The Action Execution Process Implemented in Different Cognitive Architectures: A Review
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paper_title: A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures

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A number of leading cognitive architectures that are inspired by the human brain, at various levels of granularity, are reviewed and compared, with special attention paid to the way their internal structures and dynamics map onto neural processes. Four categories of Biologically Inspired Cognitive Architectures (BICAs) are considered, with multiple examples of each category briefly reviewed, and selected examples discussed in more depth: primarily symbolic architectures (e.g. ACT-R), emergentist architectures (e.g. DeSTIN), developmental robotics architectures (e.g. IM-CLEVER), and our central focus, hybrid architectures (e.g. LIDA, CLARION, 4D/RCS, DUAL, MicroPsi, and OpenCog). Given the state of the art in BICA, it is not yet possible to tell whether emulating the brain on the architectural level is going to be enough to allow rough emulation of brain function; and given the state of the art in neuroscience, it is not yet possible to connect BICAs with large-scale brain simulations in a thoroughgoing way. However, it is nonetheless possible to draw reasonably close function connections between various components of various BICAs and various brain regions and dynamics, and as both BICAs and brain simulations mature, these connections should become richer and may extend further into the domain of internal dynamics as well as overall behavior.
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paper_title: LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning

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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.
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paper_title: Sensory Motor System: Modeling the Process of Action Execution

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Sensory Motor System: Modeling the Process of Action Execution Daqi Dong (DDONG@Memphis.Edu) Stan Franklin (FRANKLIN@Memphis.Edu) Department of Computer Science and the Institute for Intelligent Systems, University of Memphis, Memphis TN 38152, USA Abstract This paper presents a cognitive model—the Sensory Motor System (SMS)—for an action execution process, as a new module of the LIDA systems-level cognitive model. Action execution refers to a situation in which a software agent or robot executes a selected goal-directed action in the real world so as to output pertinent movement. Action execution requires transforming a selected goal-directed action into lower-level executable actions, and executing them. A sensorimotor system derived from the subsumption architecture has been implemented into the SMS; and several cognitive neuroscience hypotheses have been incorporated as well, including the two visual systems and others. A computational SMS has been created inside a LIDA-based software agent in Webots to model the execution of a grip action. The grip’s design is inspired by the arm controller of the robot Herbert and the current study of human’s action. Simulated results are compared to human performance. Keywords: Sensory Motor System (SMS); action execution; LIDA model; subsumption architecture; two visual systems; grip action; robot Herbert; Webots; cognitive modeling. 1. Introduction Action presents two aspects. On the one hand, the agent (Franklin & Graesser, 1997) selects the action motivated from inside as a result of mental processes. Thus, the agent understands what it will do before the execution begins. However, this understanding of the action is not executable in the real world, because the needed low-level environmental information is not yet involved. On the other hand, the action’s execution may not be understandable to the agent, because the environmental elements involved are low-level raw data without explicit meaning, while that which is understandable must have some form of meaning for the agent. As an example, the agent does not directly understand the raw stimulus data retrieved by its sensors from the environment; rather, the data must be transformed into high-level meaning by a perception process; that is, the transformation produces an understandable representation of the sensed data. Action execution performs a transformation similar to that of perception, but in reverse: converts an understandable action into low-level movements. Milner & Goodale have proposed a hypothesis in their work on the two visual systems 1 (1992; 2008), which supports a model for how a human maintains and integrates these two facets of action: the understandable and the In the LIDA Model, the concept of ventral and dorsal streams for the transmission of visual information has been extended to multimodal transmission. executable—in other words, “what to do” and “how to do it”. They proposed two cortical systems, the ventral and dorsal streams, providing “vision for perception” and “vision for action” respectively. Regarding the roles of the two streams in the guidance of action, the perceptual mechanism in the ventral stream identifies a goal object, and helps to select an appropriate course of action, while the dorsal stream “is critical for the detailed specification and online control of the constituent movements that form the action” (Milner & Goodale, 2008, p. 775). Following the hypothesis of the two visual systems, the dual aspects of action are represented in the LIDA Model as the distinct processes of action selection and action execution. Action selection has been described in previous work (Franklin, Madl, D’Mello, & Snaider, 2013); here we specify the action execution in the form of the Sensory Motor System (SMS) to extend LIDA. The SMS responds by transforming a desired understandable action, a selected behavior in LIDA, into an executable low-level action sequence, a sequence of motor commands, and executing them. The next section describes the LIDA Model and its relationship to the SMS. Section 3 contains an overview of the subsumption architecture (Brooks, 1986, 1991), which is used as the SMS’s prototype; and the SMS’s concept is described in Section 4 in detail. Section 5 introduces the simulation of a specific action execution process, gripping. One aspect of human grip performance, the grip aperture, has been compared to the simulated results. We are currently comparing SMS to other models of the action execution process. Also, besides the grip aperture, we plan to simulate other aspects of human grip performance in future, such as the grip force, velocity, etc. 2. The LIDA Model The LIDA model is a systems-level cognitive model (Franklin et al., 2013). It implements and fleshes out a number of psychological and neuropsychological theories, but is primarily based on Global Workspace Theory (Baars, 1988, 2002). The model is grounded in the LIDA cognitive cycle. The simulated human mind can be viewed as functioning via a continual sequence of these cycles. Each cognitive cycle consists of three phases: 1) The LIDA agent first senses the environment, recognizes objects, and builds its understanding of the current situation; 2) By a competitive process, as specified by Global Workspace Theory (Baars, 1988), it then decides what portion of the represented situation should be attended to and broadcasted to the rest of the system; 3) Finally, the broadcasted portion
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paper_title: Cognitive architectures: Research issues and challenges

