search Heuristics

paper_content:

This chapter describes programs that solve constraint satisfaction problems with multiple heuristics. It demonstrates the varied efficacy of individual constraint-solving metrics and the potential power available from a mixture of heuristics that references them. It describes a weighted-mixture decision process, and explains how one autonomous learner constructs its own labeled training examples from its search experience, and then learns a weighted mixture from them. Four new techniques are introduced to manage a large body of conflicting heuristics, and illustrated with empirical results.
---
paper_title: Attention Mechanisms in the CHREST Cognitive Architecture

paper_content:

In this paper, we describe the attention mechanisms in CHREST, a computational architecture of human visual expertise. CHREST organises information acquired by direct experience from the world in the form of chunks . These chunks are searched for, and verified, by a unique set of heuristics, comprising the attention mechanism. We explain how the attention mechanism combines bottom-up and top-down heuristics from internal and external sources of information. We describe some experimental evidence demonstrating the correspondence of CHREST's perceptual mechanisms with those of human subjects. Finally, we discuss how visual attention can play an important role in actions carried out by human experts in domains such as chess.
---
paper_title: CHARISMA: A Context Hierarchy-based cognitive architecture for self-motivated social agents

paper_content:

In this paper, we are developing the CIVS (Civilization-Inspired Vying Societies) system, which is a novel evolutionary learning multi-agent system loosely inspired by the history of human civilization. The main objective of the CIVS system is to develop a bottom-up artificial-life approach to produce artificial agents that are inherently social in how they think, learn, adapt, and operate, so as to be more adaptable and generally intelligent in a complex, challenging environment. To this end, the CHARISMA (Context Hierarchy-based Adaptive ReasonIng Self-Motivated Agent) cognitive architecture is proposed for the agents within the CIVS system. By using intrinsic motivations and dynamic knowledge representation, the CHARISMA cognitive architecture can provide autonomous mental development capability for the agent to develop its knowledge and skills through its own interactions within a dynamic challenging environment as well as social interactions with other agents.
---
paper_title: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

paper_content:

A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail. Index Terms—Visual attention, scene analysis, feature extraction, target detection, visual search. ———————— F ————————
---
paper_title: A Computational Perspective on Visual Attention

paper_content:

Although William James declared in 1890, "Everyone knows what attention is," today there are many different and sometimes opposing views on the subject. This fragmented theoretical landscape may be because most of the theories and models of attention offer explanations in natural language or in a pictorial manner rather than providing a quantitative and unambiguous statement of the theory. They focus on the manifestations of attention instead of its rationale. In this book, John Tsotsos develops a formal model of visual attention with the goal of providing a theoretical explanation for why humans (and animals) must have the capacity to attend. He takes a unique approach to the theory, using the full breadth of the language of computation--rather than simply the language of mathematics--as the formal means of description. The result, the Selective Tuning model of vision and attention, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. The overarching conclusion is that human vision is based on a general purpose processor that can be dynamically tuned to the task and the scene viewed on a moment-by-moment basis. Tsotsos offers a comprehensive, up-to-date overview of attention theories and models and a full description of the Selective Tuning model, confining the formal elements to two chapters and two appendixes. The text is accompanied by more than 100 illustrations in black and white and color; additional color illustrations and movies are available on the book's Web site
---
paper_title: Object Learning Through Active Exploration

paper_content:

This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts.
---
paper_title: Multimodal saliency-based bottom-up attention a framework for the humanoid robot iCub

paper_content:

This work presents a multimodal bottom-up attention system for the humanoid robot iCub where the robot's decisions to move eyes and neck are based on visual and acoustic saliency maps. We introduce a modular and distributed software architecture which is capable of fusing visual and acoustic saliency maps into one egocentric frame of reference. This system endows the iCub with an emergent exploratory behavior reacting to combined visual and auditory saliency. The developed software modules provide a flexible foundation for the open iCub platform and for further experiments and developments, including higher levels of attention and representation of the peripersonal space.
---
paper_title: Incremental Object Perception in an Attention-Driven Cognitive Architecture.

paper_content:

With few exceptions, architectural approaches to modeling cognition have historically emphasized what happens in the mind following the transduction of environmental signals into percepts. To our knowledge, none of these architectures implements a sophisticated, general theory of human attention. In this paper we summarize progress to date on a new cognitive architecture called ARCADIA that gives a central role to attention in both perception and cognition. First, we give an overview of the architecture, comparing it to other approaches when appropriate. Second, we present a model of incremental object construction and property binding in ARCADIA using the well known change blindness phenomena to illustrate the time course of object perception and its dependence on attention. Finally, we discuss near-term challenges and future plans.
---
paper_title: Operability Model Architecture Demonstration Final Report

paper_content:

Abstract : The Operator Model Architecture (OMAR) is an integrated suite of software tools to support the construction of Human Performance Process (HPP) models. This report documents the use of OMAR to create and test HPP models for Air Traffic Control (ATC) and research in anthropometric human modeling. In addition, this report documents the development of new model analysis tools.
---
paper_title: Cognitive programs: software for attention's executive

paper_content:

What are the computational tasks that an executive controller for visual attention must solve? This question is posed in the context of the Selective Tuning model of attention. The range of required computations go beyond top-down bias signals or region-of-interest determinations, and must deal with overt and covert fixations, process timing and synchronization, information routing, memory, matching control to task, spatial localization, priming, and coordination of bottom-up with top-down information. During task execution, results must be monitored to ensure the expected results. This description includes the kinds of elements that are common in the control of any kind of complex machine or system. We seek a mechanistic integration of the above, in other words, algorithms that accomplish control. Such algorithms operate on representations, transforming a representation of one kind into another, which then forms the input to yet another algorithm. Cognitive Programs (CPs) are hypothesized to capture exactly such representational transformations via stepwise sequences of operations. CPs, an updated and modernized offspring of Ullman's Visual Routines, impose an algorithmic structure to the set of attentional functions and play a role in the overall shaping of attentional modulation of the visual system so that it provides its best performance. This requires that we consider the visual system as a dynamic, yet general-purpose processor tuned to the task and input of the moment. This differs dramatically from the almost universal cognitive and computational views, which regard vision as a passively observing module to which simple questions about percepts can be posed, regardless of task. Differing from Visual Routines, CPs explicitly involve the critical elements of Visual Task Executive (vTE), Visual Attention Executive (vAE), and Visual Working Memory (vWM). Cognitive Programs provide the software that directs the actions of the Selective Tuning model of visual attention.
---
paper_title: A Computational Implementation of a Human Attention Guiding Mechanism in MIDAS v5

paper_content:

In complex human-machine systems, the human operator is often required to intervene to detect and solve problems. Given this increased reliance on the human in these critical human-machine systems, there is an increasing need to validly predict how operators allocate their visual attention. This paper describes the information-seeking (attention-guiding) model within the Man-machine Integration Design and Analysis System (MIDAS) v5 software - a predictive model that uses the Salience, Effort, Expectancy and Value (SEEV) of an area of interest to guide a person's attention. The paper highlights the differences between using a probabilistic fixation approach and the SEEV approach in MIDAS to drive attention.
---
paper_title: A model of eye movements and visual attention

paper_content:

This paper introduces the EMMA model of eye movements and visual attention. EMMA provides a formal model of the temporal and spatial aspects of eye movements as they arise from shifts in visual attention. To implement the model, EMMA is integrated into the ACT-R/PM cognitive architecture (Anderson & Lebiere, 1998) and requires minimal modification of existing ACT-R/PM models. This paper details the EMMA model, theory, and implementation and also demonstrates how the extended framework helps to capture important aspects of behavior in an equation-solving task.
---
paper_title: A cognitive approach to multimodal attention

paper_content:

An adaptive attention mechanism is a requirement when an autonomous robot has to deal with real world environments. In this paper we present a novel cognitive architecture which enables integrated and efficient filtering of multiple modality sensory information. The proposed attention mechanism is based on contexts that determine what sensorimotor data is relevant to the current situation. These contexts are used as a mean to adaptively select constrained cognitive focus within the vast multimodal sensory space. In this framework, the focus of attention can be directed to meaningful complex percepts, thus allowing the implementation of higher cognitive capabilities. Sonar, contact, and visual sensory modalities have been used in the perception process, and the motor capability of the physical agent is provided by a differential wheel drive system. The testing of this artificial attention approach, carried out initially in the domain of counterpart recognition and chasing, has demonstrated both a great decrease in computation power requirements and ease of multimodal integration for cognitive representations.
---
paper_title: A Context-Dependent Attention System for a Social Robot

paper_content:

This paper presents part of an on-going project to integrate perception, attention, drives, emotions, behavior arbitration, and expressive acts for a robot designed to interact socially with humans. We present the design of a visual attention system based on a model of human visual search behavior from Wolfe (1994). The attention system integrates perceptions (motion detection, color saliency, and face popouts) with habituation effects and influences from the robot's motivational and behavioral state to create a context-dependent attention activation map. This activation map is used to direct eye movements and to satiate the drives of the motivational system.
---
paper_title: PASAR: An integrated model of prediction, anticipation, sensation, attention and response for artificial sensorimotor systems

paper_content:

A wide range of neuroscientific studies suggest the existence of cognitive mechanisms like attention, prediction, anticipation and strong vertical interactions between different hierarchical layers of the brain while performing complex tasks. Despite advances in both cognitive brain research and in the development of brain-inspired artificial cognitive systems, the interplay of these key ingredients of cognition remain largely elusive and unquantified in complex real-world tasks. Furthermore, it has not yet been demonstrated how a self-contained hierarchical cognitive system acting under limited resource constraints can quantifiably benefit from the incorporation of top-down and bottom-up attentional mechanisms. In this context, an open fundamental question is how a data association mechanism can integrate bottom-up sensory information and top-down knowledge. Here, building on the Distributed Adaptive Control (DAC) architecture, we propose a single framework for integrating these different components of cognition and demonstrate the framework's performance in solving real-world and simulated robot tasks. Using the model we quantify the interactions between prediction, anticipation, attention and memory. Our results support the strength of a complete system that incorporates attention, prediction and anticipation mechanisms compared to incomplete systems for real-world and complex tasks. We unveil the relevance of transient memory that underlines the utility of the above mechanisms for intelligent knowledge management in artificial sensorimotor systems. These findings provide concrete predictions for physiological and psychophysical experiments to validate our model in biological cognitive systems.
---
paper_title: Guided Search 2.0 A revised model of visual search

paper_content:

An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and/or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues.
---
paper_title: Pre-attentive and Attentive Vision Module

paper_content:

This paper introduces a new vision module, called PAAV, developed for the cognitive architecture ACT-R. Unlike ACT-R's default vision module that was originally developed for top-down perception only, PAAV was designed to model a wide range of tasks, such as visual search and scene viewing, where pre-attentive bottom-up processes are essential for the validity of a model. PAAV builds on attentive components of the default vision module and incorporates greater support for modeling pre-attentive components of human vision. The module design incorporates the best practices from existing models of vision. The validity of the module was tested on four different tasks.
---
paper_title: Elephants don't play chess

paper_content:

There is an alternative route to Artificial Intelligence that diverges from the directions pursued under that banner for the last thirty some years. The traditional approach has emphasized the abstract manipulation of symbols, whose grounding in physical reality has rarely been achieved. We explore a research methodology which emphasizes ongoing physical interaction with the environment as the primary source of constraint on the design of intelligent systems. We show how this methodology has recently had significant successes on a par with the most successful classical efforts. We outline plausible future work along these lines which can lead to vastly more ambitious systems.
---
paper_title: Human-like learning in a conscious agent

paper_content:

In this article, we propose the Conscious-Emotional Learning Tutoring System technology, a biologically plausible cognitive agent based on human brain functions. This agent is capable of learning and remembering events and any related information such as corresponding procedures, stimuli and their emotional valences. In our model, emotions play a role in the encoding and remembering of events. This allows the agent to improve its behaviour or by remembering previously selected behaviours which were influenced by its emotional mechanism. Moreover, the architecture incorporates a realistic memory consolidation process based on a data mining algorithm.
---
paper_title: Global workspace theory of consciousness: toward a cognitive neuroscience of human experience.

paper_content:

Abstract Global workspace (GW) theory emerged from the cognitive architecture tradition in cognitive science. Newell and co-workers were the first to show the utility of a GW or “blackboard” architecture in a distributed set of knowledge sources, which could cooperatively solve problems that no single constituent could solve alone. The empirical connection with conscious cognition was made by Baars (1988, 2002). GW theory generates explicit predictions for conscious aspects of perception, emotion, motivation, learning, working memory, voluntary control, and self systems in the brain. It has similarities to biological theories such as Neural Darwinism and dynamical theories of brain functioning. Functional brain imaging now shows that conscious cognition is distinctively associated with wide spread of cortical activity, notably toward frontoparietal and medial temporal regions. Unconscious comparison conditions tend to activate only local regions, such as visual projection areas. Frontoparietal hypometabolism is also implicated in unconscious states, including deep sleep, coma, vegetative states, epileptic loss of consciousness, and general anesthesia. These findings are consistent with the GW hypothesis, which is now favored by a number of scientists and philosophers.
---
paper_title: Agents with Shared Mental Models for Enhancing Team Decision-Makings

paper_content:

Proactive information sharing is a challenging issue faced by intelligence agencies in effectively making critical decisions under time pressure in areas related to homeland security. Motivated by psychological studies on human teams, a team-oriented agent architecture, Collaborative Agents for Simulating Teamwork (CAST), was implemented to allow agents in a team to anticipate the information needs of teammates and help them with their information needs proactively and effectively. In this paper, we extend CAST with a decision-making module. Through two sets of experiments in a simulated battlefield, we evaluate the effectiveness of the decision-theoretic proactive communication strategy in improving team performance, and the effectiveness of information fusion as an approach to alleviating the information overload problem faced by distributed decision makers.
---
paper_title: A COMPUTATIONAL MODEL OF MACHINE CONSCIOUSNESS

paper_content:

Despite many efforts, there are no computational models of consciousness that can be used to design conscious intelligent machines. This is mainly attributed to available definitions of consciousness being human centered, vague, and incomplete. Through a biological analysis of consciousness and concept of machine intelligence, we propose a physical definition of consciousness with the hope to model it in intelligent machines. We propose a computational model of consciousness driven by competing motivations, goals, and attention-switching. We propose a concept of mental saccades that is useful for explaining the attention-switching and focusing mechanism from computational perspective. Finally, we compare our model with other computational models of consciousness.
---
paper_title: CERA-CRANIUM: A Test Bed for Machine Consciousness Research

paper_content:

This paper describes a novel framework designed as a test bed for machine consciousness cognitive models (MCCM). This MCCM experimentation framework is based on a generalpurpose cognitive architecture that can be integrated in different environments and confronted with different problem domains. The definition of a generic cognitive control system for abstract agents is the root of the versatility of the presented framework. The proposed control system, which is inspired in the major cognitive theories of consciousness, provides mechanisms for both sensory data acquisition and motor action execution. Sensory and motor data is represented in the proposed architecture using different level workspaces where percepts and actions are generated thanks to the competition and collaboration of specialized processors. Additionally, this cognitive architecture provides the means to modulate perception and behavior; in other words, it offers an interface for a higher control layer to drive the way percepts and actions are generated and how they interact with each other. This mechanism permits the experimentation with virtually any high level cognitive model of consciousness. An illustrative application scenario, autonomous explorer robots, is also reviewed in this work.
---
paper_title: Implementing an efficient causal learning mechanism in a cognitive tutoring agent

paper_content:

An important research problem for developing complex cognitive agents is to provide them with human-like learning mechanisms. One important type of learning among episodic, emotional, and procedural learning is causal learning. In current cognitive agents, causal learning has up to now been implemented with techniques such as Bayesian networks that are not scalable for handling a large volume of data like a human does. In this paper, we address this problem of causal learning using a modified constraint based data mining algorithm that respects temporal ordering of the events. That is, from a huge amount of data, the algorithm filters the data to extract the most important information. We illustrate the application of this learning mechanism in a cognitive tutoring agent for the complex domain of teaching robotic arm manipulation.
---
paper_title: Tower of Hanoi: evidence for the cost of goal retrieval.

