Short Literature Review for a General Player Model Based on Behavlets
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paper_title: Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features

paper_content:

As player demographics broaden it has become important to understand variation in player types. Improved player models can help game designers create games that accommodate a range of playing styles, and may also facilitate the design of systems that detect the currently-expressed player type and adapt dynamically in real-time. Existing approaches can model players, but most focus on tracking and classifying behaviour based on simple functional metrics such as deaths, specific choices, player avatar attributes, and completion times. We describe a novel approach which seeks to leverage expert domain knowledge using a theoretical framework linking behaviour and game design patterns. The aim is to derive features of play from sequences of actions which are intrinsically informative about behaviour--which, because they are directly interpretable with respect to psychological theory of behaviour, we name `Behavlets'. We present the theoretical underpinning of this approach from research areas including psychology, temperament theory, player modelling, and game composition. The Behavlet creation process is described in detail; illustrated using a clone of the well-known game Pac-Man, with data gathered from 100 participants. A workshop-based evaluation study is also presented, where nine game design expert participants were briefed on the Behavlet concepts and requisite models, and then attempted to apply the method to games of the well-known first/third-person shooter genres, exemplified by `Gears of War', (Microsoft). The participants found 139 Behavlet concepts mapping from behavioural preferences of the temperament types, to design patterns of the shooter genre games. We conclude that the Behavlet approach has significant promise, is complementary to existing methods and can improve theoretical validity of player models.
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paper_title: Does an Individual’s Myers-Briggs Type Indicator Preference Influence Task-Oriented Technology Use?

paper_content:

Technology innovators face the challenge of finding representative groups of users to participate in design activities. In some cases, software applications will target an audience of millions, and the characteristics of the vast number of potential users are unclear to the design team. In other cases, a technology is so new that the target market of potential users is not known. The Myers-Briggs Type Indicator (MBTI) measures individual personality preferences on four dimensions and is used by psychologists to explain certain differences in human behavior. The definitions of the MBTI dimensions suggest they could be a factor explaining why individuals take different approaches to using software applications. This study explores whether MBTI preferences affect behavior when individuals perform tasks using three different software applications. We find a person’s MBTI type influences how they organize email and the informational features they rely on when using a decision support system.
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paper_title: What video games have to teach us about learning and literacy

paper_content:

Good computer and video games like System Shock 2, Deus Ex, Pikmin, Rise of Nations, Neverwinter Nights, and Xenosaga: Episode 1 are learning machines. They get themselves learned and learned well, so that they get played long and hard by a great many people. This is how they and their designers survive and perpetuate themselves. If a game cannot be learned and even mastered at a certain level, it won't get played by enough people, and the company that makes it will go broke. Good learning in games is a capitalist-driven Darwinian process of selection of the fittest. Of course, game designers could have solved their learning problems by making games shorter and easier, by dumbing them down, so to speak. But most gamers don't want short and easy games. Thus, designers face and largely solve an intriguing educational dilemma, one also faced by schools and workplaces: how to get people, often young people, to learn and master something that is long and challenging--and enjoy it, to boot.
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paper_title: Reinterpreting the Myers-Briggs Type Indicator From the Perspective of the Five-Factor Model of Personality

paper_content:

ABSTRACT The Myers-Briggs Type Indicator (MBTI, Myers & McCaulley, 1985) was evaluated from the perspectives of Jung's theory of psychological types and the five-factor model of personality as measured by self-reports and peer ratings on the NEO Personality Inventory (NEO-PI, Costa & McCrae, 1985b) Data were provided by 267 men and 201 women ages 19 to 93 Consistent with earlier research and evaluations, there was no support for the view that the MBTI measures truly dichotomous preferences or qualitatively distinct types, instead, the instrument measures four relatively independent dimensions The interpretation of the Judging-Perceiving index was also called into question The data suggest that Jung's theory is either incorrect or inadequately operationalized by the MBTI and cannot provide a sound basis for interpreting it However, correlational analyses showed that the four MBTI indices did measure aspects of four of the five major dimensions of normal personality The five-factor model provides an alternative basis for interpreting MBTI findings within a broader, more commonly shared conceptual framework
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paper_title: Player Typology in Theory and Practice

paper_content:

Player satisfaction modeling depends in part upon quantitative or qualitative typologies of playing preferences, although such approaches require scrutiny. Examination of psychometric typologies reveal that type theories have—except in rare cases—proven inadequate and have made way for alternative trait theories. This suggests any future player typology that will be sufficiently robust will need foundations in the form of a trait theory of playing preferences. This paper tracks the development of a sequence of player typologies developing from psychometric type theory roots towards an independently validated trait theory of play, albeit one yet to be fully developed. Statistical analysis of the results of one survey in this lineage is presented, along with a discussion of theoretical and practical ways in which the surveys and their implied typological instruments have evolved.
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paper_title: People Efficiently Explore the Solution Space of the Computationally Intractable Traveling Salesman Problem to Find Near-Optimal Tours

paper_content:

Humans need to solve computationally intractable problems such as visual search, categorization, and simultaneous learning and acting, yet an increasing body of evidence suggests that their solutions to instantiations of these problems are near optimal. Computational complexity advances an explanation to this apparent paradox: (1) only a small portion of instances of such problems are actually hard, and (2) successful heuristics exploit structural properties of the typical instance to selectively improve parts that are likely to be sub-optimal. We hypothesize that these two ideas largely account for the good performance of humans on computationally hard problems. We tested part of this hypothesis by studying the solutions of 28 participants to 28 instances of the Euclidean Traveling Salesman Problem (TSP). Participants were provided feedback on the cost of their solutions and were allowed unlimited solution attempts (trials). We found a significant improvement between the first and last trials and that solutions are significantly different from random tours that follow the convex hull and do not have self-crossings. More importantly, we found that participants modified their current better solutions in such a way that edges belonging to the optimal solution ("good" edges) were significantly more likely to stay than other edges ("bad" edges), a hallmark of structural exploitation. We found, however, that more trials harmed the participants' ability to tell good from bad edges, suggesting that after too many trials the participants "ran out of ideas." In sum, we provide the first demonstration of significant performance improvement on the TSP under repetition and feedback and evidence that human problem-solving may exploit the structure of hard problems paralleling behavior of state-of-the-art heuristics.
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paper_title: An Introduction to the Five-Factor Model and Its Applications

paper_content:

ABSTRACT The five-factor model of personality is a hierarchical organization of personality traits in terms of five basic dimensions: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience. Research using both natural language adjectives and theoretically based personality questionnaires supports the comprehensiveness of the model and its applicability across observers and cultures. This article summarizes the history of the model and its supporting evidence; discusses conceptions of the nature of the factors; and outlines an agenda for theorizing about the origins and operation of the factors. We argue that the model should prove useful both for individual assessment and for the elucidation of a number of topics of interest to personality psychologists.
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paper_title: Challenges for Game Designers

paper_content:

Welcome to a book written to challenge you, improve your brainstorming abilities, and sharpen your game design skills! Challenges for Game Designers: Non-Digital Exercises for Video Game Designers is filled with enjoyable, interesting, and challenging exercises to help you become a better video game designer, whether you are a professional or aspire to be. Each chapter covers a different topic important to game designers, and was taken from actual industry experience. After a brief overview of the topic, there are five challenges that each take less than two hours and allow you to apply the material, explore the topic, and expand your knowledge in that area. Each chapter also includes 10 non-digital shorts to further hone your skills. None of the challenges in the book require any programming or a computer, but many of the topics feature challenges that can be made into fully functioning games. The book is useful for professional designers, aspiring designers, and instructors who teach game design courses, and the challenges are great for both practice and homework assignments. The book can be worked through chapter by chapter, or you can skip around and do only the challenges that interest you. As with anything else, making great games takes practice and Challenges for Game Designers provides you with a collection of fun, thoughtprovoking, and of course, challenging activities that will help you hone vital skills and become the best game designer you can be.
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paper_title: Towards an Ontological Language for Game Analysis

paper_content:

The Game Ontology Project (GOP) is creating a framework for describing, analyzing and studying games, by defining a hierarchy of concepts abstracted from an analysis of many specific games. GOP borrows concepts and methods from prototype theory as well as grounded theory to achieve a framework that is always growing and changing as ne w games are analyzed or particular research questions are explored. The top level of the ontolo gy (interface, rules, goals, entities, and entity manipulation) is described as well as a part icular ontological entry. Finally, by engaging in three short discussions centered on relevant games studies research questions, the ontology’s utility is demonstrated.
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paper_title: Uncertainty in Games

paper_content:

