Evolutionary learning of multiagents using strategic coalition in the IPD game

Seung Ryong Yang; Sung Bae Cho

doi:10.1007/978-3-540-39896-7_5

Evolutionary learning of multiagents using strategic coalition in the IPD game

Seung Ryong Yang, Sung Bae Cho

College of Computing

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

Abstract

Social and economic systems consist of complex interactions among its members. Their behaviors become adaptive according to changing environment. In many cases, an individual's behaviors can be modeled by a stimulus-response system in a dynamic environment. In this paper, we use the Iterated Prisoner's Dilemma (IPD) game, which is a simple model to deal with complex problems for dynamic systems. We propose strategic coalition consisting of many agents and simulate their emergence in a co-evolutionary learning environment. Also we introduce the concept of confidence for agents in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results show that co-evolutionary learning with coalitions and confidence can produce better performing strategies that generalize well in dynamic environments.

Original language	English
Title of host publication	Intelligent Agents and Multi-Agent Systems
Editors	Jaeho Lee, Mike Barley
Publisher	Springer Verlag
Pages	50-61
Number of pages	12
ISBN (Electronic)	9783540204602
DOIs	https://doi.org/10.1007/978-3-540-39896-7_5
Publication status	Published - 2003
Event	6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003 - Seoul, Korea, Republic of Duration: 2003 Nov 7 → 2003 Nov 8

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2891
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003
Country/Territory	Korea, Republic of
City	Seoul
Period	03/11/7 → 03/11/8

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-39896-7_5

Cite this

Yang, S. R., & Cho, S. B. (2003). Evolutionary learning of multiagents using strategic coalition in the IPD game. In J. Lee, & M. Barley (Eds.), Intelligent Agents and Multi-Agent Systems (pp. 50-61). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2891). Springer Verlag. https://doi.org/10.1007/978-3-540-39896-7_5

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Yang, SR & Cho, SB 2003, Evolutionary learning of multiagents using strategic coalition in the IPD game. in J Lee & M Barley (eds), Intelligent Agents and Multi-Agent Systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2891, Springer Verlag, pp. 50-61, 6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003, Seoul, Korea, Republic of, 03/11/7. https://doi.org/10.1007/978-3-540-39896-7_5

Evolutionary learning of multiagents using strategic coalition in the IPD game. / Yang, Seung Ryong; Cho, Sung Bae.
Intelligent Agents and Multi-Agent Systems. ed. / Jaeho Lee; Mike Barley. Springer Verlag, 2003. p. 50-61 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2891).

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

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AB - Social and economic systems consist of complex interactions among its members. Their behaviors become adaptive according to changing environment. In many cases, an individual's behaviors can be modeled by a stimulus-response system in a dynamic environment. In this paper, we use the Iterated Prisoner's Dilemma (IPD) game, which is a simple model to deal with complex problems for dynamic systems. We propose strategic coalition consisting of many agents and simulate their emergence in a co-evolutionary learning environment. Also we introduce the concept of confidence for agents in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results show that co-evolutionary learning with coalitions and confidence can produce better performing strategies that generalize well in dynamic environments.

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Yang SR, Cho SB. Evolutionary learning of multiagents using strategic coalition in the IPD game. In Lee J, Barley M, editors, Intelligent Agents and Multi-Agent Systems. Springer Verlag. 2003. p. 50-61. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-39896-7_5

Evolutionary learning of multiagents using strategic coalition in the IPD game

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