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


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
Number of pages12
ISBN (Electronic)9783540204602
Publication statusPublished - 2003
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)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003
Country/TerritoryKorea, Republic of

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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