Learn your opponent's strategy (in polynomial time)!
Agents that interact in a distributed environment might increase their utility by behaving optimally given the strategies of the other agents. To do so, agents need to learn about those with whom they share the same world.
This paper examines interactions among agents from a game theoretic perspective. In this context, learning has been assumed as a means to reach equilibrium. We analyze the complexity of this learning process. We start with a restricted two-agent model, in which agents are represented by finite automata, and one of the agents plays a fixed strategy. We show that even with this restrictions, the learning process may be exponential in time.
We then suggest a criterion of simplicity, that induces a class of automata that are learnable in polynomial time.
- R. Aumann and A. Brandenburger. Epistemic conditions for Nash equilibrium. Working Paper 91-042, Harvard Business School, 1991.
- Fortnow, L., Whang, D. (1994) Optimality and domination in repeated games with bounded players. Technical report. Department of Computer Science University of Chicago, Chicago
- Gilboa, I., Samet, D. (1989) Bounded vs. unbounded rationality: The tyranny of the weak. Games and Economic Behavior 1: pp. 213-221
- Kalai, E. Bounded rationality and strategic complexity in repeated games. In: Ichiishi, T., Neyman, A., Tauman, Y. eds. (1990) Game Theory and Aplications. Academic Press, San Diego, pp. 131-157
- Kearns, M. J., Vazirani, U. V. (1994) An Introduction to Computational Learning Theory. MIT press, Cambridge, Massachusetts
- Yishay Mor. Computational approaches to rational choice. Master's thesis, Hebrew University, 1995. In preparation.
- Yishay Mor and Jeffrey S. Rosenschein. Time and the prisoner's dilemma, 1995. International Conference on Multiagent Systems.(to appear).
- A. Neyman. Bounded complexity justifies cooperation in finitely repeated prisoner's dilemma. Economic Letters, pages 227–229, 1985.
- Papadimitriou, C. H. (1992) On players with a bounded, number of states. Games and Economic Behavior 4: pp. 122-131
- Rivest, R., Schapire, R. (1993) Inference of finite automata using homing sequences. Information and Computation 103: pp. 299-347
- Roth, A. E., Prasnikar, V., Okuno-Fujiwara, M., Zamir, S. (1991) Bargining and market behavior in jerusalem, ljubljana, pittsburg, and tokyo: an experimantal study. American Economic Review 81: pp. 1068-1095
- A. Rubinstein. Finite automata play the repeated prisoner's dilemma. ST/ICERD Discussion Paper 85/109, London School of Economics, 1985.
- Learn your opponent's strategy (in polynomial time)!
- Book Title
- Adaption and Learning in Multi-Agent Systems
- Book Subtitle
- IJCAI'95 Workshop Montréal, Canada, August 21, 1995 Proceedings
- pp 164-176
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series Subtitle
- Lecture Notes in Artificial Intelligence
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Additional Links
- Distributed Artificial Intelligence
- repeated games
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