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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.
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paper_title: The neuroscience of grasping

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Nature Rev. Neurosci. 6, 726–736 (2005) The correct image for panel 7d is shown below.
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paper_title: The cognitive neuroscience of prehension: recent developments

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Prehension, the capacity to reach and grasp, is the key behavior that allows humans to change their environment. It continues to serve as a remarkable experimental test case for probing the cognitive architecture of goal-oriented action. This review focuses on recent experimental evidence that enhances or modifies how we might conceptualize the neural substrates of prehension. Emphasis is placed on studies that consider how precision grasps are selected and transformed into motor commands. Then, the mechanisms that extract action relevant information from vision and touch are considered. These include consideration of how parallel perceptual networks within parietal cortex, along with the ventral stream, are connected and share information to achieve common motor goals. On-line control of grasping action is discussed within a state estimation framework. The review ends with a consideration about how prehension fits within larger action repertoires that solve more complex goals and the possible cortical architectures needed to organize these actions.
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paper_title: How Can the Human Mind Occur in the Physical Universe

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1. Cognitive Architecture 2. The Modular Organization of the Mind 3. Human Associative Memory 4. The Adaptive Control of Thought 5. What Does It Take to Be Human? Lessons From High School Algebra 6. How Can the Human Mind Occur?
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paper_title: An Expectation-based, Multi-focal, Saccadic (EMS) Vision System for Vehicle Guidance

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The ‘MarVEye’ camera configuration realized with three to four conventional miniature CCD-TV-cameras mounted fix relative to each other on a high bandwidth pan-and-tilt platform is discussed. A saccadic type of vision with intermittent phases of fast saccades and smooth pursuit does complicate image sequence interpretation; however, with explicit spatio-temporal representations available in the 4-D approach this can be handled in a straightforward way. The newly developed machine vision system based on COTSPC-hardware and object-oriented programming is presented. Applications in the field of road vehicle guidance are discussed.
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paper_title: RCS: A Cognitive Architecture for Intelligent MultiAgent Systems

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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.
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paper_title: A general dynamic vision architecture for UGV and UAV