In life, uncertainty surrounds us. Things that we thought were good for us turn out to be bad for us (and vice versa); people we thought we knew well behave in mysterious ways; the stock market takes a nosedive. Thanks to an inexplicable optimism, most of the time we are fairly cheerful about it all. But we do devote much effort to managing and ameliorating uncertainty. Is it any wonder, then, asks Greg Costikyan, that we have taken this aspect of our lives and transformed it culturally, making a series of elaborate constructs that subject us to uncertainty but in a fictive and nonthreatening way? That is: we create games. In this concise and entertaining book, Costikyan, an award-winning game designer, argues that games require uncertainty to hold our interest, and that the struggle to master uncertainty is central to their appeal. Game designers, he suggests, can harness the idea of uncertainty to guide their work. Costikyan explores the many sources of uncertainty in many sorts of games -- from Super Mario Bros. to Rock/Paper/Scissors, from Monopoly to CityVille, from FPS Deathmatch play to Chess. He describes types of uncertainty, including performative uncertainty, analytic complexity, and narrative anticipation. And he suggest ways that game designers who want to craft novel game experiences can use an understanding of game uncertainty in its many forms to improve their designs.
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paper_title: Formal Models And Game Design

paper_content:

A baseband switching device for switching between first and second redundant channels is provided with the capability of adjusting the phase and polarity of the data signals in each channel to be equal so that data will continue without interruption when switching from one channel to another.
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paper_title: Rules of Play: Game Design Fundamentals

paper_content:

This text offers an introduction to game design and a unified model for looking at all kinds of games, from board games and sports to computer and video games. Also included are concepts, strategies, and methodologies for creating and understanding games.
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paper_title: Patterns In Game Design

paper_content:

PART I BACKGROUND 1 Introduction 2 An Activity-Based Framework for Describing Games 3 Game Design Patterns PART II THE PATTERN 4 Using Design Patterns 5 Game Design Patterns for Game Elements 6 Game Design Patterns for Resource and Resource Management 7 Game Design Patterns for Information, Communication, and Presentation 8 Actions and Events Patterns 9 Game Design Patterns for Narrative Structures, Predictability, and Immersion Patterns 10 Game Design Patterns for Social Interaction 11 Game Design Patterns for Goals 12 Game Design Patterns for Goal Structures 13 Game Design Patterns for Game Sessions 14 Game Design Patterns for Game Mastery and Balancing 15 Game Design Patterns for Meta Games, Replayability, and Learning Curves
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paper_title: Intelligent Modeling of the User in Interactive Entertainment

paper_content:

A theme of the symposium is to explore ways to employ AI to make games more appealing to people who do not enjoy current genres, and to expand the market for interactive entertainment beyond the traditional niche of young male players. We suggest that AI techniques employed in the world of intelligent tutoring to model the user and adjust instruction, help and content could be fruitfully adapted to interactive entertainment. In computer-based educational tutoring, adaptivity to user behaviors and characteristics such as gender and cognitive developmental level have been shown to increase learner motivation, engagement and achievement in the area of mathematics learning. Similarly, utilizing data regarding player behaviors such as latency and errors to construct a model of the player would allow for more adaptive game play, which in turn would increase the appeal of computer games to a wider audience.
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paper_title: Integrating Domain Experts in Educational Game Authoring: A Case Study

paper_content:

Authoring educational games introduces difficult problems because it is the product of multidisciplinary work, integrating very different experts with different backgrounds that use different terminology. In this paper we discuses how a team composed of computer science experts, an education expert and two medical experts successfully tacked the problem of designing and implementing an educational video game. An approach consisting of different tools and strategies was used to ensure educational value, correctness and completeness of the knowledge represented in the game. The game's goal is to teach basic medical first response procedures to young students (12-15 year old) by using photo realistic representations of the situations and videos with correct realization of the procedures. The game was successfully completed and is currently available online and being tested with real students.
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paper_title: An Investigation of Gamification Typologies for Enhancing Learner Motivation

paper_content:

