Article

Exploring XCS in multiagent environments

Authors:

Hiroyasu Inoue,

Keiki Takadama,

Katsunori ShimoharaAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

Pages 109 - 111

https://doi.org/10.1145/1102256.1102281

Published: 25 June 2005 Publication History

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Abstract

This paper investigates the adaptability of XCS in four different multiagent environments. The environments are realized in a simplified soccer game, and they include (1) single-agent environment, (2) multiagent environment with an opponent, (3) multiagent environment with a teammate, (4) multiagent environment with both an opponent and a teammate. Although XCS generally seems inferior to strength-based XCS in such stochastic environments, experimental results in a specific stochastic environment show that XCS is superior to strength-based XCS. Furthermore, XCS with profit sharing is more effective than one using the bucket brigade in multiagent environments.

References

[1]

T. Kovacs. XCS's strength-based twin. Part I. In Learning Classifier Systems, volume LNCS 2661, pages 61--80. Springer, 2003.

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[2]

K. Miyazaki, M. Yamamura, and S. Kobayashi. On the rationality of profit sharing in reinforcement learning. In Proceedings of the 3rd International Conference on Fuzzy Logic, Neural Nets and Soft Computing, pages 285--288, 1994.

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[3]

S. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149--175, 1995.

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Nantogma SXu YRan W(2021)A Coordinated Air Defense Learning System Based on Immunized Classifier SystemsSymmetry10.3390/sym1302027113:2(271)Online publication date: 5-Feb-2021
https://doi.org/10.3390/sym13020271
Wang CChen HYan CXiang X(2019)Reinforcement Learning with an Extended Classifier System in Zero-sum Markov Games2019 IEEE International Conference on Agents (ICA)10.1109/AGENTS.2019.8929148(44-49)Online publication date: Oct-2019
https://doi.org/10.1109/AGENTS.2019.8929148
Fredivianus NGeihs K(2017)Classifier systems with native fuzzy logic control operationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082487(1341-1348)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082487
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Index Terms

Exploring XCS in multiagent environments
1. Computing methodologies
  1. Machine learning

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Published In

GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

June 2005

431 pages

ISBN:9781450378000

DOI:10.1145/1102256

Conference Chair:
Franz Rothlauf
University of Mannheim, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2005

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GECCO05

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SIGEVO

GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 26, 2005

D.C., Washington

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Nantogma SXu YRan W(2021)A Coordinated Air Defense Learning System Based on Immunized Classifier SystemsSymmetry10.3390/sym1302027113:2(271)Online publication date: 5-Feb-2021
https://doi.org/10.3390/sym13020271
Wang CChen HYan CXiang X(2019)Reinforcement Learning with an Extended Classifier System in Zero-sum Markov Games2019 IEEE International Conference on Agents (ICA)10.1109/AGENTS.2019.8929148(44-49)Online publication date: Oct-2019
https://doi.org/10.1109/AGENTS.2019.8929148
Fredivianus NGeihs K(2017)Classifier systems with native fuzzy logic control operationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082487(1341-1348)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082487
Irvan MYamada TTerano T(2013)Influence of Organizational Learning for Multi-Agent Simulation based on an Adaptive Classifier SystemIEEJ Transactions on Electronics, Information and Systems10.1541/ieejeiss.133.1752133:9(1752-1761)Online publication date: 2013
https://doi.org/10.1541/ieejeiss.133.1752
Lode CRichter USchmeck HPelikan MBranke J(2010)Adaption of XCS to multi-learner predator/prey scenariosProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830669(1015-1022)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830483.1830669

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Theoretical XCS parameter settings of learning accurate classifiers

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