Learning in One-Shot Strategic Form Games

We propose a machine learning approach to action prediction in one-shot games. In contrast to the huge literature on learning in games where an agent's model is deduced from its previous actions in a multi-stage game, we propose the idea of inferring correlations between agents' actions in different one-shot games in order to predict an agent's action in a game which she did not play yet. We define the approach and show, using real data obtained in experiments with human subjects, the feasibility of this approach. Furthermore, we demonstrate that this method can be used to increase payoffs of an adequately informed agent. This is, to the best of our knowledge, the first proposed and tested approach for learning in one-shot games, which is the most basic form of multi-agent interaction.

[1]  Michael P. Wellman,et al.  Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm , 1998, ICML.

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[3]  Craig Boutilier,et al.  The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems , 1998, AAAI/IAAI.

[4]  Miguel A. Costa-Gomes,et al.  Cognition and Behavior in Normal-Form Games: An Experimental Study , 1998 .

[5]  A. Roth,et al.  Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria , 1998 .

[6]  Ronen I. Brafman,et al.  R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning , 2001, J. Mach. Learn. Res..

[7]  Ya'akov Gal,et al.  Learning Social Preferences in Games , 2004, AAAI.

[8]  David Carmel,et al.  Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies , 1999, Autonomous Agents and Multi-Agent Systems.

[9]  Miguel A. Costa-Gomes,et al.  Cognition and Behavior in Normal-Form Games: An Experimental Study , 1998 .

[10]  Drew Fudenberg,et al.  Game theory (3. pr.) , 1991 .

[11]  J. Kagel,et al.  Handbook of Experimental Economics , 1997 .

[12]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

[13]  Michael L. Littman,et al.  Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.

[14]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[15]  Elmar G. Wolfstetter.  AUCTIONS: AN INTRODUCTION , 1996 .

[16]  Dale O. Stahl,et al.  Population Rule Learning in Symmetric Normal-Form Games , 2001 .

[17]  I. Gilboa,et al.  Case-Based Decision Theory , 1995 .

[18]  Colin Camerer,et al.  A Cognitive Hierarchy Model of Games , 2004 .

[19]  A. Roth,et al.  Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term* , 1995 .

[20]  Robert L. Slonim,et al.  Learning and equilibrium as useful approximations: Accuracy of prediction on randomly selected constant sum games , 2007 .

[21]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[22]  Yoav Shoham,et al.  Run the GAMUT: a comprehensive approach to evaluating game-theoretic algorithms , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..