In this paper we present a new gamified learning system called Reflex which builds on our previous research, placing greater emphasis on variation in learner motivation and associated behaviour, having a particular focus on gamification typologies. Reflex comprises a browser based 3D virtual world that embeds both learning content and learner feedback. In this way the topography of the virtual world plays an important part in the presentation and access to learning material and learner feedback. Reflex presents information to learners based on their curriculum learning objectives and tracks their movement and interactions within the world. A core aspect of Reflex is its gamification design, with our engagement elements and processes based on Marczewski's eight gamification types [1]. We describe his model and its relationship to Bartle's player types [2] as well as the RAMP intrinsic motivation model [3]. We go on to present an analysis of experiments using Reflex with students on two 2nd year Computing modules. Our data mining and cluster analysis on the results of a gamification typology questionnaire expose variation in learner motivation. The results from a comprehensive tracking of the interactions of learners within Reflex are discussed and the acquired tracking data is discussed in context of gamification typologies and metacognitive tendencies of the learners. We discuss correlations in actual learner behaviour to that predicted by gamified learner profile. Our results illustrate the importance of taking variation in learner motivation into account when designing gamified learning systems.
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paper_title: User-system-experience model for user centered design in computer games

paper_content:

This paper details the central ideas to date, from a PhD entitled ‘Player Profiling for Adaptive Artificial Intelligence in Computer and Video Games’. Computer and videogames differ from other web and productivity software in that games are much more highly interactive and immersive experiences. Whereas usability and user modelling for other software may be based on productivity alone, games require an additional factor that takes account of the quality of the user experience in playing a game. In order to describe that experience we describe a model of User, System and Experience (USE) in which the primary construct for evaluation of a player’s experience will be the Experience Fluctuation Model (EFM), taken from Flow theory. We illustrate with a straightforward example how this system may be automated in real-time within a commercial game.
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paper_title: Compositional abstractions of hybrid control systems

paper_content:

Abstraction is a natural way to hierarchically decompose the analysis and design of hybrid systems. Given a hybrid control system and some desired properties, one extracts an abstracted system while preserving the properties of interest. Abstractions of purely discrete systems is a mature area, whereas abstractions of continuous systems is a recent activity. We present a framework for abstraction that applies to abstract control systems capturing discrete, continuous, and hybrid systems. Parallel composition is presented in a categorical framework and an algorithm is proposed to construct abstractions of hybrid control systems. Finally, we show that our abstractions of hybrid systems are compositional.
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paper_title: Theory of Games and Economic Behavior

paper_content:

This is the classic work upon which modern-day game theory is based. What began more than sixty years ago as a modest proposal that a mathematician and an economist write a short paper together blossomed, in 1944, when Princeton University Press published "Theory of Games and Economic Behavior." In it, John von Neumann and Oskar Morgenstern conceived a groundbreaking mathematical theory of economic and social organization, based on a theory of games of strategy. Not only would this revolutionize economics, but the entirely new field of scientific inquiry it yielded--game theory--has since been widely used to analyze a host of real-world phenomena from arms races to optimal policy choices of presidential candidates, from vaccination policy to major league baseball salary negotiations. And it is today established throughout both the social sciences and a wide range of other sciences.
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paper_title: Formal Models And Game Design

paper_content:

A baseband switching device for switching between first and second redundant channels is provided with the capability of adjusting the phase and polarity of the data signals in each channel to be equal so that data will continue without interruption when switching from one channel to another.
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paper_title: Machine learning in digital games: a survey

paper_content:

Artificial intelligence for digital games constitutes the implementation of a set of algorithms and techniques from both traditional and modern artificial intelligence in order to provide solutions to a range of game dependent problems. However, the majority of current approaches lead to predefined, static and predictable game agent responses, with no ability to adjust during game-play to the behaviour or playing style of the player. Machine learning techniques provide a way to improve the behavioural dynamics of computer controlled game agents by facilitating the automated generation and selection of behaviours, thus enhancing the capabilities of digital game artificial intelligence and providing the opportunity to create more engaging and entertaining game-play experiences. This paper provides a survey of the current state of academic machine learning research for digital game environments, with respect to the use of techniques from neural networks, evolutionary computation and reinforcement learning for game agent control.
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paper_title: Learning principles and interaction design for ‘Green My Place’: A massively multiplayer serious game

paper_content:

The usual approach to serious game design is to construct a single game intended to address the specific domain problem being addressed. This paper describes a novel alternative approach, focussed on embedding smaller game elements into a comprehensive framework, which provides stronger motive for play and thus greater chance of effect. This serious game design methodology was developed for an EU project to teach energy efficient knowledge and behaviour to users of public buildings around Europe. The successful implementation of this game is also described. The cutting-edge educational principles that formed the basis for the design are drawn from recent research in serious games and energy efficiency, and include the Behavlet, a novel behaviour-transformation concept developed by the authors. The game design framework presented illustrates a clear approach for serious games dealing with topics applicable at societal scales.
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paper_title: Player modeling using self-organization in Tomb Raider: Underworld

paper_content:

We present a study focused on constructing models of players for the major commercial title Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level playing behavior data obtained from 1365 players that completed the TRU game. The unsupervised learning approach utilized reveals four types of players which are analyzed within the context of the game. The proposed approach automates, in part, the traditional user and play testing procedures followed in the game industry since it can inform game developers, in detail, if the players play the game as intended by the game design. Subsequently, player models can assist the tailoring of game mechanics in real-time for the needs of the player type identified.
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paper_title: Human-level control through deep reinforcement learning

paper_content:

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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paper_title: Evolving personas for player decision modeling

paper_content:

This paper explores how evolved game playing agents can be used to represent a priori defined archetypical ways of playing a test-bed game, as procedural personas. The end goal of such procedural personas is substituting players when authoring game content manually, procedurally, or both (in a mixed-initiative setting). Building on previous work, we compare the performance of newly evolved agents to agents trained via Q-learning as well as a number of baseline agents. Comparisons are performed on the grounds of game playing ability, generalizability, and conformity among agents. Finally, all agents' decision making styles are matched to the decision making styles of human players in order to investigate whether the different methods can yield agents who mimic or differ from human decision making in similar ways. The experiments performed in this paper conclude that agents developed from a priori defined objectives can express human decision making styles and that they are more generalizable and versatile than Q-learning and hand-crafted agents.
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paper_title: Some studies in machine learning using the game of checkers

paper_content:

Two machine-learning procedures have been investigated in some detail using the game of checkers. Enough work has been done to verify the fact that a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program. Further-more, it can learn to do this in a remarkably short period of time (8 or 10 hours of machine-playing time) when given only the rules of the game, a sense of direction, and a redundant and incomplete list of parameters which are thought to have something to do with the game, but whose correct signs and relative weights are unknown and unspecified. The principles of machine learning verified by these experiments are, of course, applicable to many other situations.
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paper_title: Mastering the game of Go with deep neural networks and tree search

paper_content:

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
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paper_title: Player-Centred Game Design : Player Modelling and Adaptive Digital Games

paper_content:

We describe an approach to player-centred game design through adaptive game technologies [9]. The work presented is the result of on-going collaborative research between Media and Computing groups at the University of Ulster, and so we begin with a review of related literature from both areas before presenting our new ideas. In particular we focus on three areas of related research: understanding players, modelling players, and adaptive game technology. We argue that player modelling and adaptive technologies may be used alongside existing approaches to facilitate improved player-centred game design in order to provide a more appropriate level of challenge, smooth the learning curve, and enhance the gameplay experience for individual players regardless of gender, age and experience. However, adaptive game behaviour is a controversial topic within game research and development and so while we outline the potential of such technologies, we also address the most significant concerns.
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paper_title: Real-time rule-based classification of player types in computer games

paper_content:

The power of using machine learning to improve or investigate the experience of play is only beginning to be realised. For instance, the experience of play is a psychological phenomenon, yet common psychological concepts such as the typology of temperaments have not been widely utilised in game design or research. An effective player typology provides a model by which we can analyse player behaviour. We present a real-time classifier of player type, implemented in the test-bed game Pac-Man. Decision Tree algorithms CART and C5.0 were trained on labels from the DGD player typology (Bateman and Boon, 21st century game design, vol. 1, 2005). The classifier is then built by selecting rules from the Decision Trees using a rule- performance metric, and experimentally validated. We achieve ~70% accuracy in this validation testing. We further analyse the concept descriptions learned by the Decision Trees. The algorithm output is examined with respect to a set of hypotheses on player behaviour. A set of open questions is then posed against the test data obtained from validation testing, to illustrate the further insights possible from extended analysis.
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paper_title: Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features

paper_content:

