Learning in One-Shot Strategic Form Games

  • Alon Altman
  • Avivit Bercovici-Boden
  • Moshe Tennenholtz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)

Abstract

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.

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References

  1. 1.
    Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey. Journal of AI Research 4, 237–285 (1996)Google Scholar
  2. 2.
    Erev, I., Roth, A.E.: Predicting how people play games: Reinforcement learning in games with unique strategy equilibrium. American Economic Review 88, 848–881 (1998)Google Scholar
  3. 3.
    Claus, C., Boutilier, C.: The dynamics of reinforcement learning in cooperative multi-agent systems. In: Proc. Workshop on Multi-Agent Learning, pp. 602–608 (1997)Google Scholar
  4. 4.
    Fudenberg, D., Levine, D.: The theory of learning in games. MIT Press, Cambridge (1998)MATHGoogle Scholar
  5. 5.
    Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Proc. 11th ICML, pp. 157–163 (1994)Google Scholar
  6. 6.
    Hu, J., Wellman, M.: Multi-agent reinforcement learning: Theoretical framework and an algorithms. In: Proc. 15th ICML (1998)Google Scholar
  7. 7.
    Brafman, R.I., Tennenholtz, M.: R-max – a general polynomial time algorithm for near-optimal reinforcement learning. In: IJCAI 2001 (2001)Google Scholar
  8. 8.
    Carmel, D., Markovitch, S.: Exploration strategies for model-based learning in multiagent systems. Autonomous Agents and Multi-agent Systems 2(2), 141–172 (1999)CrossRefGoogle Scholar
  9. 9.
    Kagel, J.H., Roth, A.: The Handbook of Experimental Economics. Princeton University Press, Princeton (1995)Google Scholar
  10. 10.
    Roth, A., Erev, I.: Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior 8, 164–212 (1995)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Camerer, C.F., Ho, T.H., Chong, J.K.: A cognitive hierarchy model of games. The Quarterly Journal of Economics 119(3), 861–898 (2004)MATHCrossRefGoogle Scholar
  12. 12.
    Costa-Gomes, M., Crawford, V.P., Broseta, B.: Cognition and behavior in normal-form games: An experimental study. Econometrica 69(5), 1193–1235 (2001)CrossRefGoogle Scholar
  13. 13.
    Gal, Y., Pfeffer, A., Marzo, F., Grosz, B.J.: Learning social preferences in games. In: Proc. of AAAI 2004, pp. 226–231 (2004)Google Scholar
  14. 14.
    Stahl, D.O.: Population rule learning in symmetric normal-form games: theory and evidence. Journal of Economic Behavior and Organization 1304, 1–14 (2001)Google Scholar
  15. 15.
    Gilboa, I., Schmeidler, D.: Case-based decision theory. Quarterly Journal of Economics 110, 605–639 (1995)MATHCrossRefGoogle Scholar
  16. 16.
    Fudenberg, D., Tirole, J.: Game Theory. MIT Press, Cambridge (1991)Google Scholar
  17. 17.
    Nudelman, E., Wortman, J., Shoham, Y., Leyton-Brown, K.: Run the gamut: A comprehensive approach to evaluating game-theoretic algorithms. In: AAMAS 2004, pp. 880–887 (2004)Google Scholar
  18. 18.
    Wolfstetter, E.: Auctions: An introduction. Journal of Economic Surveys 10(4), 367–420 (1996)CrossRefGoogle Scholar
  19. 19.
    Erev, I., Roth, A., Slonim, R., Barron, G.: Learning and equilibrium as useful approximations: accuracy of prediction on randomly selected constant sum games (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alon Altman
    • 1
  • Avivit Bercovici-Boden
    • 1
  • Moshe Tennenholtz
    • 1
  1. 1.Faculty of Industrial Engineering and ManagementTechnion — Israel Institute of TechnologyHaifaIsrael

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