Learning Social Preferences in Games

This paper presents a machine-learning approach to modeling human behavior in one-shot games. It provides a framework for representing and reasoning about the social factors that affect people’s play. The model predicts how a human player is likely to react to different actions of another player, and these predictions are used to determine the best possible strategy for that player. Data collection and evaluation of the model were performed on a negotiation game in which humans played against each other and against computer models playing various strategies. A computer player trained on human data outplayed Nash equilibrium and Nash bargaining computer players as well as humans. It also generalized to play people and game situations it had not seen before.Engineering and Applied Science

Search and planning under incomplete information : a study using Bridge card play

This thesis investigates problem-solving in domains featuring incomplete information and multiple agents with opposing goals. In particular, we describe Finesse --- a system that forms plans for the problem of declarer play in the game of Bridge. We begin by examining the problem of search. We formalise a best defence model of incomplete information games in which equilibrium point strategies can be identified, and identify two specific problems that can affect algorithms in such domains. In Bridge, we show that the best defence model corresponds to the typical model analysed in expert texts, and examine search algorithms which overcome the problems we have identified. Next, we look at how planning algorithms can be made to cope with the difficulties of such domains. This calls for the development of new techniques for representing uncertainty and actions with disjunctive effects, for coping with an opposition, and for reasoning about compound actions. We tackle these problems with a..

CP-nets and Nash equilibria

We relate here two formalisms that are used for different purposes in reasoning about multi-agent systems. One of them are strategic games that are used to capture the idea that agents interact with each other while pursuing their own interest. The other are CP-nets that were introduced to express qualitative and conditional preferences of the users and which aim at facilitating the process of preference elicitation. To relate these two formalisms we introduce a natural, qualitative, extension of the notion of a strategic game. We show then that the optimal outcomes of a CP-net are exactly the Nash equilibria of an appropriately defined strategic game in the above sense. This allows us to use the techniques of game theory to search for optimal outcomes of CP-nets and vice-versa, to use techniques developed for CP-nets to search for Nash equilibria of the considered games.Comment: 6 pages. in: roc. of the Third International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS '05). To appea

Implicit and explicit measures: a test of a dissociative model of aggression

This study tested a dissociative model of aggression measurement. Aggression is construed as having two components, each of which is associated more strongly with either implicit or explicit measures of aggression. A videogame based frustration manipulation was used to elicit hostile aggressive responses in the form of hard force applied to buttons. Instrumental aggression criteria were also assessed in the form of honesty in reporting game outcomes, willingness to pause games while believing that pausing could damage the study results, and willingness to use unfair strategies that are also described as damaging to study results. Differential prediction of these behaviors by implicit and explicit measures of aggression supported a dissociative model of aggression measurement.M.S.Committee Chair: Dr. Lawrence R. James; Committee Member: Dr. Jack M. Feldman; Committee Member: Dr. James S. Robert

Dominant Strategies in Two Qubit Quantum Computations

Nash equilibrium is a solution concept in non-strictly competitive, non-cooperative game theory that finds applications in various scientific and engineering disciplines. A non-strictly competitive, non-cooperative game model is presented here for two qubit quantum computations that allows for the characterization of Nash equilibrium in these computations via the inner product of their state space. Nash equilibrium outcomes are optimal under given constraints and therefore offer a game-theoretic measure of constrained optimization of two qubit quantum computations.Comment: The abstract has been re-written and technical details added to section 5 in version

Team reasoning and intentional cooperation for mutual benefit

This paper proposes a concept of intentional cooperation for mutual benefit. This concept uses a form of team reasoning in which team members aim to achieve common interests, rather than maximising a common utility function, and in which team reasoners can coordinate their behaviour by following pre-existing practices. I argue that a market transaction can express intentions for mutually beneficial cooperation even if, extensionally, participation in the transaction promotes each party’s self-interest