Game Theoretical Interactions of Moving Agents

Game theory has been one of the most successful quantitative concepts to describe social interactions, their strategical aspects, and outcomes. Among the payoff matrix quantifying the result of a social interaction, the interaction conditions have been varied, such as the number of repeated interactions, the number of interaction partners, the possibility to punish defective behavior etc. While an extension to spatial interactions has been considered early on such as in the "game of life", recent studies have focussed on effects of the structure of social interaction networks. However, the possibility of individuals to move and, thereby, evade areas with a high level of defection, and to seek areas with a high level of cooperation, has not been fully explored so far. This contribution presents a model combining game theoretical interactions with success-driven motion in space, and studies the consequences that this may have for the degree of cooperation and the spatio-temporal dynamics in the population. It is demonstrated that the combination of game theoretical interactions with motion gives rise to many self-organized behavioral patterns on an aggregate level, which can explain a variety of empirically observed social behaviors

Using Computational Agents to Design Participatory Social Simulations

In social science, the role of stakeholders is increasing in the development and use of simulation models. Their participation in the design of agent-based models (ABMs) has widely been considered as an efficient solution to the validation of this particular type of model. Traditionally, "agents" (as basic model elements) have not been concerned with stakeholders directly but via designers or role-playing games (RPGs). In this paper, we intend to bridge this gap by introducing computational or software agents, implemented from an initial ABM, into a new kind of RPG, mediated by computers, so that these agents can interact with stakeholders. This interaction can help not only to elicit stakeholders' informal knowledge or unpredicted behaviours, but also to control stakeholders' focus during the games. We therefore formalize a general participatory design method using software agents, and illustrate it by describing our experience in a project aimed at developing agent-based social simulations in the field of air traffic management.Participatory Social Simulations, Agent-Based Social Simulations, Computational Agents, Role-Playing Games, Artificial Maieutics, User-Centered Design

Imitation learning through games: theory, implementation and evaluation

Despite a history of games-based research, academia has generally regarded commercial games as a distraction from the serious business of AI, rather than as an opportunity to leverage this existing domain to the advancement of our knowledge. Similarly, the computer game industry still relies on techniques that were developed several decades ago, and has shown little interest in adopting more progressive academic approaches. In recent times, however, these attitudes have begun to change; under- and post-graduate games development courses are increasingly common, while the industry itself is slowly but surely beginning to recognise the potential offered by modern machine-learning approaches, though games which actually implement said approaches on more than a token scale remain scarce. One area which has not yet received much attention from either academia or industry is imitation learning, which seeks to expedite the learning process by exploiting data harvested from demonstrations of a given task. While substantial work has been done in developing imitation techniques for humanoid robot movement, there has been very little exploration of the challenges posed by interactive computer games. Given that such games generally encode reasoning and decision-making behaviours which are inherently more complex and potentially more interesting than limb motion data, that they often provide inbuilt facilities for recording human play, that the generation and collection of training samples is therefore far easier than in robotics, and that many games have vast pre-existing libraries of these recorded demonstrations, it is fair to say that computer games represent an extremely fertile domain for imitation learning research. In this thesis, we argue in favour of using modern, commercial computer games to study, model and reproduce humanlike behaviour. We provide an overview of the biological and robotic imitation literature as well as the current status of game AI, highlighting techniques which may be adapted for the purposes of game-based imitation. We then proceed to describe our contributions to the field of imitation learning itself, which encompass three distinct categories: theory, implementation and evaluation. We first describe the development of a fully-featured Java API - the Quake2 Agent Simulation Environment (QASE) - designed to facilitate both research and education in imitation and general machine-learning, using the game Quake 2 as a testbed. We outline our motivation for developing QASE, discussing the shortcomings of existing APIs and the steps which we have taken to circumvent them. We describe QASE’s network layer, which acts as an interface between the local AI routines and the Quake 2 server on which the game environment is maintained, before detailing the API’s agent architecture, which includes an interface to the MatLab programming environment and the ability to parse and analyse full recordings of game sessions. We conclude the chapter with a discussion of QASE’s adoption by numerous universities as both an undergraduate teaching tool and research platform. We then proceed to describe the various imitative mechanisms which we have developed using QASE and its MatLab integration facilities. We first outline a behaviour model based on a well-known psychological model of human planning. Drawing upon previous research, we also identify a set of believability criteria - elements of agent behaviour which are of particular importance in determining the “humanness” of its in-game appearance. We then detail a reinforcement-learning approach to imitating the human player’s navigation of his environment, centred upon his pursuit of items as strategic goals. In the subsequent section, we describe the integration of this strategic system with a Bayesian mechanism for the imitation of tactical and motion-modelling behaviours. Finally, we outline a model for the imitation of reactive combat behaviours; specifically, weapon-selection and aiming. Experiments are presented in each case to demonstrate the imitative mechanisms’ ability to accurately reproduce observed behaviours. Finally, we criticise the lack of any existing methodology to formally gauge the believability of game agents, and observe that the few previous attempts have been extremely ad-hoc and informal. We therefore propose a generalised approach to such testing; the Bot-Oriented Turing Test (BOTT). This takes the form of an anonymous online questionnaire, an accompanying protocol to which examiners should adhere, and the formulation of a believability index which numerically expresses each agent’s humanness as indicated by its observers, weighted by their experience and the accuracy with which the agents were identified. To both validate the survey approach and to determine the efficacy of our imitative models, we present a series of experiments which use the believability test to evaluate our own imitation agents against both human players and traditional artificial bots. We demonstrate that our imitation agents perform substantially better than even a highly-regarded rule-based agent, and indeed approach the believability of actual human players. Some suggestions for future directions in our research, as well as a broader discussion of open questions, conclude this thesis

Autistic traits affect interpersonal motor coordination by modulating strategic use of role-based behavior

Background: Despite the fact that deficits in social communication and interaction are at the core of Autism Spectrum Conditions (ASC), no study has yet tested individuals on a continuum from neurotypical development to autism in an on-line, cooperative, joint action task. In our study, we aimed to assess whether the degree of autistic traits affects participants' ability to modulate their motor behavior while interacting in a Joint Grasping task and according to their given role. Methods: Sixteen pairs of adult participants played a cooperative social interactive game in which they had to synchronize their reach-to-grasp movements. Pairs were comprised of one ASC and one neurotypical with no cognitive disability. In alternate experimental blocks, one participant knew what action to perform (instructed role) while the other had to infer it from his/her partner’s action (adaptive role). When in the adaptive condition, participants were told to respond with an action that was either opposite or similar to their partner. Participants also played a non-social control game in which they had to synchronize with a non-biological stimulus. Results: In the social interactive task, higher degree of autistic trait s predicted less ability to mod ulate joint action according to one’s interactive role. In the non-social task, autistic traits did not predict differences in movement preparation and planning, thus ruling out the possibility that social interact ive task results were due to basic motor or executive function difficulties. Furthermore, when participants played the non-social game, the higher their autistic traits, the more they were interfered by the non-biological stimulus. Conclusions: Our study shows for the first time that high autistic traits predict a stereotypical interaction style when individuals are required to modulate their movements in order to coordinate with their partner according to their role in a joint action task. Specifically, the infrequent emergence of role-based motor behavior modulation during on-line motor cooperation in participants with high autistic traits sheds light on the numerous difficulties ASC have in nonverbal social interaction

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator