On the agenda(s) of research on multi-agent learning

AAAI Fall Symposium - Technical Report

Volumn FS-04-02, Issue , 2004, Pages 89-95

  Shoham, Yoav ^a   Powers, Rob ^a   Grenager, Trond ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MULTI-AGENT LEARNING; REINFORCEMENT LEARNING; STOCHASTIC GAMES; WELL-DEFINED PROBLEMS;

ARTIFICIAL INTELLIGENCE; GAME THEORY; MULTI AGENT SYSTEMS; PROBLEM SOLVING; RESEARCH AND DEVELOPMENT MANAGEMENT; STOCHASTIC CONTROL SYSTEMS;

LEARNING SYSTEMS;

EID: 26444543263     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. Bellman, R. 1957. Dynamic Programming. Princeton University Press.
2. Bowling, M., and Veloso, M. 2001. Rational and convergent learning in stochastic games. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence.
3. Bowling, M. 2000. Convergence problems of general-sum multiagent reinforcement learning. In Proceedings of the Seventeenth International Conference on Machine Learning, 89-94.
4. Brown, G. 1951. Iterative solution of games by fictitious play. In Activity Analysis of Production and Allocation. New York: John Wiley and Sons.
5. Camerer, C.; Ho, T.; and Chong, J. 2002. Sophisticated EWA learning and strategic teaching in repeated games. Journal of Economic Theory 104:137-188.
6. Chang, Y.-H., and Kaelbling, L. P. 2001. Playing is believing: The role of beliefs in multi-agent learning. In Proceedings of NIPS.
7. Claus, C., and Boutilier, C. 1998. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, 746-752.
8. Erev, I., and Roth, A. E. 1998. Predicting how people play games: reinforcement leaning in experimental games with unique, mixed strategy equilibria. The American Economic Review 88(4):848-881.
9. Fudenberg, D., and Kreps, D. 1993. Learning mixed equilibria. Games and Economic Behavior 5:320-367.
10. Gmytrasiewicz, P.; Durfee, E.; and Wehe, D. 1991. A decision-theoretic approach to coordinating multiagent interactions. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, 62-68.
11. Greenwald, A.; Hall, K.; and Serrano, R. 2002. Correlated-Q learning. In NIPS Workshop on Multiagent Learning.
12. Hannan, J. F. 1959. Approximation to bayes risk in repeated plays. Contributions to the Theory of Games 3:97-139.
13. Hu, J., and Wellman, P. 1998. Multiagent reinforcement learning: Theoretical framework and an algorithm. In Proceedings of the Fifteenth International Conference on Machine Learning, 242-250.
14. Hu, J., and Wellman, M. 2001. Learning about other agents in a dynamic multiagent system. Journal of Cognitive Systems Research 2:67-69.
15. Hu, J., and Wellman, M. 2002. Multiagent Q-learning. Journal of Machine Learning.
16. Jehiel, P., and Samet, D. 2001. Learning to play games in extensive form by valuation. NAJ Economics 3.
17. Kalai, E., and Lehrer, E. 1993. Rational learning leads to nash equilibrium. Econometrica 61(5): 1019-1045.
18. Leyton-Brown, K., and Tennenholtz, M. 2003. Localeffect games. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, 772-780.
19. Littman, M. L., and Szepesvari, C. 1996. A generalized reinforcement-learning model: Convergence and applications. In Proceedings of the 13th International Conference on Machine Learning, 310-318.
20. Littman, M. L. 1994. Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the 11th International Conference on Machine Learning, 157-163.
21. Littman, M. L. 2001. Friend-or-foe Q-learning in generalsum games. In Proceedings of the Eighteenth International Conference on Machine Learning.
22. Mertens, J.-F., and Zamir, S. 1985. Formulation of bayesian analysis for games with incomplete information. International Journal of Game Theory 14:1-29.
23. Nachbar, J. H., and Zame, W. R. 1996. Non-computable strategies and discounted repeated games. Economic Theory 8:103-122.
24. Neyman, A. 1985. Bounded complexity justifies cooperation in finitely repeated prisoner's dilemma. Economic Letters 227-229.
25. Papadimitriou, C., and Yannakakis, M. 1994. On complexity as bounded rationality. In STOC-94, 726-733.
26. Powers, R., and Shoham, Y. 2005. New criteria and a new algorithm for learning in multi-agent systems. In Advances in Neural Information Processing Systems. Forthcoming.
27. Rubinstein, A. 1998. Modeling Bounded Rationality. MIT Press.
28. Sen, S.; Sekaran, M.; and Hale, J. 1994. Learning to coordinate without sharing information. In Proceedings of the Twelfth National Conference on Artificial Intelligence, 426-431.
29. Stone, P., and Littman, M. L. 2001. Implicit negotiation in repeated games. In Meyer, J.-J., and Tambe, M., eds., Pre-proceedings of the Eighth International Workshop on Agent Theories, Architectures, and Languages (ATAL-2001), 96-105.
30. Tennenholtz, M. 2002. Efficient learning equilibrium. In Advances in Neural Information Processing Systems, volume 15. Cambridge, Mass.: MIT Press.
31. Watkins, C. J. C. H., and Dayan, P. 1992. Technical note: Q-learning. Machine Learning 8(3/4):279-292.
32. Wellman, M. P., and Wurman, P. R. 1999. A trading agent competition for the research community. In IJCAI-99 Workshop on Agent-Mediated Electronic Trading.