As player demographics broaden it has become important to understand variation in player types. Improved player models can help game designers create games that accommodate a range of playing styles, and may also facilitate the design of systems that detect the currently-expressed player type and adapt dynamically in real-time. Existing approaches can model players, but most focus on tracking and classifying behaviour based on simple functional metrics such as deaths, specific choices, player avatar attributes, and completion times. We describe a novel approach which seeks to leverage expert domain knowledge using a theoretical framework linking behaviour and game design patterns. The aim is to derive features of play from sequences of actions which are intrinsically informative about behaviour--which, because they are directly interpretable with respect to psychological theory of behaviour, we name `Behavlets'. We present the theoretical underpinning of this approach from research areas including psychology, temperament theory, player modelling, and game composition. The Behavlet creation process is described in detail; illustrated using a clone of the well-known game Pac-Man, with data gathered from 100 participants. A workshop-based evaluation study is also presented, where nine game design expert participants were briefed on the Behavlet concepts and requisite models, and then attempted to apply the method to games of the well-known first/third-person shooter genres, exemplified by `Gears of War', (Microsoft). The participants found 139 Behavlet concepts mapping from behavioural preferences of the temperament types, to design patterns of the shooter genre games. We conclude that the Behavlet approach has significant promise, is complementary to existing methods and can improve theoretical validity of player models.
---
paper_title: Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty

paper_content:

This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions assessed from physiological signals. The validity of this approach was first tested by analyzing the questionnaire responses, electroencephalogram (EEG) signals, and peripheral signals of the players playing a Tetris game at three difficulty levels. This analysis confirms that the different difficulty levels correspond to distinguishable emotions, and that, playing several times at the same difficulty level gives rise to boredom. The next step was to train several classifiers to automatically detect the three emotional classes from EEG and peripheral signals in a player-independent framework. By using either type of signals, the emotional classes were successfully recovered, with EEG having a better accuracy than peripheral signals on short periods of time. After the fusion of the two signal categories, the accuracy raised up to 63%.
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paper_title: Experience Assessment and Design in the Analysis of Gameplay

paper_content:

We report research on player modeling using psychophysiology and machine learning, conducted through interdisciplinary collaboration between researchers of computer science, psychology, and game design at Aalto University, Helsinki. First, we propose the Play Patterns And eXperience (PPAX) framework to connect three levels of game experience that previously had remained largely unconnected: game design patterns, the interplay of game context with player personality or tendencies, and state-of-the-art measures of experience (both subjective and non-subjective). Second, we describe our methodology for using machine learning to categorize game events to reveal corresponding patterns, culminating in an example experiment. We discuss the relation between automatically detected event clusters and game design patterns, and provide indications on how to incorporate personality profiles of players in the analysis. This novel interdisciplinary collaboration combines basic psychophysiology research with game design patterns and machine learning, and generates new knowledge about the interplay between game experience and design.
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Title: Short Literature Review for a General Player Model Based on Behavlets

Section 1: INTRODUCTION
Description 1: This section should provide an overview of the importance of understanding and modelling players, define the term "player model," and discuss the foundational areas (psychology of behaviour, language of general game design, and model of actions) as well as the research questions addressed in the review.

Section 2: LITERATURE REVIEW
Description 2: This section should summarize previous work relevant to player modelling, including game decomposition, player psychology, combined approaches for play experience frameworks, and recent player models.

Section 3: Personality and Play
Description 3: This subsection should focus on the influence of player personality on gameplay and discuss various models and theories such as temperament theory, Myers-Briggs Type Indicator, and Demographic Game Design (DGD) player typologies.

Section 4: Game Structure
Description 4: This subsection should describe methods for decomposing games into constituent parts, including various perspectives (mechanistic, dynamic, aesthetic) and standards (e.g., MDA model, Rules schema, game design patterns).

Section 5: Play Experience Frameworks
Description 5: This subsection should cover frameworks that combine game decomposition and player psychology models, highlighting various approaches and theories proposed for understanding the player experience.

Section 6: Player Modelling
Description 6: This subsection should discuss various computational intelligence methods for player modelling, illustrating the challenges and methodologies used for feature extraction and defining different player attributes.

Section 7: DISCUSSION
Description 7: This section should synthesize the information reviewed and discuss the rationale for a generalized player model. It should also propose future work, including systematic reviews and meta-analyses to validate and expand upon the findings.

Section 8: CONCLUSION
Description 8: This section should summarize the key points from the review, providing a snapshot of the current state of the art in player modelling and outlining the proposed strategy for validating and expanding these models through systematic review and meta-analysis.