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International Conference on Parallel Problem Solving from Nature

PPSN 2004: Parallel Problem Solving from Nature - PPSN VIII pp 872–881Cite as

Evolutionary Multi-agent Systems

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3242))

Abstract

In Multi-Agent learning, agents must learn to select actions that maximize their utility given the action choices of the other agents. Cooperative Coevolution offers a way to evolve multiple elements that together form a whole, by using a separate population for each element. We apply this setup to the problem of multi-agent learning, arriving at an evolutionary multi-agent system (EA-MAS). We study a problem that requires agents to select their actions in parallel, and investigate the problem solving capacity of the EA-MAS for a wide range of settings.

Secondly, we investigate the transfer of the COllective INtelligence(COIN) framework to the EA-MAS. COIN is a proved engineering approach for learning of cooperative tasks in MASs, and consists of re-engineering the utilities of the agents so as to contribute to the global utility. It is found that, as in the Reinforcement Learning case, the use of the Wonderful Life Utility specified by COIN also leads to improved results for the EA-MAS.

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Author information

Authors and Affiliations

  1. Center for Mathematics and Computer Science (CWI), P.O. Box 94079, Amsterdam, The Netherlands

    Pieter J. ’t Hoen

  2. Decision Support Systems Group, Utrecht University, P.O. Box 80.089, Utrecht, The Netherlands

    Edwin D. de Jong

Authors
  1. Pieter J. ’t Hoen
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  2. Edwin D. de Jong
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Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Xin Yao

  2. Automated Scheduling, Optimisation and Planning Group, School of Computer Science & IT, University of Nottingham, NG8 1BB, Nottingham, UK

    Edmund K. Burke

  3. Intelligent Systems Group Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizábal 1, 20018, San Sebastian, Donostia, Spain

    José A. Lozano

  4. University of the West of England, Bristol, United Kingdom

    Jim Smith

  5. GeNeura Team, Depto. Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain

    Juan Julián Merelo-Guervós

  6. School of Computer Science, The University of Birmingham, B15 1TT, Edgbaston, Birmingham, UK

    John A. Bullinaria

  7. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, Great Britain

    Jonathan E. Rowe

  8. School of Computer Science, University of Birmingham, United Kingdom

    Peter Tiňo

  9. School of Computer Science, The University of Birmingham, B15 2TT, Birmingham, UK

    Ata Kabán

  10. Faculty of Computer Science, Dortmund University of Technology, 44221, Dortmund, Germany

    Hans-Paul Schwefel

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© 2004 Springer-Verlag Berlin Heidelberg

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’t Hoen, P.J., de Jong, E.D. (2004). Evolutionary Multi-agent Systems. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_88

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  • DOI: https://doi.org/10.1007/978-3-540-30217-9_88

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23092-2

  • Online ISBN: 978-3-540-30217-9

  • eBook Packages: Springer Book Archive

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