Evolutionary learning of multiagents using strategic coalition in the IPD game

Seung Ryong Yang, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationIntelligent Agents and Multi-Agent Systems
EditorsJaeho Lee, Mike Barley
PublisherSpringer Verlag
Pages50-61
Number of pages12
ISBN (Electronic)9783540204602
Publication statusPublished - 2003 Jan 1
Event6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003 - Seoul, Korea, Republic of
Duration: 2003 Nov 72003 Nov 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2891
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003
CountryKorea, Republic of
CitySeoul
Period03/11/703/11/8

Fingerprint

Iterated Prisoner's Dilemma
Evolutionary Learning
Prisoner's Dilemma Game
Coalitions
Confidence
Dynamic Environment
Dynamical systems
Adaptive Behavior
Economics
Learning Environment
Dynamic Systems
Generalise
Experimental Results
Interaction

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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

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 proceedingConference contribution

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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)).