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The expectation-based 4D approach to dynamic machine vision exploiting integral spatiotemporal models of objects in the real world is discussed in the application domains of unmanned ground and air vehicles. The method has demonstrated superior performance over the last half decade in autonomous road vehicle guidance with three different vans and busses, with an AGV on the factory floor and with completely autonomous relative state estimation for a twin turboprop aircraft in the landing approach to a runway without any external support; in all application areas only a small set of conventional microcomputers was sufficient for realizing the system. This shows the computational efficiency of the method combining both conventional engineering type algorithms and artificial intelligence components in a well balanced way.
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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.
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paper_title: An integrated theory of the mind

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Adaptive control of thought–rational (ACT–R; J. R. Anderson & C. Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT–R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time, a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. A number of simple and complex empirical examples are described to illustrate how these modules function singly and in concert.
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paper_title: Serial modules in parallel: the psychological refractory period and perfect time-sharing.

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The authors describe ACT-R/perceptual-motor (ACT-R/PM), an integrated theory of cognition, perception, and action that consists of the ACT-R production system and a set of perceptual-motor modules. Each module (including cognition) is essentially serial, but modules run in parallel with one another. ACT-R/PM can model simple dual tasks such as the psychological refractory period (PRP), including subtle results previously explained with executive process interactive control (EPIC, D. E. Meyer & D. E. Kieras, 1997a). The central difference between the theories is that EPIC's productions can fire in parallel, whereas in ACT-R/PM, they are serial. Results from three PRP-like experiments with more demanding cognitive requirements indicate that cognitive processing for the 2 tasks need not overlap. ACT-R's activation-based retrieval processes are critical in accounting for the timing of these tasks and for explaining the dual-task performance decrement.
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paper_title: BECCA: Reintegrating AI for Natural World Interaction

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Natural world interaction (NWI), the pursuit of arbitrary goals in unstructured physical environments, is an excellent motivating problem for the reintegration of artificial intelligence. It is the problem set that humans struggle to solve. At a minimum it entails perception, learning, planning, and control, and can also involve language and social behavior. An agent's fitness in NWI is achieved by being able to perform a wide variety of tasks, rather than being able to excel at one. In an attempt to address NWI, a brain-emulating cognition and control architecture (BECCA) was developed. It uses a combination of feature creation and model-based reinforcement learning to capture structure in the environment in order to maximize reward. BECCA avoids making common assumptions about its world, such as stationarity, determinism, and the Markov assumption. BECCA has been demonstrated performing a set of tasks which is non-trivially broad, including a vision-based robotics task. Current development activity is focused on applying BECCA to the problem of general Search and Retrieve, a representative natural world interaction task.
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paper_title: CERA-CRANIUM: A Test Bed for Machine Consciousness Research

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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.
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paper_title: An Overview of the EPIC Architecture for Cognition and Performance with Application to Human-Computer Interaction

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EPIC (Executive Process-Interactive Control) is a cognitive architecture especially suited for modeling human multimodal and multiple-task performance. The EPIC architecture includes peripheral sensory-motor processors surrounding a production-rule cognitive processor and is being used to construct precise computational models for a variety of human-computer interaction situations. We briefly describe some of these models to demonstrate how EPIC clarifies basic properties of human performance and provides usefully precise accounts of performance speed.
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paper_title: The GLAIR Cognitive Architecture

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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.
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paper_title: A Unified Cognitive Architecture for Physical Agents

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In this paper we describe ICARUS, a cognitive architecture for physical agents that integrates ideas from a number of traditions, but that has been especially influenced by results from cognitive psychology. We review ICARUS' commitments to memories and representations, then present its basic processes for performance and learning. We illustrate the architecture's behavior on a task from in-city driving that requires interaction among its various components. In addition, we discuss ICARUS' consistency with qualitative findings about the nature of human cognition. In closing, we consider the framework's relation to other cognitive architectures that have been proposed in the literature.
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paper_title: Sensory Motor System: Modeling the Process of Action Execution

paper_content:

Sensory Motor System: Modeling the Process of Action Execution Daqi Dong (DDONG@Memphis.Edu) Stan Franklin (FRANKLIN@Memphis.Edu) Department of Computer Science and the Institute for Intelligent Systems, University of Memphis, Memphis TN 38152, USA Abstract This paper presents a cognitive model—the Sensory Motor System (SMS)—for an action execution process, as a new module of the LIDA systems-level cognitive model. Action execution refers to a situation in which a software agent or robot executes a selected goal-directed action in the real world so as to output pertinent movement. Action execution requires transforming a selected goal-directed action into lower-level executable actions, and executing them. A sensorimotor system derived from the subsumption architecture has been implemented into the SMS; and several cognitive neuroscience hypotheses have been incorporated as well, including the two visual systems and others. A computational SMS has been created inside a LIDA-based software agent in Webots to model the execution of a grip action. The grip’s design is inspired by the arm controller of the robot Herbert and the current study of human’s action. Simulated results are compared to human performance. Keywords: Sensory Motor System (SMS); action execution; LIDA model; subsumption architecture; two visual systems; grip action; robot Herbert; Webots; cognitive modeling. 1. Introduction Action presents two aspects. On the one hand, the agent (Franklin & Graesser, 1997) selects the action motivated from inside as a result of mental processes. Thus, the agent understands what it will do before the execution begins. However, this understanding of the action is not executable in the real world, because the needed low-level environmental information is not yet involved. On the other hand, the action’s execution may not be understandable to the agent, because the environmental elements involved are low-level raw data without explicit meaning, while that which is understandable must have some form of meaning for the agent. As an example, the agent does not directly understand the raw stimulus data retrieved by its sensors from the environment; rather, the data must be transformed into high-level meaning by a perception process; that is, the transformation produces an understandable representation of the sensed data. Action execution performs a transformation similar to that of perception, but in reverse: converts an understandable action into low-level movements. Milner & Goodale have proposed a hypothesis in their work on the two visual systems 1 (1992; 2008), which supports a model for how a human maintains and integrates these two facets of action: the understandable and the In the LIDA Model, the concept of ventral and dorsal streams for the transmission of visual information has been extended to multimodal transmission. executable—in other words, “what to do” and “how to do it”. They proposed two cortical systems, the ventral and dorsal streams, providing “vision for perception” and “vision for action” respectively. Regarding the roles of the two streams in the guidance of action, the perceptual mechanism in the ventral stream identifies a goal object, and helps to select an appropriate course of action, while the dorsal stream “is critical for the detailed specification and online control of the constituent movements that form the action” (Milner & Goodale, 2008, p. 775). Following the hypothesis of the two visual systems, the dual aspects of action are represented in the LIDA Model as the distinct processes of action selection and action execution. Action selection has been described in previous work (Franklin, Madl, D’Mello, & Snaider, 2013); here we specify the action execution in the form of the Sensory Motor System (SMS) to extend LIDA. The SMS responds by transforming a desired understandable action, a selected behavior in LIDA, into an executable low-level action sequence, a sequence of motor commands, and executing them. The next section describes the LIDA Model and its relationship to the SMS. Section 3 contains an overview of the subsumption architecture (Brooks, 1986, 1991), which is used as the SMS’s prototype; and the SMS’s concept is described in Section 4 in detail. Section 5 introduces the simulation of a specific action execution process, gripping. One aspect of human grip performance, the grip aperture, has been compared to the simulated results. We are currently comparing SMS to other models of the action execution process. Also, besides the grip aperture, we plan to simulate other aspects of human grip performance in future, such as the grip force, velocity, etc. 2. The LIDA Model The LIDA model is a systems-level cognitive model (Franklin et al., 2013). It implements and fleshes out a number of psychological and neuropsychological theories, but is primarily based on Global Workspace Theory (Baars, 1988, 2002). The model is grounded in the LIDA cognitive cycle. The simulated human mind can be viewed as functioning via a continual sequence of these cycles. Each cognitive cycle consists of three phases: 1) The LIDA agent first senses the environment, recognizes objects, and builds its understanding of the current situation; 2) By a competitive process, as specified by Global Workspace Theory (Baars, 1988), it then decides what portion of the represented situation should be attended to and broadcasted to the rest of the system; 3) Finally, the broadcasted portion
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paper_title: A cognitive theory of consciousness