paper_content:

Past research on the Tower of Hanoi problem has provided clear evidence for the importance of goal-subgoal structures in problem solving. However, the nature of the traditional Tower of Hanoi problem makes it impossible to determine whether there is any special cost associated with storing or retrieving goals. A variation of the Tower of Hanoi problem is described that allows one to determine separately if there is an effect of how long a goal has to be retained on storage time or how long ago it was formed on retrieval time. This paradigm provides evidence for an effect of retention interval on retrieval time and not on storage time. An ACT-R (Adaptive Control of Thought-Rational; J. R. Anderson & C. Lebiere, 1998) simulation of these data is described, which treats goal memory as no different from other memories.
---
paper_title: What Does Your Actor Remember? Towards Characters with a Full Episodic Memory

paper_content:

A typical present-day virtual actor is able to store episodes in an ad hoc manner, which does not allow for reconstructing the actor's personal stories. This paper proposes a virtual RPG actor with a full episodic memory, which allows for this reconstruction. The paper presents the memory architecture, overviews the prototype implementation, presents a benchmark for the efficiency of the memory measurement, and details the conducted tests.
---
paper_title: Decision-Making in an Embedded Reasoning System

paper_content:

The development of reasoning systems that can reason and plan in a continuously changing environment is emerging as an important area of research in Artificial Intelligence. This paper describes some of the features of a Procedural Reasoning System (PRS) that enables it to operate effectively in such environments. The basic system design is first described and it is shown how this architecture supports both goal-directed reasoning and the ability to react rapidly to unanticipated changes in the environment. The decision-making capabilities of the system are then discussed and it is indicated how the system integrates these components in a manner that takes account of the bounds on both resources and knowledge that typify most real-time operations. The system has been applied to handling malfunctions on the space shuttle, threat assessment, and the control of an autonomous robot.
---
paper_title: Developing a Multi-Tasking Cognitive Agent Using the COGNET/iGEN Integrative Architecture

paper_content:

Abstract : This report describes CHI Systems model development effort as part of the initial model comparison process of the Agent-Based Modeling and Behavioral Representation (AMBR) program being conducted by the Air Force Research Laboratory. CHI Systems developed its model of human multi-tasking behavior using the COGNET integrative executable cognitive architecture and iGEN model development environment. The example problem environment in which all AME models in this comparison cycle were executed is a simplified Air Traffic Control (ATC) environment, developed by an independent moderator contractor, BBN Technologies. The moderator contractor also collected human performance data from live subjects as a point of comparison for model performance.
---
paper_title: Incremental Object Perception in an Attention-Driven Cognitive Architecture.

paper_content:

With few exceptions, architectural approaches to modeling cognition have historically emphasized what happens in the mind following the transduction of environmental signals into percepts. To our knowledge, none of these architectures implements a sophisticated, general theory of human attention. In this paper we summarize progress to date on a new cognitive architecture called ARCADIA that gives a central role to attention in both perception and cognition. First, we give an overview of the architecture, comparing it to other approaches when appropriate. Second, we present a model of incremental object construction and property binding in ARCADIA using the well known change blindness phenomena to illustrate the time course of object perception and its dependence on attention. Finally, we discuss near-term challenges and future plans.
---
paper_title: Simulating visual qualia in the CERA-CRANIUM cognitive architecture.

paper_content:

The concept of qualia poses a central problem in the framework of consciousness studies. Despite it being a controversial issue even in the study of human consciousness, we argue that qualia can be complementarily studied using artificial cognitive architectures. In this work we address the problem of defining qualia in the domain of artificial systems, providing a model of "artificial qualia". Furthermore, we partially apply the proposed model to the generation of visual qualia using the cognitive architecture CERA-CRANIUM, which is modeled after the global workspace theory of consciousness. It is our aim to define, characterize and identify artificial qualia as direct products of a simulated conscious perception process. Simple forms of the apparent motion effect are used as the basis for a preliminary experimental setting focused on the simulation and analysis of synthetic visual experience. In contrast with the study of biological brains, the inspection of the dynamics and transient inner states of the artificial cognitive architecture can be performed effectively, thus enabling the detailed analysis of covert and overt percepts generated by the system when it is confronted with specific visual stimuli. The observed states in the artificial cognitive architecture during the simulation of apparent motion effects are used to discuss the existence of possible analogous mechanisms in human cognition processes.
---
paper_title: Learning Recursive Control Programs from Problem Solving

paper_content:

In this paper, we propose a new representation for physical control -- teleoreactive logic programs -- along with an interpreter that uses them to achieve goals. In addition, we present a new learning method that acquires recursive forms of these structures from traces of successful problem solving. We report experiments in three different domains that demonstrate the generality of this approach. In closing, we review related work on learning complex skills and discuss directions for future research on this topic.
---
paper_title: Self-Programming: Operationalizing Autonomy

paper_content:

Lacking an operational definition of autonomy has considerably weakened the concept's impact in systems engineering. Most current “autonomous” systems are built to operate in conditions more or less fully described a priori, which is insufficient for achieving highly autonomous systems that adapt efficiently to unforeseen situations. In an effort to clarify the nature of autonomy we propose an operational definition of autonomy: a self-programming process. We introduce Ikon Flux, a proto-architecture for self-programming systems and we describe how it meets key requirements for the construction of such systems. Structural Autonomy as Self-Programming We aim at the construction of machines able to adapt to unforeseen situations in open-ended environments. Adaptation is used here in a strong sense as the ability of a machine not only to maintain but also to improve its utility function and so, in partially specified conditions with limited resources (including time) and knowledge. As a case in point, today’s Mars rovers would simply ignore the presence of an alien character waving its tentacles in front of the cameras: observers on Earth would probably see and identify it, but the rover itself would simply not be aware of this extraordinary situation and engineers would have to upload software upgrades to change its mission and plans. In sharp contrast to such engineering, we expect adaptation to be performed automatically, i.e. with no further intervention by programmers after a machine enters service. Our adaptable rover would be fitted with software aiming at discovering facts in a general fashion, that is, not limited to ultra-specific mission schemes. This software would ideally be able to generate new missions and related skills according to the context, within the limitations of its resources hardware, energy, time horizon, etc.
---
paper_title: The society of mind

paper_content:

Marvin Minsky--one of the fathers of computer science and cofounder of the Artificial Intelligence Laboratory at MIT--gives a revolutionary answer to the age-old question: "how does the mind work?"
---
paper_title: Interdisciplinary Development and Evaluation of Cognitive Architectures Exemplified with the SiMA Approach

paper_content:

In this paper we show how simple simulation scenarios can be used to develop and test foundational functionalities of cognitive architectures, exemplified with the SiMA architecture. We present an interdisciplinary methodology that considers the challenges in capturing and evaluating basic functionalities of the human mind. In this regard, we structure and concretize assumptions from various disciplines and show how we evaluate their plausibility in a consistent model, using parametrized simulations.
---
paper_title: Psychologically realistic cognitive agents: taking human cognition seriously

paper_content:

Cognitive architectures may serve as a good basis for building mind/brain-inspired, psychologically realistic cognitive agents for various applications that require or prefer human-like behaviour and performance. This article explores a well-established cognitive architecture CLARION and shows how its behaviour and performance capture human psychology at a detailed level. The model captures many psychological quasi-laws concerning categorisation, induction, uncertain reasoning, decision-making, and so on, which indicates human-like characteristics beyond what other models have been shown to be capable of. Thus, CLARION constitutes an advance in developing more psychologically realistic cognitive agents.
---
paper_title: A Domain-Specific Software Architecture for Adaptive Intelligent Systems

paper_content:

A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We present a domain-specific software architecture (DSSA) that we have developed for a large application domain of adaptive intelligent systems (AISs). The DSSA provides: (a) an AIS reference architecture designed to meet the functional requirements shared by applications in this domain, (b) principles for decomposing expertise into highly reusable components, and (c) an application configuration method for selecting relevant components from a library and automatically configuring instances of those components in an instance of the architecture. The AIS reference architecture incorporates features of layered, pipe and filter, and blackboard style architectures. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture. >
---
paper_title: Attention in the ASMO Cognitive Architecture

paper_content:

The ASMO Cognitive Architecture has been developed to support key capabilities: attention, awareness and self-modification. In this paper we describe the underlying attention model in ASMO. The ASMO Cognitive Architecture is inspired by a biological attention theory, and offers a mechanism for directing and creating behaviours, beliefs, anticipation, discovery, expectations and changes in a complex system. Thus, our attention based architecture provides an elegant solution to the problem of behaviour development and behaviour selection particularly when the behaviours are mutually incompatible.
---
paper_title: CONSCIOUSNESS IS COMPUTATIONAL: THE LIDA MODEL OF GLOBAL WORKSPACE THEORY

paper_content:

We argue that the functions of consciousness are implemented in a bio-computational manner. That is to say, the conscious as well as the non-conscious aspects of human thinking, planning, and perception are produced by adaptive, biological algorithms. We propose that machine consciousness may be produced by similar adaptive algorithms running on the machine. Global Workspace Theory is currently the most empirically supported and widely discussed theory of consciousness. It provides a high-level description of such algorithms, based on a large body of psychological and brain evidence. LIDA provides an explicit implementation of much of GWT, which can be shown to perform human-like tasks, such as the interactive assignment of naval jobs to sailors. Here we provide brief descriptions of both GWT and LIDA in relation to the scientific evidence bearing on consciousness in the brain. A companion article explores how this approach could lead to machine consciousness.1 We also discuss the important distinction between volition and consciously mediated action selection, and describe an operational definition of consciousness via verifiable reportability. These are issues that may well bear on the possibility of machine consciousness.
---
paper_title: LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning

paper_content:

We describe a cognitive architecture learning intelligent distribution agent (LIDA) that affords attention, action selection and human-like learning intended for use in controlling cognitive agents that replicate human experiments as well as performing real-world tasks. LIDA combines sophisticated action selection, motivation via emotions, a centrally important attention mechanism, and multimodal instructionalist and selectionist learning. Empirically grounded in cognitive science and cognitive neuroscience, the LIDA architecture employs a variety of modules and processes, each with its own effective representations and algorithms. LIDA has much to say about motivation, emotion, attention, and autonomous learning in cognitive agents. In this paper, we summarize the LIDA model together with its resulting agent architecture, describe its computational implementation, and discuss results of simulations that replicate known experimental data. We also discuss some of LIDA's conceptual modules, propose nonlinear dynamics as a bridge between LIDA's modules and processes and the underlying neuroscience, and point out some of the differences between LIDA and other cognitive architectures. Finally, we discuss how LIDA addresses some of the open issues in cognitive architecture research.
---
paper_title: Object Learning Through Active Exploration

paper_content:

This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts.
---
paper_title: Meta-case-Based Reasoning: Using Functional Models to Adapt Case-Based Agents

paper_content:

It is useful for an intelligent software agent to be able to adapt to new demands from an environment. Such adaptation can be viewed as a redesign problem; an agent has some original functionality but the environment demands an agent with a slightly different functionality, so the agent redesigns itself. It is possible to take a case-based approach to this redesign task. Furthermore, one class of agents which can be amenable to redesign of this sort is case-based reasoners. These facts suggest the notion of "meta-case-based reasoning," i.e., the application of case-based redesign techniques to the problem of adapting a case-based reasoning process. Of course, meta-case-based reasoning is a very broad topic. In this paper we focus on a more specific issue within meta-case-based reasoning: balancing the use of relatively efficient but knowledge intensive symbolic techniques with relatively flexible but computationally costly numerical techniques. In particular, we propose a mechanism whereby qualitative functional models are used to efficiently propose a set of design alternatives to specific elements within a meta-case and then reinforcement learning is used to select among these alternatives. We describe an experiment in which this mechanism is applied to a case-based disassembly agent. The results of this experiment show that the combination of model-based adaptation and reinforcement learning can address meta-case-based reasoning problems which are not effectively addressed by either approach in isolation.
---
paper_title: An Experiment in Agent Teaching by Subject Matter Experts

paper_content:

This paper presents a successful knowledge-acquisition experiment in which subject matter experts who did not have any prior knowledge-engineering experience succeeded in teaching the Disciple- COA agent how to critique courses of action, a challenge problem addressed by the DARPA's High-Performance Knowledge Bases program. We first present the COA challenge problem and the architecture of Disciple- COA, a learning agent shell from the Disciple family. Then we present the knowledge acquisition experiment, detailing both the expert?Disciple interactions, and the automatic knowledge-base development processes that take place as a result of these interactions. The results of this experiment provide strong evidence that the Disciple approach is a viable solution to the knowledge acquisition bottleneck.
---
paper_title: Integrating Planning and Reacting in a Heterogeneous Asynchronous Architecture for Controlling Real-World Mobile Robots

paper_content:

This paper presents a heterogeneous, asynchronous architecture for controlling autonomous mobile robots which is capable of controlling a robot performing multiple tasks in real time in noisy, unpredictable environments. The architecture produces behavior which is reliable, task-directed (and taskable), and reactive to contingencies. Experiments on real and simulated realworld robots are described. The architecture smoothly integrates planning and reacting by performing these two functions asynchronously using heterogeneous architectural elements, and using the results of planning to guide the robot's actions but not to control them directly. The architecture can thus be viewed as a concrete implementation of Agre and Chapman's plans-ascommunications theory. The central result of this work is to show that completely unmodified classical AI programming methodologies using centralized world models can be usefully incorporated into real-world embedded reactive systems.
---
paper_title: Action primitives for bionics inspired action planning system: Abstraction layers for action planning based on psychoanalytical concepts

paper_content:

Action planning is important for autonomous systems since the capacity to find a solution for a particular problem determines the degree of system autonomy. When designing an action planning system for real world domains, hierarchical systems offer a promising way to split complex planning goals in clearly-arranged sub-goals. Although dividing problems into chunks depicts a performance adequate approach for planning, considerations how plan fragments are saved in memory must be made. Memory demands grow with the abstraction level of the planning system. A decision unit for autonomous agents forms the foundation for this work. An action planning system that was designed to fit the constraints of a bionically inspired information representation module is presented. Finally, the system is integrated into autonomous agents and evaluated within an artificial life simulation.
---
paper_title: prodigy/analogy: Analogical Reasoning in General Problem Solving

paper_content:

This paper describes the integration of analogical reasoning into general problem solving as a method of learning at the strategy level to solve problems more effectively. The method based on derivational analogy has been fully implemented in prodigy/analogy and proven empirically to be amenable to scaling up both in terms of domain and problem complexity. prodigy/analogy addresses a set of challenging problems, namely: how to accumulate episodic problem solving experience, cases, how to define and decide when two problem solving situations are similar, how to organize a large library of planning cases so that it may be efficiently retrieved, and finally how to successfully transfer chains of problem solving decisions from past experience to new problem solving situations when only a partial match exists among corresponding problems. The paper discusses the generation and replay of the problem solving cases and we illustrate the algorithms with examples. We present briefly the library organization and the retrieval strategy. We relate this work with other alternative strategy learning methods, and also with plan reuse. prodigy/analogy casts the strategy-level learning process for the first time as the automation of the complete cycle of constructing, storing, retrieving, and flexibly reusing problem solving experience. We demonstrate the effectiveness of the analogical replay strategy by providing empirical results on the performance of prodigy/analogy, accumulating and reusing a large case library in a complex problem solving domain. The integrated learning system reduces the problem solving search effort incrementally as more episodic experience is compiled into the library of accumulated learned knowledge.
---
paper_title: ARDIS: knowledge‐based architecture for visual system configuration in dynamic surface inspection

paper_content:

: This work presents an approach to dynamic surface inspection in laminated materials based on the configuration of a visual system to obtain good quality control of the manufacturing surface. It aims to overcome some of the limitations of the single-use visual inspection systems by integrating and differentiating knowledge. The configuration task for surface inspection is solved as a Configuration-Design task according with the CommonKADS methodology. This task is analysed at the knowledge level and is decomposed into simple subtasks to reach the inference level. The generic domain knowledge involved in the configuration and revision tasks is differentiated in six types: initial, global, environment, real-time, image quality and computer vision techniques. The main goal of the proposed architecture is to reuse this partitioned knowledge for the configuration and revision of different visual surface inspection systems. The architecture has been implemented in a platform that distinguishes several operation modes: visualization of specific surface inspection results, system configuration and knowledge injection. Results obtained in the configuration and revision tasks for two surface inspection systems, stainless-steel and wood, are presented.
---
paper_title: Procedural knowledge

paper_content:

Much of commonsense knowledge about the real world is in the form of procedures or sequences of actions for achieving particular goals. In this paper, a formalism is presented for representing such knowledge using the notion of process. A declarative semantics for the representation is given, which allows a user to state facts about the effects of doing things in the problem domain of interest. An operational semantics is also provided, which shows how this knowledge can be used to achieve particular goals or to form intentions regarding their achievement. Given both semantics, our formalism additionally serves as an executable specification language suitable for constructing complex systems. A system based on this formalism is described, and examples involving control of an autonomous robot and fault diagnosis for NASA's space shuttle are provided.
---
paper_title: What Does Your Actor Remember? Towards Characters with a Full Episodic Memory

paper_content:

A typical present-day virtual actor is able to store episodes in an ad hoc manner, which does not allow for reconstructing the actor's personal stories. This paper proposes a virtual RPG actor with a full episodic memory, which allows for this reconstruction. The paper presents the memory architecture, overviews the prototype implementation, presents a benchmark for the efficiency of the memory measurement, and details the conducted tests.
---
paper_title: Modeling visual search of displays of many objects: The role of differential acuity and fixation memory

paper_content:

This paper describes a classic data set on visual search of 100-object displays that differ in size, shape, and color and presents a cognitive architecture model based on the active vision concept that accounts for the effects using differential visual acuity and fixation memory provided by a persistent visual store. The results provide an approximate upper bound on the duration of fixation memory, and some approximate acuity functions for modeling visual search.
---
paper_title: Pre-attentive and Attentive Vision Module

paper_content:

This paper introduces a new vision module, called PAAV, developed for the cognitive architecture ACT-R. Unlike ACT-R's default vision module that was originally developed for top-down perception only, PAAV was designed to model a wide range of tasks, such as visual search and scene viewing, where pre-attentive bottom-up processes are essential for the validity of a model. PAAV builds on attentive components of the default vision module and incorporates greater support for modeling pre-attentive components of human vision. The module design incorporates the best practices from existing models of vision. The validity of the module was tested on four different tasks.
---
paper_title: A LIDA cognitive model tutorial

paper_content:

Abstract Over a decade in the making and described in some seventy-five published papers, the LIDA cognitive model is comprehensive, complex, and hard to “wrap one’s head around”. Here we offer, in tutorial fashion, a current, relatively complete and somewhat detailed, description of the conceptual LIDA model, with pointers to more complete accounts of individual processes in the literature. These descriptions also include some features of the workings of the LIDA model that have not been published previously. The tutorial begins with several short sections designed to ease the reader into the LIDA model. These are followed by an account of the conceptual commitments of the LIDA model. We also include a brief introduction to the LIDA computational model via the LIDA Framework, with pointers to its own tutorial. This is followed by sketches of several of the LIDA based agents developed with the help of the Framework. The tutorial ends with a section on current research activity, which includes a table showing which aspects of the LIDA conceptual model have currently been implemented computationally.
---
paper_title: The Novamente artificial intelligence engine

paper_content:

The Novamente AI Engine, a novel AI software system, is briefly reviewed. Novamente is an integrative artificial general intelligence design, which integrates aspects of many prior AI projects and paradigms, including symbolic, probabilistic, evolutionary programming and reinforcement learning approaches; but its overall architecture is unique, drawing on system-theoretic ideas regarding complex mental dynamics and associated emergent patterns. The chapter reviews both the conceptual models of mind and intelligence which inspired the system design, and the concrete architecture of Novamente as a software system.
---
paper_title: Modelling Memory and Learning Consistently from Psychology to Physiology

paper_content:

Natural selection pressures have resulted in the physical resources of the brain being organized into modules that perform different general types of information processes. Each module is made up of submodules performing different information processes of the general type, and each submodule is made up of yet more detailed modules. At the highest level, modules correspond with major anatomical structures like the cortex, hippocampus, basal ganglia, cerebellum etc. In the cortex, for example, the more detailed modules include areas, columns, neurons, and a series of neuron substructures down to molecules. Any one memory or learning phenomenon requires many information processes performed by many different anatomical structures. However, the modular structure makes it possible to describe a memory phenomenon at a high (psychological) level in terms of the information processes performed by the major anatomical structures. The same phenomenon can be described at each level in a hierarchy of more detailed descriptions, in terms of the information processes performed by anatomical substructures. At higher levels, descriptions are approximate but can be mapped to more detailed, more precise descriptions as required down to neuron levels and below. The total information content of a high level description is small enough that it can be fully understood. Small parts of a high level phenomenon, when described at a more detailed level, also have a small enough information content to be understood. The information processes and resultant hierarchy of descriptions therefore make it possible to understand cognitive phenomena like episodic, semantic or working memory in terms of neuron processes via a series of intermediate levels of description.
---
paper_title: Simulating visual qualia in the CERA-CRANIUM cognitive architecture.

paper_content:

The concept of qualia poses a central problem in the framework of consciousness studies. Despite it being a controversial issue even in the study of human consciousness, we argue that qualia can be complementarily studied using artificial cognitive architectures. In this work we address the problem of defining qualia in the domain of artificial systems, providing a model of "artificial qualia". Furthermore, we partially apply the proposed model to the generation of visual qualia using the cognitive architecture CERA-CRANIUM, which is modeled after the global workspace theory of consciousness. It is our aim to define, characterize and identify artificial qualia as direct products of a simulated conscious perception process. Simple forms of the apparent motion effect are used as the basis for a preliminary experimental setting focused on the simulation and analysis of synthetic visual experience. In contrast with the study of biological brains, the inspection of the dynamics and transient inner states of the artificial cognitive architecture can be performed effectively, thus enabling the detailed analysis of covert and overt percepts generated by the system when it is confronted with specific visual stimuli. The observed states in the artificial cognitive architecture during the simulation of apparent motion effects are used to discuss the existence of possible analogous mechanisms in human cognition processes.
---
paper_title: Modeling visual search of displays of many objects: The role of differential acuity and fixation memory

paper_content:

This paper describes a classic data set on visual search of 100-object displays that differ in size, shape, and color and presents a cognitive architecture model based on the active vision concept that accounts for the effects using differential visual acuity and fixation memory provided by a persistent visual store. The results provide an approximate upper bound on the duration of fixation memory, and some approximate acuity functions for modeling visual search.
---
paper_title: A cognitive system for adaptive decision making

paper_content:

This paper seeks to investigate how the existing knowledge in a cognitive system can be used to help fill-in an incomplete situation picture. This is motivated by the human brain's innate ability to use new incoming information together with stored knowledge to fill in gaps of the whole picture in the mind. The research focus of this paper is on the inference or retrieval of information that is relevant to the current circumstances, but not directly obtainable from actual observations. The objective is to derive methods to help uncover as complete a situation picture as possible with high degree of confidence. Two computational approaches to enhance situation awareness for adaptive decision making given partial information are proposed. The proposed approaches enhanced an existing system Dynamic Bayesian Reasoning and Advanced Intelligent Network (D'Brain) which is a cognitive based dynamic reasoning system that uses Bayesian network as the underlying knowledge representation. Simulations are carried out to test the feasibility of the proposed approaches. The results show that they are promising approaches to fulfil the objectives in enhancing situation awareness for adaptive decision making.
---
paper_title: A Foundational Architecture for Artificial General Intelligence

paper_content:

Implementing and fleshing out a number of psychological and neuroscience theories of cognition, the LIDA conceptual model aims at being a cognitive “theory of everything.” With modules or processes for perception, working memory, episodic memories, “consciousness,” procedural memory, action selection, perceptual learning, episodic learning, deliberation, volition, and non-routine problem solving, the LIDA model is ideally suited to provide a working ontology that would allow for the discussion, design, and comparison of AGI systems. The LIDA architecture is based on the LIDA cognitive cycle, a sort of “cognitive atom.” The more elementary cognitive modules and processes play a role in each cognitive cycle. Higher-level processes are performed over multiple cycles. In addition to giving a quick overview of the LIDA conceptual model, and its underlying computational technology, we argue for the LIDA architecture's role as a foundational architecture for an AGI. Finally, lessons For AGI researchers drawn from the model and its architecture are discussed.
---
paper_title: The GLAIR Cognitive Architecture

paper_content:

GLAIR (Grounded Layered Architecture with Integrated Reasoning) is a multi-layered cognitive architecture for embodied agents operating in real,virtual, or simulated environments containing other agents. The highest layer of the GLAIR Architecture, the Knowledge Layer (KL), contains the beliefs of the agent, and is the layer in which conscious reasoning, planning, and act selection is performed. The lowest layer of the GLAIR Architecture, the Sensori-Actuator Layer (SAL), contains the controllers of the sensors and effectors of the hardware or software robot. Between the KL and the SAL is the Perceptuo-Motor Layer (PML), which grounds the KL symbols in perceptual structures and subconscious actions, contains various registers for providing the agent's sense of situatedness in the environment, and handles translation and communication between the KL and the SAL. The motivation for the development of GLAIR has been "Computational Philosophy", the computational understanding and implementation of human-level intelligent behavior without necessarily being bound by the actual implementation of the human mind. Nevertheless, the approach has been inspired by human psychology and biology.
---
paper_title: The Explorer System

paper_content:

In the Explorer scenario we deal with the problems of modeling space, acting in this space and reasoning about it. Comparing with the motivating example in Section 1.3 the Explorer scenario focuses around issues related to the second bullet in the example. The setting is that of Fido moving around in an initially unknown (Fido was just unpacked from the box), large scale (it is a whole house so the sensors do not perceive all there is from one spot), environment inhabited by humans (the owners of Fido and possible visitors). These humans can be both users and bystanders. The version of Fido that we work with in the Explorer scenario can move around but interaction with the environment is limited to non-physical interaction such as “talking”. The main sensors of the system are a laser scanner and a camera mounted on a pan-tilt enabling Fido to look around by turning its “neck”. Figure 10.1 shows a typical situation from the Explorer scenario.
---
paper_title: The Effects of Bounding Rationality on the Performance and Learning of CHREST Agents in Tileworld

paper_content:

Learning in complex and complicated domains is fundamental to performing suitable and timely actions within them. The ability of chess masters to learn and recall huge numbers of board configurations to produce near-optimal actions provides evidence that chunking mechanisms are likely to underpin human learning. Cognitive theories based on chunking argue in favour for the notion of bounded rationality since relatively small chunks of information are learnt in comparison to the total information present in the environment. CHREST, a computational architecture that implements chunking theory, has previously been used to investigate learning in deterministic environments such as chess, where future states are solely dependent upon the actions of agents. In this paper, the CHREST architecture is implemented in agents situated in “Tileworld”, a stochastic environment whose future state depends on both the actions of agents and factors intrinsic to the environment which agents have no control over. The effects of bounding agents’ visual input on learning and performance in various scenarios where the complexity of Tileworld is altered is analysed using computer simulations. Our results show that interactions between independent variables are complex and have important implications for agents situated in stochastic environments where a balance must be struck between learning and performance.
---
paper_title: CogSketch: sketch understanding for cognitive science research and for education

paper_content:

Sketching is a powerful means of working out and communicating ideas. Sketch understanding involves a combination of visual, spatial, and conceptual knowledge and reasoning, which makes it both challenging to model and potentially illuminating for cognitive science. This paper describes CogSketch, an ongoing effort of the NSF-funded Spatial Intelligence and Learning Center, which is being developed both as a research instrument for cognitive science and as a platform for sketchbased educational software. We describe the idea of open-domain sketch understanding, the scientific hypotheses underlying CogSketch, and provide an overview of the models it employs, illustrated by simulation studies and ongoing experiments in creating sketch-based educational software.
---
paper_title: Modeling Cultural and Personality Biases in Decision-Making

paper_content:

Cultural, personality and affective biases in decision-making are well documented. This chapter describes a method for modeling multiple decision biases resulting from cultural effects, personality traits and affective states, within the context of a symbolic cognitive-affective agent architecture: the MAMID methodology and architecture. The approach emphasizes the role of affect in decision-biases, as the primary mediating factor of a wide range of biasing effects, and lends itself to exploring alternative mechanisms mediating a wide range of decision biases. The approach provides a uniform framework for modeling both content and processing biases, in terms of parameter vectors that control processing within the architecture modules. The effects of these biases are encoded in specific values of architecture parameters, which then influence the processing of the distinct architecture modules, including the architecture topology itself. The associated simulation environment enables the modeling of a wide variety of decision-makers, in terms of distinct personality and cultural profiles, and consequent affective profile and affect-induced decision-biases. The key contribution, and distinguishing feature, of the MAMID modeling approach is the parameter space it provides for representing the interacting effects of multiple types and sources of biases, and the potential of this approach for modeling the fundamental mechanisms that mediate decision-biases.
---
paper_title: CELTS: A Cognitive Tutoring Agent with Human-Like Learning Capabilities and Emotions

paper_content:

To provide a rich learning experience, an intelligent tutoring agent should be able to take into account past and present events, and to learn from its interactions with learners to continuously improve the assistance it provides. Until now, the learning capabilities of tutoring agents in educational technologies have been generally very limited. In this chapter, we address this challenge with Conscious-Emotional-Learning Tutoring System (CELTS), a cognitive tutoring agent, whose architecture is inspired by the latest neuroscientific theories. CELTS implements human-like learning capabilities such as episodic, emotional, procedural and causal learning. The agent is used in a simulation-based tutoring system for learning the complex task of operating the Canadarm2 robotic arm on the International Space Station (ISS). Experimental evaluations showed that the agent’s learning capabilities considerably enhance its adaptation to learners during interactions and consequently improves learners’ performance.
---
paper_title: DIARC: A Testbed for Natural Human-Robot Interaction

paper_content:

DIARC, a distributed integrated affect, reflection, cognition architecture for robots, provides many features that are critical to successful natural human-robot interaction. As such, DIARC is an ideal platform for experimentation in HRI. In this paper we describe the architecture and and its implementation in ADE, paying particular attention to its interaction capabilities and features that allow robust operation. These features are evaluated in the context of the 2006 AAAI Robot Competition.
---
paper_title: SHRUTI: A Neurally Motivated Architecture for Rapid, Scalable Inference

paper_content:

A can sorting, counting and flattening machine flattens empty cans by folding the can body over the back side of the ring or top end of the can so that both the brand name of the can on the can body and all identifying indicia on the ring end of the can are easily readable after the can has been completely flattened in a one step operation by a single machine operator. The machine employs a series of resettable counters which automatically count the total number of each brand of cans, the total number of cans returned per individual customer and the total number of cans reprocessed by the machine in a given time period.
---
paper_title: Integrating theories of motor sequencing in the SAL hybrid architecture

paper_content:

Abstract The Synthesis of ACT-R and Leabra (SAL) hybrid cognitive architecture is the integration of two theories of cognitive functioning, each itself a highly integrative theory of cognition, ACT-R being predominantly a symbolic production-rule based architecture and Leabra a neural modeling architecture. The combination of the two architectures allows for richer dynamics that take advantage of neural and symbolic aspects of cognition and provides mutual constraints that promote convergence towards models that are both neurophysiologically and psychologically valid. We present a hybrid model that makes use of multi-level and multi-system integration to allow an instructed assembly task to be carried out in way that is robust to noise and error. Specifically, the model shows how higher-level error recovery routines can interface with lower-level sensory, motor, and error detection processes and result in a robustness to noise and noise-induced errors. The benefits of an integrated cognitive architecture, in which multiple systems and processes operating at multiple levels are recruited to provide a way around the limitations of simpler systems composed of isolated modules that do not allow information to be propagated as easily, provide motivation for the adoption of a generally integrated approach to cognitive systems.
---
paper_title: A Functional Model of Sensemaking in a Neurocognitive Architecture

paper_content:

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesisupdating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment.
---
paper_title: Piece of mind: Long-term memory structure in ACT-R and CHREST

paper_content:

