Model-based learning of interaction strategies in multi-agent systems

Journal of Experimental and Theoretical Artificial Intelligence

Volumn 10, Issue 3, 1998, Pages 309-332

  Carmel, David ^a   Markovitch, Shaul ^a  

^a Techion   (Israel)

Author keywords

Model based learning; Multi agent system

Indexed keywords

EID: 0032344579     PISSN: 0952813X     EISSN: 13623079     Source Type: Journal    
DOI: 10.1080/095281398146789     Document Type: Article

References (31)

1. Abreu, D., and Rubinstein, A., 1988, The structure of Nash equilibrium in repeated games with finite automata. Econometrica, 56(6): 1259-1281.
2. Angluin, D., 1978, On the complexity of minimum inference of regular sets. Information and Control, 39: 337-350.
3. Angluin, D., 1987, Learning regular sets from queries and counter-examples. Information and Computation, 75: 87-106.
4. Axelrod, R., 1984, The Evolution of Cooperation (New York: Basic Books).
5. Barto, A. G., Bradtke, S. J., and Singh, S. P., 1995, Learning to act using real-time dynamic programming. Artificial Intelligence, 72(1): 81-138.
6. Ben-Porath, E., 1990, The complexity of computing a best response automaton in repeated games with mixed strategies. Games and Economic Behaviour, 2: 1-12.
7. Bertsekas, D. P., 1987, Dynamic Programming: Deterministic and Stochastic Models (New York: Prentice-Hall).
8. Carmel, D., and Markovitch, S., 1996a, Incorporating opponent models into adversary search. In Proceedings of Thirteenth National Conference on Artificial Intelligence (AAAI 96), Portland, Oregon, August, pp. 120-125.
9. Carmel, D., and Markovitch, S., 1996b, Learning models of intelligent agents. In Proceedings of Thirteenth National Conference on Artificial Intelligence (AAAI 96), Portland, Oregon, August, pp. 62-67.
10. Carmel, D., and Markovitch, S., 1997, Exploration and adaptation in multi-agent systems: a model-based approach. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 97), Nagoya, Japan, August pp. 606-611.
11. Cormen, T. H., Leiserson, C. E., and Rivest, R. L., 1990, Introduction to Algorithms (Cambridge, Mass: M IT Press).
12. Fortnow, L., and Whang, D., 1994, Optimality and domination in repeated games with bounded players. In Proceedings of the 26th Annual ACM Symposium on Theory and Computing, Montreal, Quebec (ACM Press), pp. 741-749.
13. Freund, Y., Kearns, M., Ron, D., Rubinfeld, R., Schapire, R. E., and Sellie, L., 1993, Efficient learning of typical finite automata from random walls. In Proceedings of the 25th Annual ACM Symposium on Theory and Computing, San Diego, CA, May (ACM Press), pp. 315-324.
14. Gilboa, I., and Samet, R. E., 1989, Bounded versus unbounded rationality: The tyranny of the weak. Games and Economic Behaviour, 1: 213-221.
15. Gold, E. M., 1978, Complexity of automaton identification from given data. Information and Control, 37: 301-320.
16. Hopcroft, J. E., and Ullman, J. D., 1979, Introduction to Automata Theory, Languages and Computation (Mass: Addison-Wesley), 1979.
17. Khardon, R., 1996, Learning to take actions. In Proceeding of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland, Oregon, pp. 787-792.
18. Knoblauch, V., 1994, Computable strategies for repeated Prisoner's Dilemma. Games and Economic Behaviour, 7: 381-389.
19. Mor, V., Goldman, C. V., and Rosenschein, J. S., 1996, Learn your opponent's strategy (in polynomial time). In G. Weiß and S. Sen (eds) Adaptation and Learning in Multi-agent Systems, Lecture Notes in AI (Berlin: Springer-Verlag).
20. Papadimitriou, C. H., 1992, On players with a bounded number of states. Games and Economic Behaviour, 4: 122-131.
21. Papadimitriou, C. H., and Tsitsiklis, J. N., 1987, The complexity of Markov decision processes. Mathematics of Operations Research, 12(3): 441-450.
22. Pitt, L., 1989, Inductive inference, DFAs and computational complexity. In K. P. Jantke, (ed.) Analogical and Inductive Inference, Lecture Notes in AI, Vol. 397 (Berlin: Springer-Verlag), pp. 18-44.
23. Rivest, R. L., and Schapire, R. E., 1993, Inference of finite automata using homing sequences. Information and Computation, 103(2): 299-347.
24. Ron, D., and Rubinfeled, R., 1995, Exactly learning automata with small cover time. In Proceedings of the Eighth Annual ACM Conference on Computational Learning Theory, Santa Cruz, Ca, pp. 427-436.
25. Rubinstein, A., 1986, Finite automata play the repeated Prisoner's Dilemma. Journal of Economic Theory, 39: 83-96.
26. Sandholm, T. W., and Crites, R. H., 1995, Multiagent reinforcement learning and the iterated Prisoner's Dilemma. Biosystems Journal, 37: 147-166.
27. Sen, S., Sekaran, M., and Hale, J., 1994, Learning to coordinate without sharing information. In Proceeding of the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, Washington, pp. 426-431.
28. Shoham, Y., and Tennenholtz, M., 1994, Co-Learning and the evolution of social activity. Technical Report STAN-CS-TR-94-1511 (CA: Stanford University, Department of Computer Science).
29. Sutton, R. S., 1990, Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Proceedings of the 7 th International Conference on Machine Learning, San Mateo, CA (Morgan Kaufman), pp. 216-224.
30. Watkins, C. J. C. H., and P. Dayan, 1992, Technical notes: Q-learning. Machine Learning, 8: 279-292.
31. Weiß, G., and Sen, S., 1996, Adaptation and Learning in Multi-agent Systems, Lecture Notes in AI, Vol. 1042 (Berlin: Springer-Verlag).