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List of figures and tables Preface Part I. Introduction: 1. What is to be explained some preliminaries Part II. The Basic Model: 2. Model 1: conscious representations are internally consistent and globally distributed 3. The neural basis of conscious experience Part III. The Fundamental Role of Context: 4. Model 2: unconscious contexts shape conscious experience 5. Model 3: conscious experience is informative - it always demands some degree of adaptation Part IV. Goals and Voluntary Control: 6. Model 4: Goal contexts, spontaneous problem solving, and the stream of consciousness 7. Model 5: volition as ideomotor control of thought and action Part V. Attention, self, and conscious self-monitoring: 8. Model 6: attention as control of access to consciousness 9. Model 7. Self as the dominant context of experience and action Part VI. Consciousness is Functional: 10. The functions of consciousness Part VII. Conclusion: 11. A summary and some future directions Glossary and guide to theoretical claims References Name index, Subject index.
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Title: The Action Execution Process Implemented in Different Cognitive Architectures: A Review

Section 1: Introduction
Description 1: Write an introduction that provides an overview of the significance of action execution in autonomous agents and introduces the main themes and questions addressed in the review.

Section 2: Action Execution Processes of Cognitive Architectures
Description 2: Write an overview of the action execution processes in various cognitive architectures, followed by detailed examination of each architecture’s action execution implementation.

Section 3: 4D/RCS
Description 3: Provide details on the 4D/RCS architecture, including its hierarchical and sensory interaction control processes, along with specific implementation of action execution.

Section 4: ACT-R
Description 4: Discuss the ACT-R architecture, focusing on its memory modules, production rules, and the separation of action preparation and action execution.

Section 5: BECCA
Description 5: Describe the BECCA architecture, highlighting its automatic feature creator, model-based reinforcement learner, and two-step action selection process.

Section 6: CERA-CRANIUM
Description 6: Explain the CERA-CRANIUM architecture, emphasizing its layered structure and modules related to action planners, preprocessors, and reactive processors.

Section 7: CLARION
Description 7: Review the CLARION architecture, detailing its action-centered subsystem, motivational subsystem, and the interaction between explicit and implicit levels.

Section 8: EPIC
Description 8: Detail the EPIC architecture, including its sensory, motor, and cognitive processors, and the preparation and execution of movements.

Section 9: GLAIR
Description 9: Outline the GLAIR architecture, focusing on its layered structure, and the process of transforming high-level commands into actuator-specific instructions.

Section 10: ICARUS
Description 10: Discuss the ICARUS architecture, with emphasis on its conceptual inference, goal selection, and skill execution mechanisms.

Section 11: LIDA
Description 11: Describe the LIDA architecture, based on the LIDA cognitive cycle, sensory motor system, and the dual aspects of action selection and execution.

Section 12: Soar
Description 12: Explain the Soar architecture, particularly its production rules, operator selection processes, and reliance on external programs for final action execution.

Section 13: Conclusions
Description 13: Provide a summary of the commonalities and differences in action execution across cognitive architectures, including key observations and overall implications.

Section 14: Inside Action Execution
Description 14: Write about the typical processes and representations found within the action execution stages in cognitive architectures.

Section 15: The Cooperation between Action Selection and Execution
Description 15: Discuss how action selection and execution processes cooperate within different cognitive architectures.

Section 16: Environmental Information for Action Execution
Description 16: Elaborate on the role of environmental information in specifying and adjusting commands during action execution.