Learn your opponent’s strategy (in polynomial time)!

Yishay Mor; Claudia V. Goldman; Jeffrey S. Rosenschein

doi:10.1007/3-540-60923-7_26

Learn your opponent’s strategy (in polynomial time)!

Yishay Mor, Claudia V. Goldman, Jeffrey S. Rosenschein

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

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.

Original language	English
Title of host publication	Adaption and Learning in Multi-Agent Systems - IJCAI 1995 Workshop, Proceedings
Editors	Gerhard Weib, Sandip Sen
Publisher	Springer Verlag
Pages	165-176
Number of pages	12
ISBN (Print)	9783540609230
DOIs	https://doi.org/10.1007/3-540-60923-7_26
State	Published - 1996
Event	Workshop on Adaptation and Learning in Multi-Agent Systems, 1995 held as part of 14th International Joint Conference on Artificial Intelligence, IJCAI 1995 - Montreal, Canada Duration: 21 Aug 1995 → 21 Aug 1995

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1042
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Workshop on Adaptation and Learning in Multi-Agent Systems, 1995 held as part of 14th International Joint Conference on Artificial Intelligence, IJCAI 1995
Country/Territory	Canada
City	Montreal
Period	21/08/95 → 21/08/95

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1996.

Keywords

Automata
Distributed artificial intelligence
Learning
Repeated games

Access to Document

10.1007/3-540-60923-7_26

Cite this

Mor, Y., Goldman, C. V., & Rosenschein, J. S. (1996). Learn your opponent’s strategy (in polynomial time)! In G. Weib, & S. Sen (Eds.), Adaption and Learning in Multi-Agent Systems - IJCAI 1995 Workshop, Proceedings (pp. 165-176). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1042). Springer Verlag. https://doi.org/10.1007/3-540-60923-7_26

Mor, Yishay ; Goldman, Claudia V. ; Rosenschein, Jeffrey S. / Learn your opponent’s strategy (in polynomial time)!. Adaption and Learning in Multi-Agent Systems - IJCAI 1995 Workshop, Proceedings. editor / Gerhard Weib ; Sandip Sen. Springer Verlag, 1996. pp. 165-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Mor, Y, Goldman, CV & Rosenschein, JS 1996, Learn your opponent’s strategy (in polynomial time)! in G Weib & S Sen (eds), Adaption and Learning in Multi-Agent Systems - IJCAI 1995 Workshop, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1042, Springer Verlag, pp. 165-176, Workshop on Adaptation and Learning in Multi-Agent Systems, 1995 held as part of 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, Montreal, Canada, 21/08/95. https://doi.org/10.1007/3-540-60923-7_26

Learn your opponent’s strategy (in polynomial time)! / Mor, Yishay; Goldman, Claudia V.; Rosenschein, Jeffrey S.
Adaption and Learning in Multi-Agent Systems - IJCAI 1995 Workshop, Proceedings. ed. / Gerhard Weib; Sandip Sen. Springer Verlag, 1996. p. 165-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1042).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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KW - Repeated games

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Mor Y, Goldman CV, Rosenschein JS. Learn your opponent’s strategy (in polynomial time)! In Weib G, Sen S, editors, Adaption and Learning in Multi-Agent Systems - IJCAI 1995 Workshop, Proceedings. Springer Verlag. 1996. p. 165-176. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-60923-7_26

Learn your opponent’s strategy (in polynomial time)!

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this