How to advance general game playing artificial intelligence by player modelling

7 pagesGeneral game playing artificial intelligence has recently seen important advances due to the various techniques known as 'deep learning'. However the advances conceal equally important limitations in their reliance on: massive data sets; fortuitously constructed problems; and absence of any human-level complexity, including other human opponents. On the other hand, deep learning systems which do beat human champions, such as in Go, do not generalise well. The power of deep learning simultaneously exposes its weakness. Given that deep learning is mostly clever reconfigurations of well-established methods, moving beyond the state of art calls for forward-thinking visionary solutions, not just more of the same. I present the argument that general game playing artificial intelligence will require a generalised player model. This is because games are inherently human artefacts which therefore, as a class of problems, contain cases which require a human-style problem solving approach. I relate this argument to the performance of state of art general game playing agents. I then describe a concept for a formal category theoretic basis to a generalised player model. This formal model approach integrates my existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.Non peer reviewe

Studying the integrated functional cognitive basis of sustained attention with a Primed Subjective-Illusory-Contour Attention Task

Sustained attention plays an important role in everyday life, for work, learning, or when affected by attention disorders. Studies of the neural correlates of attention commonly treat sustained attention as an isolated construct, measured with computerized continuous performance tests. However, in any ecological context, sustained attention interacts with other executive functions and depends on lower level perceptual processing. Such interactions occur, for example, in inhibition of interference, and processing of complex hierarchical stimuli; both of which are important for successful ecological attention. Motivated by the need for more studies on neural correlates of higher cognition, I present an experiment to investigate these interactions of attention in 17 healthy participants measured with high-resolution electroencephalography. Participants perform a novel 2-alternative forced-choice computerised performance test, the Primed Subjective Illusory Contour Attention Task (PSICAT), which presents gestalt-stimuli targets with distractor primes to induce interference inhibition during complex-percept processing. Using behavioural and brain-imaging analyses, I demonstrate the novel result that task-irrelevant incongruency can evoke stronger behavioural and neural responses than the task-relevant stimulus condition; a potentially important finding in attention disorder research. PSICAT is available as an open-source code repository at the following url, allowing researchers to reuse and adapt it to their requirements.Peer reviewe

Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features

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 play styles/preferences, and may also facilitate the design of systems that detect 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 different 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 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.Peer reviewe

Green My Place : Evaluation of a Serious Social Online Game Designed to Promote Energy Efficient Behaviour Change

Serious games are interventions with potential for tackling pressing issues by raising awareness and inciting behaviour change. However, it is unclear which design choices maximise efficient production or intervention efficacy. For example, health games and games tackling social crises may have radically different audiences. Furthermore, players of serious games don’t self-select like audiences for entertainment games, suggesting a need to examine and discuss the outcomes of any and all serious games built upon clear design principles for clearly-defined scenarios. This paper presents a case study of Green My Place, a series game promoting energyefficiency. GMP deployed unique site-specific metrics distinguishing it from similar projects ‘disembodied’ from the environments they are intended to affect. The game’s design methodology – an MMOG framework with atomic mini-games linked to specific learning materials – offers a scaleable generic solution applicable to any domain entailing awareness/education. Field study evaluations show (weak) positive evidence of a positive impact, but lack of traction hindered success. We examine these outcomes and their possible causes, concluding that although the game itself was a noble failure, the evidence suggests that successful behavioural influence may be independent of degree of engagement – a finding with potential significance for any game with learning objectives.Peer reviewe

Computational Testing for Automated Preprocessing 2 : Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG

Existing tools for the preprocessing of EEG data provide a large choice of methods to suitably prepare and analyse a given dataset. Yet it remains a challenge for the average user to integrate methods for batch processing of the increasingly large datasets of modern research, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g., the classification of artifacts in channels, epochs or segments. This introduces extra subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularize EEG preprocessing, and thus reduce human effort, subjectivity, and consequent error. The Computational Testing for Automated Preprocessing (CTAP) toolbox facilitates: (i) batch processing that is easy for experts and novices alike; (ii) testing and comparison of preprocessing methods. Here we demonstrate the application of CTAP to high-resolution EEG data in three modes of use. First, a linear processing pipeline with mostly default parameters illustrates ease-of-use for naive users. Second, a branching pipeline illustrates CTAP's support for comparison of competing methods. Third, a pipeline with built-in parameter-sweeping illustrates CTAP's capability to support data-driven method parameterization. CTAP extends the existing functions and data structure from the well-known EEGLAB toolbox, based on Matlab, and produces extensive quality control outputs. CTAP is available under MIT open-source licence from https://github.com/bwrc/ctap.Peer reviewe

