Abstract
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.
Keywords
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
R. Aumann and A. Brandenburger. Epistemic conditions for Nash equilibrium. Working Paper 91-042, Harvard Business School, 1991.
L. Fortnow and D. Whang. Optimality and domination in repeated games with bounded players. Technical report, Department of Computer Science University of Chicago, Chicago, 1994.
I. Gilboa and D. Samet. Bounded vs. unbounded rationality: The tyranny of the weak. Games and Economic Behavior, 1:213–221, 1989.
Ehud Kalai. Bounded rationality and strategic complexity in repeated games. In T. Ichiishi, A. Neyman, and Y. Tauman, editors, Game Theory and Aplications, pages 131–157. Academic Press, San Diego, 1990.
Michael J. Kearns and Umesh V. Vazirani. An Introduction to Computational Learning Theory. MIT press, Cambridge, Massachusetts, 1994.
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.
Christos H. Papadimitriou. On players with a bounded, number of states. Games and Economic Behavior, 4:122–131, 1992.
R. Rivest and R. Schapire. Inference of finite automata using homing sequences. Information and Computation, 103:299–347, 1993.
Alvin E. Roth, Vesna Prasnikar, Mashiro Okuno-Fujiwara, and Shmuel Zamir. Bargining and market behavior in jerusalem, ljubljana, pittsburg, and tokyo: an experimantal study. American Economic Review, 81(5):1068–1095, 1991.
A. Rubinstein. Finite automata play the repeated prisoner's dilemma. ST/ICERD Discussion Paper 85/109, London School of Economics, 1985.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mor, Y., Goldman, C.V., Rosenschein, J.S. (1996). Learn your opponent's strategy (in polynomial time)!. In: Weiß, G., Sen, S. (eds) Adaption and Learning in Multi-Agent Systems. IJCAI 1995. Lecture Notes in Computer Science, vol 1042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60923-7_26
Download citation
DOI: https://doi.org/10.1007/3-540-60923-7_26
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60923-0
Online ISBN: 978-3-540-49726-4
eBook Packages: Springer Book Archive