Martin Lloyd-Kelly, Fernand Gobet, Peter Lane, ‘Piece of mind: Long-term memory structure in ACT-R and CHREST’, in D.C. Noelle, Rick Dale, Anne Warlaumont, Jeff Yoshimi, Teenie Matlock, Carolyn Jennings, Paul P. Maglio, eds., Proceedings of the 37th Annual Meeting of the Cognitive Science Society (CA, USA: Cognitive Science Society, 2015), ISBN 978-1-5108-0955-0.
---
paper_title: Casimir: An Architecture for Mental Spatial Knowledge Processing

paper_content:

Mental spatial knowledge processing often uses spatio-analogical or quasipictorial representation structures such as spatial mental models or mental images. The cognitive architecture Casimir is designed to provide a framework for computationally modeling human spatial knowledge processing relying on these kinds of representation formats. In this article, we present an overview of Casimir and its components. We briefly describe the long-term memory component and the interaction with external diagrammatic representations. Particular emphasis is placed on Casimir’s working memory and control mechanisms. Regarding working memory, we describe the conceptual foundations and the processing mechanisms employed in mental spatial reasoning. With respect to control, we explain how it is realized as a distributed, emergent facility within Casimir.
---
paper_title: Mixed-initiative Control for Teaching and Learning in Disciple

paper_content:

Disciple is an approach to agent development by subject matter experts where an expert teaches a Disciple agent his or her problem solving expertise in a way that resembles how a person teaches another person. This paper presents an overview of the teaching and learning process during which the expert helps Disciple to learn and Disciple helps the expert to teach it, emphasizing the mixed-initiative control of the component agents of Disciple, particularly the Modeling agent, the Rule Learning agent and the Exception-based Ontology Learning agent. It discusses current implementations, evaluation results, lessons learned and future developments.
---
paper_title: Determining Information Technology Project Status using Recognition-primed Decision-making-enabled Collaborative Agents for Simulating Teamwork (R-CAST)

paper_content:

In the project management environment, it is critical to know how the project is performing from the viewpoint of scope, schedule, cost, and other constraints. One source reports that 70% of IT-related projects do not meet their objective (Lewis, 2008). This paper examines the use of the R-CAST architecture to develop a collaborative agent system that aids in recommending the status of a project using color indicators (red, yellow, green) that are based on the progress and standing of the project-related constraints.
---
paper_title: Issues in Temporal and Causal Inference

paper_content:

This paper discusses several key issues in temporal and causal inference in the context of AGI. The main conclusions are: 1 the representation of temporal information should take multiple forms; 2 classical conditioning can be carried out as temporal inference; 3 causal inference can be realized without a predefined causal relation.
---
paper_title: The cognitive architecture of a robotic salesman

paper_content:

This paper describes a robotics cognitive architecture for social robots named CORTEX. This architecture integrates different levels of abstraction (from basic geometry to high-level predicates) into a unique Deep Space Representation (DSR) that different agents interface. These agents update the contents of the DSR with new data from the outer world, and execute, plan and design behaviours. The design of CORTEX as an unified deep representation allows to fit both the subsymbolic processing and flexibility requirements of robot control. In this paper a first implementation of CORTEX has been integrated into Gualzru, a robotic salesman, and tested in real scenarios. Results show that this cognitive architecture allows this robot to adequately execute its use case, and that it has a promising adaptability to achieve new tasks and be used in new scenarios.
---
paper_title: Extending Cognitive Architecture with Episodic Memory

paper_content:

In this paper, we explore the hypothesis that episodic memory is a critical component for cognitive architectures that support general intelligence. Episodic memory overlaps with case-based reasoning (CBR) and can be seen as a task-independent, architectural approach to CBR. We define the design space for episodic memory systems and the criteria any implementation must meet to be useful in a cognitive architecture. We present an implementation and demonstrate how episodic memory, combined with other components of a cognitive architecture, supports a wealth of cognitive capabilities that are difficult to attain without it.
---
paper_title: Memory systems within a cognitive architecture

paper_content:

Abstract This article addresses the division of memory systems in relation to an overall cognitive architecture. As understanding the architecture is essential to understanding the mind, developing computational cognitive architectures is an important enterprise in computational psychology (computational cognitive modeling). The article proposes a set of hypotheses concerning memory systems from the standpoint of a cognitive architecture, in particular, the four-way division of memory (including explicit and implicit procedural memory and explicit and implicit declarative memory). It then discusses in detail how these hypotheses may be validated through examining qualitatively the literature on memory. A quick review follows of computational simulations of a variety of quantitative data (which are not limited to narrowly conceived “memory tasks”). Results of accounting for both qualitative and quantitative data point to the promise of this approach.
---
paper_title: Building Internal Scene Representation in Cognitive Agents

paper_content:

Navigating in realistic environments requires continuous observation of a robot’s surroundings, and creating internal representation of a perceived scene. This incorporates a sequence of cognitive processes, including attention focus, recognition of objects, and building internal scene representation. The paper describes selected elements of a cognitive system, which implement mechanisms of scene observation based on visual saccades, followed by creating the scene representation. Such internal representation is a foundation for scene comparison, based on a distance matrix. This, in turn, allows for recognizing known places, changes in the environment, or structure of complex objects.
---
paper_title: Sentence generation for artificial brains: A glocal similarity-matching approach

paper_content:

A novel approach to sentence generation - SegSim, Sentence Generation by Similarity Matching - is outlined, and is argued to possess a number of desirable properties making it plausible as a model of sentence generation in the human brain, and useful as a guide for creating sentence generation components within artificial brains. The crux of the approach is to do as much as possible via similarity matching against a large knowledge base of previously comprehended sentences, rather than via complex algorithmic operations. To get the most out of this sort of matching, a certain amount of relatively simple rule-based processing needs to be done in pre- and post-processing steps. However, complex algorithmic operations are required only for the generation of sentences representing complex or unfamiliar thoughts. This, it is suggested, is the sort of sentence generation approach that makes sense in a system that - like a real or artificial brain - combines the capability for effective local application of logical rules with the capability for massively parallel, scalable, inexpensive similarity matching.
---
paper_title: Integrating Planning and Reacting in a Heterogeneous Asynchronous Architecture for Controlling Real-World Mobile Robots

paper_content:

This paper presents a heterogeneous, asynchronous architecture for controlling autonomous mobile robots which is capable of controlling a robot performing multiple tasks in real time in noisy, unpredictable environments. The architecture produces behavior which is reliable, task-directed (and taskable), and reactive to contingencies. Experiments on real and simulated realworld robots are described. The architecture smoothly integrates planning and reacting by performing these two functions asynchronously using heterogeneous architectural elements, and using the results of planning to guide the robot's actions but not to control them directly. The architecture can thus be viewed as a concrete implementation of Agre and Chapman's plans-ascommunications theory. The central result of this work is to show that completely unmodified classical AI programming methodologies using centralized world models can be usefully incorporated into real-world embedded reactive systems.
---
paper_title: Brain-Based Devices for the Study of Nervous Systems and the Development of Intelligent Machines

paper_content:

The simultaneous study of brain function at all levels of organization is difficult to undertake with current experimental tools. Present day electrophysiology only allows the recording of at most hundreds of neurons while an animal is performing a behavioral task. Because of this limitation and the sheer complexity of the nervous system, computational modeling has become essential in developing theories of brain function. Accordingly, our group has constructed a series of brain-based devices (BBDs), that is, physical devices with simulated nervous systems that guide behavior, to serve as a heuristic for testing theories of brain function. Unlike animal models, BBDs permit analysis of activity at all levels of the nervous system as the device behaves in its environment. Although the principal focus of developing BBDs has been to test theories of brain function, this type of modeling may also provide a basis for robotic design and practical applications.
---
paper_title: MAX: a meta-reasoning architecture for “X”

paper_content:

This article summarizes the MAX system, a meta-reasoning architecture designed to integrate the reasoning, execution, and learning required of intelligent agents. MAX is based on the idea of specifying all the reasoning and learning capabilities of a system as explicit, declarative knowledge. The mechanisms that support this, as well as the benefits and drawbacks are outlined. An example of the integrated behavior implemented in MAX is given, along with comparisons to related architectures.
---
paper_title: Adaptive Execution in Complex Dynamic Worlds

paper_content:

A robot acting in the real world must use flexible plans because actions will sometimes fail to produce desired effects, and unexpected events will sometimes demand the robot shift its attention. A plan is usually construed as a list of primitive robot actions to be executed one after another but in a complex domain, a plan must be structured to cope effectively with the myriad unpredictable details it will encounter during execution. However, adding structure to a plan involves more than augmenting the primitive plan representation; it requires a complete model of interaction with the world called situation-driven execution. Situation-driven execution assumes that a plan consists of tasks with three major components: a satisfaction test, a window of activity, and a set of execution methods that are appropriate in different circumstances. Execution of such a plan proceeds by selecting an unsatisfied task and choosing a method to achieve it based on the current world state. A task may be executed as many times as necessary to keep it satisfied while it is active. ::: This thesis proposes a plan and task representation based on program-like reactive action packages, or RAPs. A plan consists of RAP-defined tasks and each RAP generates primitive robot actions at execution time by selecting its most appropriate method. Within such a system, execution monitoring becomes an intrinsic part of the execution algorithm, and the need for separate replanning on failure disappears. RAPs are more than just programs that run at execution time, however, they are also hierarchical building blocks for plan construction. The RAP representation is structured to make a task's expected behavior evident for use in planning as well as in execution. The RAP execution system described includes a sensor memory, representation language and interpreter. Examples and experiments demonstrate a wide range of adaptive system behavior.
---
paper_title: Model-Free Learning and Control in a Mobile Robot

paper_content:

A model-free, biologically-motivated learning and control algorithm called S-learning is described as implemented in an Surveyor SRV-1 mobile robot. S-learning demonstrated learning of robotic and environmental structure sufficient to allow it to achieve its goals (finding high- or low-contrast views in its environment). No modeling information about the task or calibration information about the robot’s actuators and sensors were used in S-learning’s planning. The ability of S-learning to make movement plans was completely dependent on experience it gained as it explored. Initially it had no experience and was forced to wander randomly. With increasing exposure to the task, S-learning achieved its goals with more nearly optimal paths. The fact that this approach is model-free implies that it may be applied to many other systems, perhaps even to systems of much greater complexity.
---
paper_title: Mind model for multimodal communicative creatures and humanoids

paper_content:

This paper presents a computational model of real - time task - oriented dialog skills . The model , termed Ymir, bridges multimodal perception and multimodal action and supports the creation of autonomous computer characters that afford full - duplex , real - time face - to - face interaction with a human . Ymir has been prototyped in software , and a humanoid created , called Gandalf, capable of fluid multimodal dialog . Ymir demonstrates several new ideas in the creation of communicative computer agents , including perceptual integration of multimodal events, distributed planning and decision making, an explicit handling of real time, and perceptuo-motor system layered and motor control with human characteristics. This paper describes the model's architecture and explains its main elements . Examples of implementation and performance are given , and the architecture's limitations and possibilities are discussed .
---
paper_title: The iCub Cognitive Architecture

paper_content:

In this chapter, we examine how the roadmap guidelines set out in Chap. 6 have influenced the design of a cognitive architecture for the iCub humanoid robot. We begin with a overview of the iCub and we discuss briefly the iCub mechatronics and software infrastructure. We then describe the iCub cognitive architecture, focussing the selection of a minimal set of phylogenetic capabilities derived from the seven groups of roadmap guidelines. Since the iCub cognitive architecture is a work-in-progress, it represents only a partial implementation of the roadmap guidelines. Consequently, we close the chapter by examining the exact extent to which each guideline has been followed. The next chapter, which concludes the book, addresses some of the research challenges posed by a complete implementation of the roadmap guidelines.
---
paper_title: UAV Operator Human Performance Models

paper_content:

Abstract : The Distributed Operator Model Architecture (D-OMAR) was used as the software environment in which to build an Unmanned Aerial Vehicle (UAV) test bed. Using D-OMAR, models were developed for UAVs each with a sensor package that included daytime-TV and infrared cameras. Human performance models were developed for the Aerial Vehicle Operator (AVO) and the Sensor Operator (SO) The modeled workplace from which the AVO and SO managed a mission included positions for the AVO's control of the vehicle the SO's use of the sensor package. With the basic elements of the test bed defined, a use case was developed based on a scenario (Petkosek, Warfield, and Carre;Lta, 2005) at a commercial airport. The AVO model maintained the UAV flight path and the SO, reading from a list of Essential Elements of Information (EEI), conducted and interpreted the sensor observations of the modeled airport scene coordinating them with a Multi-Function Operator (MFO), the third member of the UAV mission team. The operations of the AVO and MFO are described and additional detail is provided on the SO model that describes multitasking, the modeling of individual difference, the modeling of episodic and declarative memory, and model robustness. The report concludes with suggestions that the UAV test bed might be employed to examine workplace design and operating procedures with the goal of improving UAV mission performance, and reducing staffing and required training time.
---
paper_title: DiPRA: Distributed Practical Reasoning Architecture

paper_content:

DiPRA (Distributed Practical Reasoning Architecture) implements the main principles of practical reasoning via the distributed action selection paradigm. We introduce and motivate the underlying theoretical and computational peculiarities of DiPRA and we describe its components, also providing as a case study a guards-and-thieves task.
---
paper_title: A Cognitive System Model for Human/Automation Dynamics in Airspace Management

paper_content:

NASA has initiated a significant thrust of research and development focused on providing the flight crew and air traffic managers automation aids to increase capacity in en route and terminal area operations through the use of flexible, more fuel-efficient routing, while improving the level of safety in commercial carrier operations. In that system development, definition of cognitive requirements for integrated multi-operator dynamic aiding systems is fundamental. In order to support that cognitive function definition, we have extended the Man Machine Integrated Design and Analysis System (MIDAS) to include representation of multiple cognitive agents (both human operators and intelligent aiding systems) operating aircraft, airline operations centers and air traffic control centers in the evolving airspace. The demands of this application require representation of many intelligent agents sharing world-models, and coordinating action/intention with cooperative scheduling of goals and actions in a potentially unpredictable world of operations. The MIDAS operator models have undergone significant development in order to understand the requirements for operator aiding and the impact of that aiding in the complex nondeterminate system of national airspace operations. The operator model's structure has been modified to include attention functions, action priority, and situation assessment. The cognitive function model has been expanded to include working memory operations including retrieval from long-term store, interference, visual-motor and verbal articulatory loop functions, and time-based losses. The operator's activity structures have been developed to include prioritization and interruption of multiple parallel activities among multiple operators, to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. The model's internal representation has been be modified so that multiple, autonomous sets of equipment will function in a scenario as the single equipment sets do now. In order to support the analysis requirements with multiple items of equipment, it is necessary for equipment to access the state of other equipment objects at initialization time (a radar object may need to access the position and speed of aircraft in its area, for example), and as a function of perception and sensor system interaction. The model has been improved to include multiple world-states as a function of equipment am operator interaction. The model has been used -1o predict the impact of warning and alert zones in aircraft operation, and, more critic-ally, the interaction of flight-deck based warning mechanisms and air traffic controller action in response to ground-based conflict prediction and alerting systems. In this operation, two operating systems provide alerting to two autonomous, but linked sets of operators, whose view of the system and whose dynamics in response are radically different. System stability and operator action was predicted using the MIDAS model.
---
paper_title: This time with feeling: Integrated model of trait and state effects on cognition and behavior

paper_content:

Both stable personality characteristics (traits) and transient emotions (states) influence cognition and behavior. In this paper, we describe a methodology for modeling these effects in terms of a set of parameters that control processing within a symbolic cognitive architecture. The underlying thesis of the approach is that the combined effects of these individual differences can be modeled by varying the architecture parameters that control both processing and the structure of knowledge within the architecture modules. We describe the architecture, provide operational definitions of representative trait and state influences in terms of the controlling parameters, and demonstrate how observed trait/state phenomena are modeled in the context of the current demonstration scenario: a peacekeeping training simulation.
---
paper_title: A procedural Long Term Memory for cognitive robotics

paper_content:

This paper provides some insights into the advantages of using a Long-Term Memory (LTM) for optimizing the adaptive learning capabilities of a cognitive robot in dynamic environments. Specifically, a procedural LTM that stores basic models and behaviours is included in the evolutionary-based Multilevel Darwinist Brain (MDB) cognitive architecture. The memory system is based on learning error stability and instability to detect if a model is candidate to enter the LTM or to be recovered. A LTM replacement strategy has been developed that is based on context detection using functional comparison of the models' response. The LTM elements are tested in theoretical functions and in a simulated example using the AIBO robot in a dynamic context with successful adaptive learning results.
---
paper_title: For the right reasons: The FORR architecture for learning in a skill domain

paper_content:

The theme of this article is that knowledge acquisition can be managed as a transition from general expertise to specific expertise. FORR is a general architecture for learning and problem solving that models expertise at a set of related problem classes. This architecture postulates initial brood domain knowledge, and gradually specializes it to simulate expertise in individual problem classes. FORR is based upon a realistic portrayal of the nature of human expertise and its application. Rather than restrict learning to a single method or a single kind of knowledge, the architecture pragmatically requires multiple, disagreeing heuristic agents to collaborate on decisions. A FORR-based program learns both from its apprenticeship to an external expert model and from practice in its domain. An implementation for game playing is described that raises interesting issues about the organization and modification of conflicting expertise, and the role that experience plays in such learning. FORR's principal strengths are its smooth integration of multiple expertise, its ability to learn many ways, its tolerance for human and machine error, its graceful degradation, its transparency, and its support for a developmental paradigm.
---
paper_title: ARDIS: knowledge‐based architecture for visual system configuration in dynamic surface inspection

paper_content:

: This work presents an approach to dynamic surface inspection in laminated materials based on the configuration of a visual system to obtain good quality control of the manufacturing surface. It aims to overcome some of the limitations of the single-use visual inspection systems by integrating and differentiating knowledge. The configuration task for surface inspection is solved as a Configuration-Design task according with the CommonKADS methodology. This task is analysed at the knowledge level and is decomposed into simple subtasks to reach the inference level. The generic domain knowledge involved in the configuration and revision tasks is differentiated in six types: initial, global, environment, real-time, image quality and computer vision techniques. The main goal of the proposed architecture is to reuse this partitioned knowledge for the configuration and revision of different visual surface inspection systems. The architecture has been implemented in a platform that distinguishes several operation modes: visualization of specific surface inspection results, system configuration and knowledge injection. Results obtained in the configuration and revision tasks for two surface inspection systems, stainless-steel and wood, are presented.
---
paper_title: Learning Spatial Models for Navigation

paper_content:

Typically, autonomous robot navigation relies on a detailed, accurate map. The associated representations, however, do not readily support human-friendly interaction. The approach reported here offers an alternative: navigation with a spatial model and commonsense qualitative spatial reasoning. Both are based on research about how people experience and represent space. The spatial model quickly develops as the result of incremental learning while the robot moves through its environment. In extensive empirical testing, qualitative spatial reasoning principles that reference this model support increasingly effective navigation in a variety of built spaces.
---
paper_title: The iCub humanoid robot : An open-systems platform for research in cognitive development

paper_content:

We describe a humanoid robot platform - the iCub - which was designed to support collaborative research in cognitive development through autonomous exploration and social interaction. The motivation for this effort is the conviction that significantly greater impact can be leveraged by adopting an open systems policy for software and hardware development. This creates the need for a robust humanoid robot that offers rich perceptuo-motor capabilities with many degrees of freedom, a cognitive capacity for learning and development, a software architecture that encourages reuse & easy integration, and a support infrastructure that fosters collaboration and sharing of resources. The iCub satisfies all of these needs in the guise of an open-system platform which is freely available and which has attracted a growing community of users and developers. To date, twenty iCubs each comprising approximately 5000 mechanical and electrical parts have been delivered to several research labs in Europe and to one in the USA.
---
paper_title: Fusing disparate information within the 4D/RCS architecture

paper_content:

In this paper, we show how the 4D/RCS (real-time control system) architecture incorporates and integrates multiple types of disparate information into a common, unifying framework. 4D/RCS is based on the supposition that different information modeling techniques offer different advantages. 4D/RCS allows for the capability to capture information in formalisms and at levels of abstraction that are suitable for the way that they are expected to be used. In the context of applying the 4D/RCS architecture to achieving the ultimate goal of the control of autonomous ground vehicle navigation, we describe the procedural and declarative types of information that have been developed and their respective values towards achieving the goal. Experimental results of information fusion within the 4D/RCS architecture are presented from the Demo III experimental unmanned vehicles (XUVs) in an extended series of demonstrations and field tests.
---
paper_title: Learning in and from brain-based devices

paper_content:

Biologically based mobile devices have been constructed that differ from robots based on artificial intelligence. These brain-based devices (BBDs) contain simulated brains that autonomously categorize signals from the environment without a priori instruction. Two such BBDs, Darwin VII and Darwin X, are described here. Darwin VII recognizes objects and links categories to behavior through instrumental conditioning. Darwin X puts together the "what,""when," and "where" from cues in the environment into an episodic memory that allows it to find a desired target. Although these BBDs are designed to provide insights into how the brain works, their principles may find uses in building hybrid machines. These machines would combine the learning ability of BBDs with explicitly programmed control systems.
---
paper_title: An implemented architecture for feature creation and general reinforcement learning

paper_content:

BECCA is a brain-emulating cognition and control architecture. It was developed in order to perform general reinforcement learning, that is, to enable unmodeled systems operating in unstructured environments to perform unfamiliar tasks. It accomplishes this through complementary feature creation and reinforcement learning (RL) algorithms. Both of these algorithms are novel and have characteristics that have not been demonstrated by previously existing algorithms. This paper contains a description of BECCA and the algorithms that underlie it, illustrated with a sample implementation.
---
paper_title: MicroPsi: contributions to a broad architecture of cognition

paper_content:

The Psi theory of human action regulation is a candidate for a cognitive architecture that tackles the problem of the interrelation of motivation and emotion with cognitive processes. We have transferred this theory into a cognitive modeling framework, implemented as an AI architecture, called MicroPsi. Here, we describe the main assumptions of the Psi theory and summarize a neural prototyping algorithm that matches perceptual input to hierarchical declarative representations.
---
paper_title: Object recognition using saliency maps and HTM learning

paper_content:

In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations. In accordance with HTM's biological inspiration, human vision mechanisms can be used to preprocess the input images. Therefore, the input images undergo a saliency computation step, revealing the plausible information of the scene, where a human might fixate. The adoption of the saliency detection module releases the HTM network from memorizing redundant information and augments the classification accuracy. The efficiency of the proposed framework has been experimentally evaluated in the ETH-80 dataset, and the classification accuracy has been found to be greater than other HTM systems.
---
paper_title: A Pragmatic Path Toward Endowing Virtually-Embodied AIs with Human-Level Linguistic Capability

paper_content:

Current work is described wherein simplified versions of the Novamente cognition engine (NCE) are being used to control virtual agents in virtual worlds such as game engines and second life. In this context, an IRC (imitation-reinforcement-correction) methodology is being used to teach the agents various behaviors, including simple tricks and communicative acts. Here we describe how this work may potentially be exploited and extended to yield a pathway toward giving the NCE robust, ultimately human level natural language conversation capability. The pathway starts via using the current system to instruct NCE-controlled agents in semiosis and gestural communication; and then continues via integration of a particular sort of hybrid rule-based/statistical NLP system (which is currently partially complete) into the NCE-based virtual agent system, in such a way as to allow experiential adaptation of the rules underlying the NLP system, in a manner that builds on the agentpsilas knowledge of semiosis and gesture.
---
paper_title: Developmental Robots - A New Paradigm

paper_content:

It has been proved to be extremely challenging for humans to program a robot to such a sufficient degree that it acts properly in a typical unknown human environment. This is especially true for a humanoid robot due to the very large number of redundant degrees of freedom and a large number of sensors that are required for a humanoid to work safely and effectively in the human environment. How can we address this fundamental problem? Motivated by human mental development from infancy to adulthood, we present a theory, an architecture, and some experimental results showing how to enable a robot to develop its mind automatically, through online, real time interactions with its environment. Humans mentally “raise” the robot through “robot sitting” and “robot schools” instead of task-specific robot programming.
---
paper_title: Object Learning Through Active Exploration

paper_content:

This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts.
---
paper_title: Casimir: An Architecture for Mental Spatial Knowledge Processing

paper_content:

Mental spatial knowledge processing often uses spatio-analogical or quasipictorial representation structures such as spatial mental models or mental images. The cognitive architecture Casimir is designed to provide a framework for computationally modeling human spatial knowledge processing relying on these kinds of representation formats. In this article, we present an overview of Casimir and its components. We briefly describe the long-term memory component and the interaction with external diagrammatic representations. Particular emphasis is placed on Casimir’s working memory and control mechanisms. Regarding working memory, we describe the conceptual foundations and the processing mechanisms employed in mental spatial reasoning. With respect to control, we explain how it is realized as a distributed, emergent facility within Casimir.
---
paper_title: A Hybrid Architecture for Situated Learning of Reactive Sequential Decision Making

paper_content:

In developing autonomous agents, one usually emphasizes only (situated) procedural knowledge, ignoring more explicit declarative knowledge. On the other hand, in developing symbolic reasoning models, one usually emphasizes only declarative knowledge, ignoring procedural knowledge. In contrast, we have developed a learning model CLARION, which is a hybrid connectionist model consisting of both localist and distributed representations, based on the two-level approach proposed in [40]. CLARION learns and utilizes both procedural and declarative knowledge, tapping into the synergy of the two types of processes, and enables an agent to learn in situated contexts and generalize resulting knowledge to different scenarios. It unifies connectionist, reinforcement, and symbolic learning in a synergistic way, to perform on-line, bottom-up learning. This summary paper presents one version of the architecture and some results of the experiments.
---
paper_title: The PlayMate System

paper_content:

Research in CoSy was scenario driven. Two scenarios were created, the Play- Mate and the Explorer. One of the integration goals of the project was to build integrated systems that addressed the tasks in these two scenarios. This chapter concerns the integrated system for the PlayMate scenario.
---
paper_title: Improving Agent Learning through Rule Analysis

paper_content:

This paper addresses the problem of improving the process by which an agent learns problem solving rules from a subject matter expert. It presents two complementary rule analysis methods that discover when a rule was learned from incomplete explanations of an example, guiding the expert to provide additional explanations. One method performs a structural analysis of the learned rule, while the other method analyzes the possible rule instantiations. Both methods have been implemented in the Disciple-RKF learning agent and have been tested both in an automatic framework and during two knowledge acquisition experiments performed with subject matter experts at the US Army War College.
---
paper_title: Grounding fingers, words and numbers in a cognitive developmental robot

paper_content:

The young math learner must make the transition from a concrete number situation, such as that of counting objects (fingers often being the most readily available), to that of using a written symbolic form that stands for the quantities the sets of objects come to represent. This challenging process is often coupled to that of learning a verbal number system that is not always transparent to children. A number of theoretical approaches have been advanced to explain aspects of how this transition takes place in cognitive development. The results obtained with the model presented here, show that a symbol grounding approach can be used to implement aspects of this transition in a cognitive robot. In the current extended version, the model develops finger and word representations, through the use of finger counting and verbal counting strategies, together with the visual representations of learned number symbols, which it uses to perform basic arithmetic operations. In the final training phases, the model is able to do this using only the number symbols as addends. We consider this an example of symbolic grounding, in that through the direct sensory experience with the body (finger counting), a category of linguistic symbol is learned (number words), and both types of representations subsequently serve to ground higher level (numerical) symbols, which are later used exclusively to perform the arithmetic operations.
---
paper_title: A comparison between cognitive and AI models of blackjack strategy learning

paper_content:

Cognitive models of blackjack playing are presented and investigated. Blackjack playing is considered a useful test case for theories on human learning. Curiously, despite the existence of a relatively simple, well-known and optimal strategy for blackjack, empirical studies have found that casino players play quite differently from that strategy. The computational models presented here attempt to explain this result by modelling blackjack playing using the cognitive architecture CHREST. Two approaches to modeling are investigated and compared; (i) the combination of classical and operant conditioning, as studied in psychology, and (ii) SARSA, as studied in AI.
---
paper_title: Scene understanding using DSO Cognitive Architecture

paper_content:

This paper presents how a novel cognitive architecture, DSO Cognitive Architecture (DSO-CA), has been applied to scene understanding. DSO-CA is able to bring to bear different types of knowledge to solve problems. It has been applied to object recognition in images. The cognitive architecture is able to fuse bottom-up perceptual information, top-down contextual knowledge and visual feedback in a way similar to how human utilizes different knowledge to recognize objects in images or video scene. This enables the cognitive system to achieve scene understanding.
---
paper_title: A Pragmatic Path Toward Endowing Virtually-Embodied AIs with Human-Level Linguistic Capability

paper_content:

Current work is described wherein simplified versions of the Novamente cognition engine (NCE) are being used to control virtual agents in virtual worlds such as game engines and second life. In this context, an IRC (imitation-reinforcement-correction) methodology is being used to teach the agents various behaviors, including simple tricks and communicative acts. Here we describe how this work may potentially be exploited and extended to yield a pathway toward giving the NCE robust, ultimately human level natural language conversation capability. The pathway starts via using the current system to instruct NCE-controlled agents in semiosis and gestural communication; and then continues via integration of a particular sort of hybrid rule-based/statistical NLP system (which is currently partially complete) into the NCE-based virtual agent system, in such a way as to allow experiential adaptation of the rules underlying the NLP system, in a manner that builds on the agentpsilas knowledge of semiosis and gesture.
---
paper_title: Cognitive Constructor: An Intelligent Tutoring System Based on a Biologically Inspired Cognitive Architecture (BICA)

paper_content:

Significant progress can be made in the part of elementary school education that relies on intelligent tutoring systems (ITS), if the role of a referee and a peer advisor will be performed by a pedagogical agent that is a computer implementation of a cognitive architecture modeling the process of learning. Recent studies in cognitive architectures funded by the DARPA IPTO BICA Program have identified the key potential of feasible today artificial intelligence as bootstrapped cognitive growth (i.e., gradual acquisition of knowledge and skills using previously acquired knowledge and skills), up to a human level of intelligence in a selected domain. This approach is not limited to laboratory settings and short-term paradigms, it is intended for a long-term, open-ended learning scenario in real-world settings. Several cognitive architectures were designed for this purpose, among which is GMU BICA, a self-aware biologically inspired cognitive architecture. Here we describe a computational model of student learning based on GMU BICA and its use as an ITS called Cognitive Constructor, which has two components called a Science Microworld and a Pedagogical Agent (GMU BICA agent). Results of our analysis show that the system will be useful in elementary school education.
---
paper_title: Designing a Robot Cognitive Architecture with Concurrency and Active Perception

paper_content:

We are implementing ADAPT, a cognitive architecture for a Pioneer mobile robot, to give the robot the full range of cognitive abilities including perception, use of natural language, learning and the ability to solve complex problems. Our perspective is that an architecture based on a unified theory of robot cognition has the best chance of attaining human-level performance. Existing work in cognitive modeling has accomplished much in the construction of such unified cognitive architectures in areas other than robotics; however, there are major respects in which these architectures are inadequate for robot cognition. This paper examines two major inadequacies of current cognitive architectures for robotics: the absence of support for true concurrency and for active
---
paper_title: Investigating multimodal real-time patterns of joint attention in an hri word learning task

paper_content:

Joint attention - the idea that humans make inferences from observable behaviors of other humans by attending to the objects and events that these others humans attend to - has been recognized as a critical component in human-robot interactions. While various HRI studies showed that having robots to behave in ways that support human recognition of joint attention leads to better behavioral outcomes on the human side, there are no studies that investigate the detailed time course of interactive joint attention processes. ::: In this paper, we present the results from an HRI study that investigates the exact time course of human multi-modal attentional processes during an HRI word learning task in an unprecedented way. Using novel data analysis techniques, we are able to demonstrate that the temporal details of human attentional behavior are critical for understanding human expectations of joint attention in HRI and that failing to do so can force humans into assuming unnatural behaviors.
---
paper_title: Measuring the level of transfer learning by an AP physics problem-solver

paper_content:

Transfer learning is the ability of an agent to apply knowledge learned in previous tasks to new problems or domains. We approach this problem by focusing on model formulation, i.e., how to move from the unruly, broad set of concepts used in everyday life to a concise, formal vocabulary of abstractions that can be used effectively for problem solving. This paper describes how the Companions cognitive architecture uses analogical model formulation to learn to solve AP Physics problems. Our system starts with some basic mathematical skills, a broad common sense ontology, and some qualitative mechanics, but no equations. Our system uses worked solutions to learn how to use equations and modeling assumptions to solve AP Physics problems. We show that this process of analogical model formulation can facilitate learning over a range of types of transfer, in an experiment administered by the Educational Testing Service.
---
paper_title: A framework with reasoning capabilities for crisis response decision-support systems

paper_content:

This paper reviews the methods in decision-support systems for crisis management. While much research has been conducted in this field, little emphasis has been placed on the uncertainty representation, reasoning, learning and real time decision-making capabilities of system. The purpose of this paper is to explore the basic assumptions of constructing an intelligent decision-support system for crisis response management. A novel framework for crisis response decision-making system under the assumption of openness to various kinds of uncertainties, reasoning and learning with real-time response is proposed. We applied the Non-Axiomatic Logic in representing and reasoning the uncertainty knowledge in the framework and demonstrated the reasoning and learning mechanisms of the framework through an application in a case study in the field of urban firefighting. The results show that the framework provides a suitable model for intelligent crisis response decision support systems.
---
paper_title: Using background knowledge to speed reinforcement learning in physical agents

paper_content:

This paper describes Icarus, an agent architecture that embeds a hierarchical reinforcement learning algorithm within a language for specifying agent behavior. An Icarus program expresses an approximately correct theory about how to behave with options at varying levels of detail, while the Icarus agent determines the best options by learning from experience. We describe Icarus and its learning algorithm, then report on two experiments in a vehicle control domain. The first examines the benefit of new distinctions about state, whereas the second explores the impact of added plan structure. We show that background knowledge increases learning rate and asymptotic performance, and decreases plan size by three orders of magnitude, relative to the typical formulation of the learning problem in our test domain.
---
paper_title: Learning to Balance Grounding Rationales for Dialogue Systems

paper_content:

This paper reports on an experiment that investigates clarification subdialogues in intentionally noisy speech recognition. The architecture learns weights for mixtures of grounding strategies from examples provided by a human wizard embedded in the system. Results indicate that the architecture learns to eliminate misunderstandings reliably despite high word error rate.
---
paper_title: Cognitive architecture for mixed human-machine team interactions for space exploration

paper_content:

With some exceptions, current robotic systems need to be explicitly commanded for interactive tasks with humans. This constraint leads to a reduction in robot-human system level capabilities due to a lack of common grounding, and in addition, the robotic systems are selfcontained with little or no “social” intelligence. A cognitive architecture embodies robotic behaviors and the logical analysis of the surrounding, dynamic operational environment. JPL has developed a formal mathematical model for behavior-based robotic control, inference of human intent from sensed action, learning, and explanation capabilities all based on process algebras. This framework allows robotic systems to autonomously infer intentions/activities of humans through sensing of their actions. This paper describes the Activity Reasoning Module (ARM) that is responsible for this analysis, reviews the formal process algebra framework, and presents an experimental study in simulation of the determination of astronaut activity from sensory analysis of perceived action. 12
---
paper_title: Distinguishing Causal Interactions in Neural Populations

paper_content:

We describe a theoretical network analysis that can distinguish statistically causal interactions in population neural activity leading to a specific output. We introduce the concept of a causal core to refer to the set of neuronal interactions that are causally significant for the output, as assessed by Granger causality. Because our approach requires extensive knowledge of neuronal connectivity and dynamics, an illustrative example is provided by analysis of Darwin X, a brain-based device that allows precise recording of the activity of neuronal units during behavior. In Darwin X, a simulated neuronal model of the hippocampus and surrounding cortical areas supports learning of a spatial navigation task in a real environment. Analysis of Darwin X reveals that large repertoires of neuronal interactions contain comparatively small causal cores and that these causal cores become smaller during learning, a finding that may reflect the selection of specific causal pathways from diverse neuronal repertoires.
---
paper_title: Planning Using Multiple Execution Architectures

paper_content:

We discuss two techniques used by the RALPHMEA agent architecture to facilitate decision making in complex, real-time domains. Multiple execution architectures are four implementations of the agent function, a functionthat receives percepts from the environment as input and outputs an action choice. The four execution architectures are defined by the different knowledge types that each uses. Depending on the domain and agent capabilities, each execution architecture has different speed and correctness properties. Metalevel control of planning computes the value of information of planning to compare to the utility of executing the current default plan. Examples are presented from an autonomous, underwater vehicle domain.
---
paper_title: Man-Machine Design and Analysis System (MIDAS) Applied to a Computer-Based Procedure-Aiding System

paper_content:

Difficult issues in design criteria confront the designers of human—computer interaction (HCI) implementations for future power plant control rooms. Such HCI-intensive control—room elements include “soft” controls and displays, computerized procedures, alarm presentations, and support for cooperative information—sharing among crewmembers. This shift in technology, from dedicated controls and displays in fixed locations to multifunction computer—driven operator workstations and wall displays, must focus not only on the required functionality of these interfaces, but also on their crafting and integration in such a way as to minimize the likelihood of operator error.With the objective of providing early insight into the cognitively error—prone consequences of selected interface dynamics, we are adapting a computer—based cognitive modeling tool, the Man—machine Integrated Design and Analysis System (MIDAS), to quantitatively model certain user requirements for operating different types of interfaces while de...
---
paper_title: Tracing Problem Solving in Real Time: fMRI Analysis of the Subject-paced Tower of Hanoi

paper_content:

Previous research has found three brain regions for tracking components of the ACT-R cognitive architecture: a posterior parietal region that tracks changes in problem representation, a prefrontal region that tracks retrieval of task-relevant information, and a motor region that tracks the programming of manual responses. This prior research has used relatively simple tasks to incorporate a slow event-related procedure, allowing the blood oxygen level-dependent (BOLD) response to go back to baseline after each trial. The research described here attempts to extend these methods to tracking problem solving in a complex task, the Tower of Hanoi, which involves many complex steps of cognition and motor actions in rapid succession. By tracking the activation patterns in these regions, it is possible to predict with intermediate accuracy when participants are planning a future sequence of moves. The article describes a cognitive model in the ACT-R architecture that is capable of explaining both the latency data in move generation and the BOLD responses in these three regions.
---
paper_title: Evaluation and Application of MIDAS v2.0

paper_content:

Version 2.0 of the Man-Machine Design and Analysis System (MIDAS) was released in 2001. It provides tools to describe an operating environment, mission, and equipment. User-defined goals, procedures, and knowledge interact with and are modified by models of perception, memory, situation awareness, and attention and constrained by the environment. Output of simulations that demonstrate or evaluate new capabilities or answer questions posed by customers are presented graphically and visually. MIDAS has been used to model different professions (soldiers in protective gear, air traffic controllers, astronauts, nuclear power plant operators, pilots), missions (e.g., flying, target designation, underwater exploration, police dispatch) and environments (e.g., battlefields, civil airspace, ocean floor, control rooms, low earth orbit). A recent independent evaluation of MIDAS V2.0 is reviewed.
---
paper_title: Cognitive Task Analysis of Expertise in Air Traffic Control

paper_content:

A cognitive task analysis was performed to analyze knowledge structures, mental models, skills, and strategies of en route controllers to provide an understanding of the key cognitive components of the air traffic controller's job. This article presents the results of three procedures as they contributed to an understanding of controller expertise: paper problem solving, performance modeling, and structured problem solving. The procedures resulted in the identification of (a) 13 primary tasks, (b) a mental model representing expert controller's organization of domain knowledge, (c) three categories of controller strategies, and (d) a hierarchy of goals. These results are being used to specify the instructional content and sequencing for the new Federal Aviation Administration en route air traffic control curriculum.
---
paper_title: Automated Intelligent Pilots for Combat Flight Simulation

paper_content:

TACAIR-SOAR is an intelligent, rule-based system that generates believable humanlike behavior for large-scale, distributed military simulations. The innovation of the application is primarily a matter of scale and integration. The system is capable of executing most of the airborne missions that the U.S. military flies in fixed-wing aircraft. It accomplishes its missions by integrating a wide variety of intelligent capabilities, including real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated entities, maintenance of situational awareness, and the ability to accept and respond to new orders while in flight. The system is currentl y deployed at the Oceana Naval Air Station WISSARD (what-if simulation system for advanced research and development) Lab and the Air Force Research Laboratory in Mesa, Arizona. Its most dramatic use was in the Synthetic Theater of War 1997, which was an operational training exercise that ran for 48 continuous hours during which TACAIR-SOAR flew all U.S. fixed-wing aircraft.
---
paper_title: A Procedure for Collecting Mental Workload Data During an Experiment That Is Comparable to IMPRINT Workload Data

paper_content:

Abstract : U.S. Army Research Laboratory (ARL) analysts use the Improved Performance Research Integration Tool (IMPRINT) to predict the mental workload and performance of Soldiers operating the Future Combat System (FCS) manned ground vehicles. IMPRINT is a human-performance-modeling tool that analysts use to build models representing Soldiers interacting with equipment to accomplish a mission. The models contain tasks, task sequences, task times, and workload estimates that allow the software to calculate estimates of mental workload and mission performance. One of the key outputs from the IMPRINT models is the combination of tasks likely to contribute to high Soldier workload. Evaluators of FCS equipment can include the potentially high workload task combinations into their evaluations to be sure that they evaluate the tasks mostly like to contribute to mental overload. The U.S. Army Aberdeen Test Center (ATC) is one of the groups responsible for testing and evaluating FCS equipment and concepts. They must identify any issues that might degrade mission performance, including Soldier mental overload. To ensure that the ATC evaluations include tasks relevant to Soldier mental workload they can include high workload task combinations identified by IMPRINT into their test plans. However, to be compatible with IMPRINT, they must evaluate workload by a methodology compatible with the IMPRINT technique. This report outlines the methodology ATC and ARL developed within the Automated Communications Analysis of Situation Awareness /IMPRINT/Joint Warfighter Test and Training Capability test conducted in May 2008.
---
paper_title: Cognitive Robotics using the Soar Cognitive Architecture

paper_content:

Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar’s original symbolic processing, which improves Soar abilities for control of robots. These extensions include mental imagery, episodic and semantic memory, reinforcement learning, and continuous model learning. This paper presents research in mobile robotics, relational and continuous model learning, and learning by situated, interactive instruction.
---
paper_title: Multimodal People Engagement with iCub

paper_content:

In this paper we present an engagement system for the iCub robot that is able to arouse in human partners a sense of “co-presence” during human-robot interaction. This sensation is naturally triggered by simple reflexes of the robot, that speaks to the partners and gazes the current “active partner” (e.g. the talking partner) in interaction tasks. The active partner is perceived through a multimodal approach: a commercial rgb-d sensor is used to recognize the presence of humans in the environment, using both 3d information and sound source localization, while iCub’s cameras are used to perceive his face.
---
paper_title: The developmental approach to multimedia speech learning

paper_content:

This paper introduces the developmental approach to speech learning, motivated by human cognitive development from infancy to adulthood. Central in the developmental approach is what is called the developmental algorithm. We introduce AA-learning as a basic learning mode for our developmental algorithm. The developmental algorithm enables the system to learn new tasks without a need of reprogramming. Some experimental results for automated animal-like learning (AA-learning) using our developmental algorithm are presented.
---
paper_title: Integration of Speech and Action in Humanoid Robots: iCub Simulation Experiments

paper_content:

Building intelligent systems with human level competence is the ultimate grand challenge for science and technology in general, and especially for cognitive developmental robotics. This paper proposes a new approach to the design of cognitive skills in a robot able to interact with, and communicate about, the surrounding physical world and manipulate objects in an adaptive manner. The work is based on robotic simulation experiments showing that a humanoid robot (iCub platform) is able to acquire behavioral, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to understand basic instructions, and to adapt its abilities to changes in internal and environmental conditions.
---
paper_title: Toward sociable robots

paper_content:

Abstract This paper explores the topic of social robots—the class of robots that people anthropomorphize in order to interact with them. From the diverse and growing number of applications for such robots, a few distinct modes of interaction are beginning to emerge. We distinguish four such classes: socially evocative, social interface, socially receptive, and sociable. For the remainder of the paper, we explore a few key features of sociable robots that distinguish them from the others. We use the vocal turn-taking behavior of our robot, Kismet, as a case study to highlight these points.
---
paper_title: Isolated word recognition in the Sigma cognitive architecture

paper_content:

Abstract Symbolic architectures are effective at complex cognitive reasoning, but typically are incapable of important forms of sub-cognitive processing – such as perception – without distinct modules connected to them via low-bandwidth interfaces. Neural architectures, in contrast, may be quite effective at the latter, but typically struggle with the former. Sigma has been designed to leverage the state-of-the-art hybrid (discrete + continuous) mixed (symbolic + probabilistic) capability of graphical models to provide in a uniform non-modular fashion effective forms of, and integration across, both cognitive and sub-cognitive behavior. Here it is shown that Sigma is not only capable of performing a simple variant of speech recognition via the same knowledge structures and reasoning algorithm used for cognitive processing, but also of leveraging its existing knowledge templates and learning algorithm to acquire automatically most of the structures and parameters needed for this recognition activity.
---
paper_title: A framework for resolving open-world referential expressions in distributed heterogeneous knowledge bases

paper_content:

We present a domain-independent approach to reference resolution that allows a robotic or virtual agent to resolve references to entities (e.g., objects and locations) found in open worlds when the information needed to resolve such references is distributed among multiple heterogeneous knowledge bases in its architecture. An agent using this approach can combine information from multiple sources without the computational bottleneck associated with centralized knowledge bases. The proposed approach also facilitates "lazy constraint evaluation", i.e., verifying properties of the referent through different modalities only when the information is needed. After specifying the interfaces by which a reference resolution algorithm can request information from distributed knowledge bases, we present an algorithm for performing open-world reference resolution within that framework, analyze the algorithm's performance, and demonstrate its behavior on a simulated robot.
---
paper_title: Continuous phone recognition in the Sigma cognitive architecture

paper_content:

Abstract Spoken language processing is an important capability of human intelligence that has hitherto been unexplored by cognitive architectures. This reflects on both the symbolic and sub-symbolic nature of the speech problem, and the capabilities provided by cognitive architectures to model the latter and its rich interplay with the former. Sigma has been designed to leverage the state-of-the-art hybrid (discrete + continuous) mixed (symbolic + probabilistic) capability of graphical models to provide in a uniform non-modular fashion effective forms of, and integration across, both cognitive and sub-cognitive behavior. In this article, previous work on speaker dependent isolated word recognition has been extended to demonstrate Sigma’s feasibility to process a stream of fluent audio and recognize phones, in an online and incremental manner with speaker independence. Phone recognition is an important step in integrating spoken language processing into Sigma. This work also extends the acoustic front-end used in the previous work in service of speaker independence. All of the knowledge used in phone recognition was added supraarchitecturally – i.e. on top of the architecture – without requiring the addition of new mechanisms to the architecture.
---
paper_title: An architectural framework for complex cognition

paper_content:

Any non-trivial task requires an appropriate representational formalism. Usually, for single-task or single-domain problems this choice of formalism is not explicitly made by the agent itself, but by the agent designer, and is implicit in the choice of data structures and algorithms used by the agent. However, complex cognition involves domains where the type of problems that the agent is expected to solve is not clear at the outset. Instead, at each stage of the problem solving process, the agent is expected to choose an appropriate formalism, solve the problem and integrate these results over the course of the entire problem solving episode. In this paper, we present one approach to solving two of the above problems - how does an agent choose the right representation and how can it integrate results from multiple representations over the course of problem solving? We present this approach in the context of Polyscheme, a cognitive architecture that is strongly integrated, focused on inference and adaptive to new information. We describe the representational formalisms and associated processes present in Polyscheme (propositional and spatial) and the decision cycle that allows information from multiple representations to be integrated. Using examples from complex tasks such as constraint satisfaction, language understanding and planning, we show how a Polyscheme agent can show improved performance by leveraging its multiple representations without the hindsight of representational choice.
---
paper_title: Odor discrimination using adaptive resonance theory

paper_content:

Abstract The paper presents two neural networks based on the adaptive resonance theory (ART) for the recognition of several odors subjected to drift. The neural networks developed by Grossberg (supervised and unsupervised) have been used for two different drift behaviors. One in which the clusters end up to overlap each other and the other when they do not. The latter case is solved by unsupervision, which is useful to track the moving clusters and possibly discover new odors autonomously.
---
paper_title: Fault diagnosis of pneumatic systems with artificial neural network algorithms

paper_content:

Pneumatic systems repeat the identical programmed sequence during their operation. The data was collected when the pneumatic system worked perfectly and had some faults including empty magazine, zero vacuum, inappropriate material, no pressure, closed manual pressure valve, missing drilling stroke, poorly located material, not vacuuming the material and low air pressure. The signals of eight sensors were collected during the entire sequence and the 24 most descriptive features of the data were encoded to present to the ANNs. A synthetic data generation process was proposed to train and test the ANNs better when signals are extremely repetitive from one sequence to other. Two artificial neural networks (ANN) were used for interpretation of the encoded signals. The tested ANNs were Adaptive Resonance Theory 2 (ART2), and Back propagation (Bp). ART2 correctly distinguished the perfect and faulty operations at all the tested vigilance values. It classified 11 faulty and 1 normal modes in seven or eight categories at the best vigilance values. Bp also distinguished perfect and faulty operations without even the slightest uncertainty. In less than 10 cases, it had difficulty identifying the 11 types of possible faults. The average estimation error of the Bp was better than 2.1% of the output range on the test data which was created by deviating the encoded values. The ART2 and Bp performance was found excellent with the proposed encoding and synthetic data generation procedures for extremely repetitive sequential data.
---
paper_title: Continuous Online Sequence Learning with an Unsupervised Neural Network Model

paper_content:

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory is recently proposed as a theoretical framework for sequence learning in the cortex. In this paper, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable-order temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average (ARIMA), feedforward neural networks: online sequential extreme learning machine (ELM), and recurrent neural networks: long short-term memory (LSTM) and echo-state networks (ESN), on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyper- parameters tuning. Therefore the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem, but is also applicable to a wide range of real-world problems such as discrete and continuous sequence prediction, anomaly detection, and sequence classification.
---
paper_title: The power of a nod and a glance: Envelope vs. emotional feedback in animated conversational agents

paper_content:

In this article we describe results froman experiment of user interaction with autonomous , human - like ( humanoid ) conversational agents . We hypothesize that for embodied conversational agents , nonverbal behaviors related to the process of conversation , what we call envelope feedback, is much more important than other feedback , such as emotional expression . We test this hypothesis by having subjects interact with three autonomous agents , all capable of full - duplex multimodal interaction: able to generate and recognize speech , intonation , facial displays , and gesture . Each agent , however , gave a different kind of feedback: ( 1 ) content - related only , ( 2 ) content + envelope feedback , and ( 3 ) content + emotional . Content-related feedback includes answering questions and executing commands; envelope feedback includes behaviors such as gaze , manual beat gesture , and head movements; emotional feedback includes smiles and looks of puzzlement . Subjects' evaluations of the systemwere c...
---
paper_title: Evaluation of Hierarchical Temporal Memory for a Real World Application

paper_content:

A large number of real world applications, such as user support systems, can still not be performed easily by conventional algorithms in comparison with the human brain. Such intelligence is often implemented, by using probability based systems. This paper focuses on comparing the implementation of a cellular phone intention estimation example on a Bayesian Network and Hierarchical Temporal Memory. It is found that Hierarchical Temporal Memory is a system that requires little effort for designing the application, and with some extra effort, further optimised results can easily be obtained.
---
paper_title: Speaker normalization using cortical strip maps : A neural model for steady state vowel categorization

paper_content:

Auditory signals of speech are speaker dependent, but representations of language meaning are speaker independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by adaptive resonance theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [Peterson, G. E., and Barney, H.L., J. Acoust. Soc. Am. 24, 175-184 (1952).] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.
---
paper_title: Biased ART: a neural architecture that shifts attention toward previously disregarded features following an incorrect prediction

paper_content:

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Two-dimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.
---
paper_title: Guiding Probabilistic Logical Inference with Nonlinear Dynamical Attention Allocation

paper_content:

In order to explore the practical manifestations of the “cognitive synergy” between the PLN (Probabilistic Logic Networks) and ECAN (Economic Attention Network) components of the OpenCog AGI architecture, we explore the behavior of PLN and ECAN operating together on two standard test problems commonly used with Markov Logic Networks (MLN). Our preliminary results suggest that, while PLN can address these problems adequately, ECAN offers little added value for the problems in their standard form. However, we outline modified versions of the problem that we hypothesize would demonstrate the value of ECAN more effectively, via inclusion of confounding information that needs to be heuristically sifted through.
---
paper_title: Wavelet-based feature-adaptive adaptive resonance theory neural network for texture identification

paper_content:

A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extrac- tion based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ''low- level'' features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ''cognitive sys- tem.'' The novelty of the model developed in this paper lies in the use of a self-organizing input layer to the fuzzy ART. Evaluation of the model is performed by using natural textures, and results ob- tained show that the developed model is capable of performing the texture recognition task effectively. Applications of the developed model include the study of artificial vision systems motivated by the human visual system model. © 1997 SPIE and IS&T. (S1017-9909(97)00403-0)
---
paper_title: Goal-Driven Cognition in the Brain: A Computational Framework

paper_content:

Current theoretical and computational models of dopamine-based reinforcement learning are largely rooted in the classical behaviorist tradition, and envision the organism as a purely reactive recipient of rewards and punishments, with resulting behavior that essentially reflects the sum of this reinforcement history. This framework is missing some fundamental features of the affective nervous system, most importantly, the central role of goals in driving and organizing behavior in a teleological manner. Even when goal-directed behaviors are considered in current frameworks, they are typically conceived of as arising in reaction to the environment, rather than being in place from the start. We hypothesize that goal-driven cognition is primary, and organized into two discrete phases: goal selection and goal engaged, which each have a substantially different effective value function. This dichotomy can potentially explain a wide range of phenomena, playing a central role in many clinical disorders, such as depression, OCD, ADHD, and PTSD, and providing a sensible account of the detailed biology and function of the dopamine system and larger limbic system, including critical ventral and medial prefrontal cortex. Computationally, reasoning backward from active goals to action selection is more tractable than projecting alternative action choices forward to compute possible outcomes. An explicit computational model of these brain areas and their function in this goal-driven framework is described, as are numerous testable predictions from this framework.
---
paper_title: Simple perception-action strategy based on hierarchical temporal memory

paper_content:

This paper presents a simple strategy for perception-action of robots in indoor environments using Hierarchical Temporal Memory which is the theory of modeling the rationale of the neocortex. The main idea of the present study is that the input of the HTM network is images of objects that robot perceives in environment, and the output of HTM network is action, such as moving along the wall, moving away, opening, and moving forward, etc. Experiments results show that the proposed method can be applied for robot learning and navigation because it imitates humans' thinking mode to process the information it receives.
---
paper_title: Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling

paper_content:

In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neocortex and artificial intelligence models of machine learning. Much of this work has focussed on the mammalian visual cortex, treating it as a hierarchically-structured pattern recognition machine that exploits statistical regularities in retinal input. It has further been proposed that the neocortex represents sensory information probabilistically, using some form of Bayesian inference to disambiguate noisy data. ::: ::: In the current paper, we focus on a particular model of the neocortex developed by Hawkins, known as hierarchical temporal memory (HTM). Our aim is to evaluate an important and recently implemented aspect of this model, namely its ability to represent temporal sequences of input within a hierarchically structured vector quantization algorithm. We test this temporal pooling feature of HTM on a benchmark of cursive handwriting recognition problems and compare it to a current state-of-the-art support vector machine implementation. We also examine whether two pre-processing techniques can enhance the temporal pooling algorithm's performance. Our results show that a relatively simple temporal pooling approach can produce recognition rates that approach the current state-of-the-art without the need for extensive tuning of parameters. We also show that temporal pooling performance is surprisingly unaffected by the use of preprocessing techniques.
---
paper_title: Image classification using HTM cortical learning algorithms

paper_content:

Recently the improved bag of features (BoF) model with locality-constrained linear coding (LLC) and spatial pyramid matching (SPM) achieved state-of-the-art performance in image classification. However, only adopting SPM to exploit spatial information is not enough for satisfactory performance. In this paper, we use hierarchical temporal memory (HTM) cortical learning algorithms to extend this LLC & SPM based model. HTM regions consist of HTM cells are constructed to spatial pool the LLC codes. Each cell receives a subset of LLC codes, and adjacent subsets are overlapped so that more spatial information can be captured. Additionally, HTM cortical learning algorithms have two processes: learning phase which make the HTM cell only receive most frequent LLC codes, and inhibition phase which ensure that the output of HTM regions is sparse. The experimental results on Caltech 101 and UIUC-Sport dataset show the improvement on the original LLC & SPM based model.
---
paper_title: Agent Smith: Towards an evolutionary rule-based agent for interactive dynamic games

paper_content:

The goal of this project is to develop an agent to play the first-person shooter game Unreal Tournament 2004 [1], a fast-paced and dynamic environment that demands that the agent must be capable of making decisions quickly. An additional goal of this project is to explore evolutionary computation as a means for learning the rule sets used to control the game-playing agent. The agent's behavior is controlled by a rule-based system, which looks at multiple high-level conditions, such as whether the agent is weak, and determines a single high-level action, such as to head for the nearest known healing source. Using an evolutionary computation approach, in which the behavior is evolved over a number of generations, the agent learns increasingly better strategies for its environment. Through the work in this project, we are exploring several research questions, including the development of successful vocabulary of high-level conditions and actions for the rule set, the challenges of using the evolutionary process to hone a rule set, and the effects of using some expert knowledge in combination with the evolutionary process.
---
paper_title: Imagery in cognitive architecture: Representation and control at multiple levels of abstraction

paper_content:

In a cognitive architecture, intelligent behavior is contingent upon the use of an appropriate abstract representation of the task. When designing a general-purpose cognitive architecture, two basic challenges related to abstraction arise, which are introduced and examined in this article. The perceptual abstraction problem results from the difficulty of creating a single perception system able to induce appropriate abstract representations in any task the agent might encounter, and the irreducibility problem arises because some tasks are resistant to being abstracted at all. The first contribution of this paper is identifying these problems, and the second contribution is showing a means to address them. This is accomplished through the use of mental imagery. To support imagery, a concrete (highly detailed) representation of the spatial state of the problem is maintained as an intermediate between the external world and an abstract representation. Actions can be simulated (imagined) in terms of this concrete representation, and the agent can derive abstract information by applying perceptual processes to the resulting concrete state. Imagery works to mitigate the perceptual abstraction problem by allowing a given perception system to work in a wider variety of tasks, since perception can be dynamically combined with imagery, and works to mitigate the irreducibility problem by allowing internal simulation of low-level control processes. To demonstrate these benefits, an implementation is described, which is an extension of the Soar architecture. An agent in this architecture that uses reinforcement learning and imagery to play an arcade game and an agent that performs sampling-based motion planning for a car-like vehicle are described, demonstrating the perceptual abstraction and irreducibility problems and the associated use of imagery to mitigate those problems in complex AI tasks. Previous AI systems have incorporated imagery-like processes, however, the functional benefit of imagery in those systems has typically been characterized as the ability to perform more efficient inference through the use of a specialized representation. The use of imagery here shows further benefits related to the perceptual abstraction and irreducibility problems, enriching the broader understanding of the role of imagery in cognitive systems.
---
paper_title: Modelling a Human-Like Bot in a First Person Shooter Game

paper_content:

Autonomous agents in videogames, usually called bots, have tried to behave as human players from their emergence more than 20 years ago. They normally try to model a part of a human expert player's knowledge with respect to the game, trying to become a competitive opponent or a good partner for other players. This paper presents a deep description of the design of a bot for playing 1 vs. 1 Death Match mode in the first person shooter Unreal TournamentTM 2004 UT2K4. This bot uses a state-based Artificial Intelligence model which emulates a big part of the behavior/knowledge actions and tricks of an expert human player in this mode. This player has participated in international UT2K4 championships. The behavioral engine considers primary and secondary actions, and uses a memory approach. It is based in an auxiliary database for learning about the fighting arena, so it stores weapons and items locations once the bot has discovered them, as a human player would do. This so-called Expert Bot has yielded excellent results, beating the game default bots even in the hardest difficulty, and being a very hard opponent for medium-level human players.
---
paper_title: Hierarchical controller learning in a First-Person Shooter

paper_content:

We describe the architecture of a hierarchical learning-based controller for bots in the First-Person Shooter (FPS) game Unreal Tournament 2004. The controller is inspired by the subsumption architecture commonly used in behaviourbased robotics. A behaviour selector decides which of three sub-controllers gets to control the bot at each time step. Each controller is implemented as a recurrent neural network, and trained with artificial evolution to perform respectively combat, exploration and path following. The behaviour selector is trained with a multiobjective evolutionary algorithm to achieve an effective balancing of the lower-level behaviours. We argue that FPS games provide good environments for studying the learning of complex behaviours, and that the methods proposed here can help developing interesting opponents for games.
---
paper_title: Extensions and Applications of Pogamut 3 Platform

paper_content:

We present recent extensions for the intelligent virtual agent development platform Pogamut 3. These extensions are: the agent coordination language StorySpeak designed for the purposes of storytelling applications, support for gestures based on BML, the visual editor of reactive plans, connection of the cognitive architecture ACT-R, an educational application aimed at orientation in urban areas and finally an emotional model.
---
paper_title: Creating Human-like Autonomous Players in Real-time First Person Shooter Computer Games

paper_content:

This paper illustrates how we create a software agent by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first person shooter computer game known as Unreal Tournament 2004. Through interacting with the game environment and its opponents, our agent learns in real-time without any human intervention. Our agent bot participated in the 2K Bot Prize competition, similar to the Turing test for intelligent agents, wherein human judges were tasked to identify whether their opponents in the game were human players or virtual agents. To perform well in the competition, an agent must act like human and be able to adapt to some changes made to the game. Although our agent did not emerge top in terms of humanlike, the overall performance of our agent was encouraging as it acquired the highest game score while staying convincing to be human-like in some judges’ opinions.
---
paper_title: Brain-Based Devices for the Study of Nervous Systems and the Development of Intelligent Machines

paper_content:

The simultaneous study of brain function at all levels of organization is difficult to undertake with current experimental tools. Present day electrophysiology only allows the recording of at most hundreds of neurons while an animal is performing a behavioral task. Because of this limitation and the sheer complexity of the nervous system, computational modeling has become essential in developing theories of brain function. Accordingly, our group has constructed a series of brain-based devices (BBDs), that is, physical devices with simulated nervous systems that guide behavior, to serve as a heuristic for testing theories of brain function. Unlike animal models, BBDs permit analysis of activity at all levels of the nervous system as the device behaves in its environment. Although the principal focus of developing BBDs has been to test theories of brain function, this type of modeling may also provide a basis for robotic design and practical applications.
---
paper_title: An Architecture for Vision and Action

paper_content:

Vision systems that have successfully supported nontrivial tasks have invariably taken advantage of constraints derived from the task and environment to increase reliability and lower the complexity of perception. We propose that it is possible to build a general purpose vision system, that is, one that can support a wide variety of tasks, and take advantage of such constraints. The central idea within our proposed architecture is the reactive skill. Skills are concurrent control routines assembled at run time using instructions from a symbolic execution system. Visual modules are used as resources in the construction of these skills. Skills control the agent as continuous feedback loops but are constructed using discrete, symbolic instructions. The key to general-purpose vision is the ability to parametrize the primitive elements of the vision system and to compose visual and control routines in a variety of ways. We demonstrate the architecture in the context of an implemented example task of a robot collecting trash off a floor and depositing it in a garbage can.
---
paper_title: 1 Iterative Learning of Grasp Adaptation through Human Corrections

paper_content:

In the context of object interaction and manipulation, one characteristic of a robust grasp is its ability to comply with external perturbations applied to the grasped object while still maintaining the grasp. In this work, we introduce an approach for grasp adaptation which learns a statistical model to adapt hand posture solely based on the perceived contact between the object and fingers. Using a multi-step learning procedure, the model dataset is built by first demonstrating an initial hand posture, which is then physically corrected by a human teacher pressing on the fingertips, exploiting compliance in the robot hand. The learner then replays the resulting sequence of hand postures, to generate a dataset of posture-contact pairs that are not influenced by the touch of the teacher. A key feature of this work is that the learned model may be further refined by repeating the correction-replay steps. Alternatively, the model may be reused in the development of new models, characterized by the contact signatures of a different object. Our approach is empirically validated on the iCub robot. We demonstrate grasp adaptation in response to changes in contact, and show successful model reuse and improved adaptation with additional rounds of model refinement.
---
paper_title: Analogical Generalization of Actions from Single Exemplars in a Robotic Architecture

paper_content:

Humans are often able to generalize knowledge learned from a single exemplar. In this paper, we present a novel integration of mental simulation and analogical generalization algorithms into a cognitive robotic architecture that enables a similarly rudimentary generalization capability in robots. Specifically, we show how a robot can generate variations of a given scenario and then use the results of those new scenarios run in a physics simulator to generate generalized action scripts using analogical mappings. The generalized action scripts then allow the robot to perform the originally learned activity in a wider range of scenarios with different types of objects without the need for additional exploration or practice. In a proof-of-concept demonstration we show how the robot can generalize from a previously learned pick-and-place action performed with a single arm on an object with a handle to a pick-and-place action of a cylindrical object with no handle with two arms.
---
paper_title: CAD Directed Robotic Deburring

paper_content:

Robots prove advantageous for the deburring of machined parts because they are more repeatable than humans and suffer no fatigue. However, most deburring robots are programmed manually, requiring a large investment in programming time. At the National Bureau of Standards' Automated Manufacturing Research Facility, research is being conducted on techniques to automate robot programming. Atechnique has been developed and demonstrated in the Cleaning and Deburring Workstation which uses CAD geometry data to automatically generate robot deburring paths. Using a graphics interface, an operator specifies the edges on a part to be deburred, the deburring tools to be used, and the speeds, feed rates, and contact forces desired. Deburring paths are generated and sent to a PUMA 760 robot controlled by the NBS Real-Time Control System. The robot uses a two-pass technique for deburring. On the first pass, the robot uses force feedback to correct the deburring path points to account for robot kinematic errors, tool wear, and minor part misplacement. On the second pass, the robot follows the corrected path, deburring the part. This paper describes the techniques, algorithms and data formats used in this robotic deburring system.
---
paper_title: A parallel distributed cognitive control system for a humanoid robot

paper_content:

During the last decade, Researchers at Vanderbilt have been developing a humanoid robot called the Intelligent Soft Arm Control (ISAC). This paper describes ISAC in terms of its software components and with respect to the design philosophy that has evolved over the course of its development. Central to the control system is a parallel, distributed software architecture, comprising a set of independent software objects or agents that execute as needed on standard PCs linked via Ethernet. Fundamental to the design philosophy is the direct physical interaction of the robot with people. Initially, this philosophy guided application development. Yet over time it became apparent that such interaction may be necessary for the acquisition of intelligent behaviors by an agent in a human-centered environment. Concurrent to that evolution was a shift from a programmer’s high-level specification of action toward the robot’s own motion acquisition of primitive behaviors through sensory-motor coordination (SMC) and task learning through cognitive control and working memory. . Described is the parallel distributed cognitive control architecture and the advantages and limitations that have guided its development. Primary structures for sensing, memory, and cognition are described. Motion learning through teleoperation and fault diagnosis through system health monitoring are described. The generality of the control system is discussed in terms of its applicability to physically heterogeneous robots and multi-robot systems.
---
paper_title: NIST research in autonomous construction

paper_content:

The National Institute of Standards and Technology Construction Metrology and Automation Group is conducting ongoing research to provide standards, methodologies, and performance metrics that will assist the development of advanced systems to automate construction tasks. Initial research in this project, entitled "Performance of Innovative Technologies for Automated Construction," has focused on autonomous large-scale pick-and-place operations using the assembly of structural steel as a test operation. An Automated Construction Testbed (ACT) is under development to research advanced concepts in crane automation, site metrology, laser-based 3D imaging, construction object identification and tracking, sensor-based data exchange, site status visualization, and design data integration for autonomous system planning. This paper provides an overview of this research effort.
---
paper_title: A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network

paper_content:

Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long-term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous one, which control a six-legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented, the robot's performance and behavior repertoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical disabling problems have been ignored.
---
paper_title: The NIST Real-time Control System (RCS): an approach to intelligent systems research

paper_content:

Differences between the deliberative and reactive aspects of behaviour have led to different scientific approaches that have divided the field of cognitive research for at least a century. In the tradition of the behaviourist school of psychology, many present day researchers in advanced robotics and artificial intelligence have abandoned inquiry into the complexities of cognitive behaviour in favour of a minimalist philosophy that focuses primarily on stimulus-response reactivity. In contrast, the Real-time Control System (RCS) developed at NIST and elsewhere over the past two decades provides a model for bridging the gap between deliberative and reactive behaviours. RCS is a reference model architecture for intelligent systems design that consists of a hierarchically layered set of processing nodes. In each node, there are both cognitive and reactive elements. At each layer, entities are recognized, tasks are deliberatively planned, and feedback from sensors closes a reactive control loop. RCS thus inte...
---
paper_title: Hybrid laser and vision based object search and localization

paper_content:

We describe a method for an autonomous robot to efficiently locate one or more distinct objects in a realistic environment using monocular vision. We demonstrate how to efficiently subdivide acquired images into interest regions for the robot to zoom in on, using receptive field cooccurrence histograms. Objects are recognized through SIFT feature matching and the positions of the objects are estimated. Assuming a 2D map of the robot's surroundings and a set of navigation nodes between which it is free to move, we show how to compute an efficient sensing plan that allows the robot's camera to cover the environment, while obeying restrictions on the different objects' maximum and minimum viewing distances. The approach has been implemented on a real robotic system and results are presented showing its practicability and the quality of the position estimates obtained.
---
paper_title: RPD-enabled agents teaming with humans for multi-context decision making

paper_content:

Team decision making under stress involving multiple contexts is an extremely challenging issue faced by various real world application domains. This research is targeted at coupling cognitive agent technology and human-centered teamwork to address the informational challenges associated with Command and Control (C2) teams in contemporary military operations. Two sets of experiments, each with various settings of context switching frequencies and tasking complexities, were conducted. To ensure that the human subjects were familiar with the C2 context, they were selected from US Army ROTC (Reserve Officer Training Corps) students. Experiments on C2 teams that involve human subjects only were conducted first. We observed the decision making behavior of human subjects and incorporated expert behaviors into R-CAST---an agent architecture built upon a naturalistic decision making model that captures how domain experts make decisions based on experiences and situational similarity recognition. We then conducted another set of experiments with R-CAST agents as teammates and decision aids for human subjects. The results show that RPD-enabled agents can significantly improve the tasking capacity of C2 teams in multi-context decision making under stress. It also suggests that higher demand situations require more competent teammates.
---
paper_title: Probabilistic Logic Based Reinforcement Learning of Simple Embodied Behaviors in a 3D Simulation World

paper_content:

Logic-based AI is often thought of as being restricted to highly abstract domains such as theorem-proving and linguistic semantics. In the Novamente AGI architecture, however, probabilistic logic is used for a wider variety of purposes, including simple reinforcement learning of infantile behaviors, which are primarily concerned with perception and action rather than abstract cognition. This paper reports some simple experiments designed to validate the viability of this approach, via using the PLN probabilistic logic framework, implemented within the Novamente AGI architecture, to carry out reinforcement learning of simple embodied behaviors in a 3D simulation world (AGISim). The specific experiment focused upon involves teaching Novamente to play the game of “fetch” using reinforcement learning based on repeated partial rewards. Novamente is an integrative AGI architecture involving considerably more than just PLN; however, in this “fetch” experiment, the only cognitive process PLN is coupled with is simple perceptual pattern mining; other Novamente cognitive processes such as evolutionary learning and economic attention allocation are not utilized, so as to allow the study and demonstration of the power of PLN on its own.
---
paper_title: Populating VBS2 with Realistic Virtual Actors

paper_content:

Synthetic environments, particularly those that are built upon video games engines, offer impressive photo-realistic rendering of 3D environments. In parallel, human behaviour representations are becoming increasingly rich, building upon results from the cognitive and affective sciences. This paper reports on the integration of the cognitive architecture, CoJACK™ with the 3D training environment, VBS2™. CoJACK is a BDI (Beliefs/Desire/ Intentions) cognitive architecture that has a tightly integrated moderator layer, supporting the investigation of the effects of affect on decision making. A suicide bomber scenario is described in which "fear" and "morale" moderators significantly influence the behaviour of the virtual actors. The resulting behaviour is compellingly realistic and human observers readily attribute and believe in an account of the motivational underpinnings of the actors' actions. The integration of CoJACK with VBS2 offers the prospect of much richer training environments where virtual actors behave in a variable though repeatable fashion, providing trainees with a truly challenging situation.
---
paper_title: CoJACK: A High-Level Cognitive Architecture with Demonstrations of Moderators, Variability, and Implications for Situation Awareness

paper_content:

Abstract We report a high-level architecture, CoJACK, that provides insights on behavior variability, situation awareness, and behavioral moderators. CoJACK combines Beliefs/Desires/Intentions (BDI) agents’ high-level knowledge representation and usability with several aspects of low-level cognitive architectures, including processing time predictions, errors, and traceability. CoJACK explores new areas for cognitive architectures, such as variability arising from moderators. It also allows aspects of situation awareness (SA) in a cognitive architecture to be explored. Its behavior and the effects of moderators on behavior are demonstrated in a simple adversarial environment. It provides lessons for other architectures including how to define, measure, and control variability due to individual and temporal aspects of cognition; the importance of SA and knowledge representations necessary to support complex SA; the potential for parameter sweeps and paths as measures of variability; and some of the complexities that will arise when aspects of moderators and SA are added to cognitive architectures.
---
paper_title: This time with feeling: Integrated model of trait and state effects on cognition and behavior

paper_content:

Both stable personality characteristics (traits) and transient emotions (states) influence cognition and behavior. In this paper, we describe a methodology for modeling these effects in terms of a set of parameters that control processing within a symbolic cognitive architecture. The underlying thesis of the approach is that the combined effects of these individual differences can be modeled by varying the architecture parameters that control both processing and the structure of knowledge within the architecture modules. We describe the architecture, provide operational definitions of representative trait and state influences in terms of the controlling parameters, and demonstrate how observed trait/state phenomena are modeled in the context of the current demonstration scenario: a peacekeeping training simulation.
---
paper_title: A psychoanalytically-inspired motivational and emotional system for autonomous agents

paper_content:

When developing autonomous agents, the problem of motivation poses one of the key questions. Following a bionic approach inspired by psychoanalysis, a multi-level motivational system to enable autonomous agents to pursue their own agenda and cope with their internal and external environment is presented. Considering embodiment, both homeostatic (extrinsic) and non-homeostatic (intrinsic) internal motivations are modeled. In particular, drives, which are represented on multiple levels, are modeled as the motivational element for agents' actions. Representing an additional level of motivations, emotions are integrated as a co-determinant of decision making.
---

Title: A Review of 40 Years of Cognitive Architecture Research: Focus on Perception, Attention, Learning and Applications

Section 1: Introduction
Description 1: Provide an overview of the past 40 years of research in cognitive architectures, focusing on perception, attention, learning, and practical applications.

Section 2: What are Cognitive Architectures?
Description 2: Define cognitive architectures and discuss their ultimate goal, the different ways artificial intelligence may be realized, and historical perspectives.

Section 3: Taxonomies of Cognitive Architectures
Description 3: Discuss different ways cognitive architectures are categorized, including symbolic, emergent, and hybrid models, and their information processing types.

Section 4: Perception
Description 4: Explore the role of perception in cognitive architectures, detailing various sensory modalities and their implementations in intelligent systems.

Section 5: Vision
Description 5: Detail the implementation of vision as a sensory modality within cognitive architectures, highlighting various approaches and applications.

Section 6: Audition
Description 6: Investigate implementations of auditory perception in cognitive architectures and its typical applications.

Section 7: Symbolic Input
Description 7: Examine the use of symbolic input methods in cognitive architectures, including text commands and graphical user interfaces.

Section 8: Attention
Description 8: Outline the mechanisms of attention in cognitive architectures, differentiating between external (perceptual) and internal attention mechanisms.

Section 9: External Attention
Description 9: Discuss various implementations of external attention mechanisms depending on sensory modalities and their applications.

Section 10: Internal Attention
Description 10: Explore internal attention mechanisms that select appropriate actions from a set of possibilities and manage goal prioritization.

Section 11: Memory
Description 11: Present diverse memory systems within cognitive architectures, differentiating between short-term and long-term memory, and their specific types.

Section 12: Short-term Memory
Description 12: Discuss the implementation and role of short-term memory as a buffer for incoming sensory data in cognitive architectures.

Section 13: Working Memory
Description 13: Examine the dynamic storage of information related to current tasks within working memory, including capacity limitations and management.

Section 14: Long-term Memory
Description 14: Outline the structures and functionalities of long-term memory in cognitive architectures, distinguishing between procedural, semantic, and episodic memory.

Section 15: Learning
Description 15: Detail different types of learning within cognitive architectures, including perceptual, declarative, procedural, and attentional learning.

Section 16: Perceptual Learning
Description 16: Explore how cognitive architectures improve skills in processing perceptual data and finding patterns in sensory inputs.

Section 17: Declarative Learning
Description 17: Discuss the acquisition of explicit knowledge about the world and the relationships between facts in cognitive architectures.

Section 18: Procedural Learning
Description 18: Investigate how cognitive architectures acquire new behaviors or modify existing ones through procedural learning.

Section 19: Attentional Learning
Description 19: Examine how cognitive architectures adjust action selection strategies based on experience, reinforcement learning, and emotions.

Section 20: Applications of Cognitive Architectures
Description 20: Present various applications of cognitive architectures, categorizing them into human performance modeling, robotics, NLP, games and puzzles, and more.

Section 21: Human Performance Modeling (HPM)
Description 21: Detail applications of cognitive architectures in building quantitative models of human performance in specific task environments.

Section 22: Human-Robot Interaction (HRI)
Description 22: Discuss the multidisciplinary field of HRI and how cognitive architectures facilitate communication between humans and robots.

Section 23: Natural Language Processing (NLP)
Description 23: Explore applications in understanding and processing written or spoken language within cognitive architectures.

Section 24: Categorization and Clustering
Description 24: Examine how cognitive architectures apply categorization, pattern recognition, and clustering to process sensory data.

Section 25: Computer Vision
Description 25: Investigate applications of cognitive architectures in solving computer vision problems and enhancing visual processing.

Section 26: Games and Puzzles
Description 26: Present applications of cognitive architectures in playing board games, video games, and solving conceptual puzzles.

Section 27: Psychological Experiments
Description 27: Discuss how cognitive architectures replicate psychological, fMRI, and EEG experiments to model human data.

Section 28: Robotics
Description 28: Examine the numerous applications of cognitive architectures in robotics, focusing on navigation, obstacle avoidance, and object manipulation.

Section 29: Virtual Agents
Description 29: Discuss the use of simulations and virtual reality as alternatives to physical embodiment in cognitive architecture applications.

Section 30: Discussion
Description 30: Summarize the main contributions, trends, and future directions in cognitive architecture research, focusing on implemented aspects of human cognition and practical applications.