Adaptive Artificial Intelligence in Games : Issues, Requirements, and a Solution through Behavlets-based General Player Modelling

8 pages, 1 figureWe present the last of a series of three academic essays which deal with the question of how and why to build a generalized player model. We propose that a general player model needs parameters for subjective experience of play, including: player psychology, game structure, and actions of play. Based on this proposition, we pose three linked research questions: RQ1 what is a necessary and sufficient foundation to a general player model?; RQ2 can such a foundation improve performance of a computational intelligence- based player model?; and RQ3 can such a player model improve efficacy of adaptive artificial intelligence in games? We set out the arguments behind these research questions in each of the three essays, presented as three preprints. The third essay, in this preprint, presents the argument that adaptive game artificial intelligence will be enhanced by a generalised player model. This is because games are inherently human artefacts which therefore, require some encoding of the human perspective in order to effectively autonomously respond to the individual player. The player model informs the necessary constraints on the adaptive artificial intelligence. A generalised player model is not only more efficient than a per-game solution, but also allows comparison between games which makes it a useful tool for studying play in general. We describe the concept and meaning of an adaptive game. We propose requirements for functional adaptive AI, arguing from first principles drawn from the games research literature. We propose solutions to these requirements, based on a formal model approach to our existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., & Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.Non peer reviewe

Computational Testing for Automated Preprocessing : a Matlab toolbox to enable large scale electroencephalography data processing

Electroencephalography (EEG) is a rich source of information regarding brain function. However, the preprocessing of EEG data can be quite complicated, due to several factors. For example, the distinction between true neural sources and noise is indeterminate; EEG data can also be very large. The various factors create a large number of subjective decisions with consequent risk of compound error. Existing tools present the experimenter with a large choice of analysis methods. Yet it remains a challenge for the researcher to integrate methods for batch-processing of the average large datasets, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g. the classification of artefacts in channels, epochs or segments. This introduces extra subjectivity, is slow and is not reproducible. Batching and well-designed automation can help to regularise EEG preprocessing, and thus reduce human effort, subjectivity and consequent error. We present the computational testing for automated preprocessing (CTAP) toolbox, to facilitate: (i) batch-processing that is easy for experts and novices alike; (ii) testing and manual comparison of preprocessing methods. CTAP extends the existing data structure and functions from the well-known EEGLAB toolbox, based on Matlab and produces extensive quality control outputs. CTAP is available under MIT licence from https://github.com/bwrc/ctap.Peer reviewe

Reduced Power in Fronto-Parietal Theta EEG Linked to Impaired Attention-Sampling in Adult ADHD

Attention-deficit/hyperactivity disorder (ADHD) in adults is understudied, especially regarding neural mechanisms such as oscillatory control of attention sampling. We report an electroencephalography (EEG) study of such cortical mechanisms, in ADHD-diagnosed adults during administration of Test of Variables of Attention (TOVA), a gold-standard continuous performance test for ADHD that measures the ability to sustain attention and inhibit impulsivity. We recorded 53 adults (28 female, 25 male, aged 18-60), and 18 matched healthy controls, using 128-channel EEG. We analyzed sensor-space features established as neural correlates of attention: timing-sensitivity and phase-synchrony of response activations, and event-related (de)synchronization (ERS/D) of alpha and theta frequency band activity; in frontal and parietal scalp regions. TOVA test performance significantly distinguished ADHD adults from neurotypical controls, in commission errors, response time variability (RTV) and d' (response sensitivity). The ADHD group showed significantly weaker target-locked and responselocked amplitudes, that were strongly right-lateralized at the N2 wave, and weaker phase synchrony (longer reset poststimulus). They also manifested significantly less parietal prestimulus 8-Hz theta ERS, less frontal and parietal poststimulus 4-Hz theta ERS, and more frontal and parietal prestimulus alpha ERS during correct trials. These differences may reflect excessive modulation of endogenous activity by strong entrainment to stimulus (alpha), combined with deficient modulation by neural entrainment to task (theta), which in TOVA involves monitoring stimulus spatial location (not predicted occurrence onset which is regular and task-irrelevant). Building on the hypotheses of theta coding for relational structure and rhythmic attention sampling, our results suggest that ADHD adults have impaired attention sampling in relational categorization tasks.Peer reviewe