Controlling Effective Introns for Multi-Agent Learning by Means of Genetic Programming

  • Hitoshi Iba
  • Makoto Terao
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 75)


This paper presents the emergence of the cooperative behavior for multiple agents by means of Genetic Programming (GP). For the purpose of evolving the effective cooperative behavior, we propose a controlling strategy of introns, which are non-executed code segments dependent upon the situation. The traditional approach to removing introns was able to cope with only a part of syntactically defined introns, which excluded other frequent types of introns. The validness of our approach is discussed with comparative experiments with robot simulation tasks, i.e., a navigation problem and an escape problem.


Genetic Programming Robot Navigation Real Robot Code Segment Robot Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Hitoshi Iba
    • 1
  • Makoto Terao
    • 2
  1. 1.Dept. of Frontier Informatics, School of Frontier ScienceThe University of TokyoJapan
  2. 2.Dept. of Inf. and Comm. Eng., School of EngineeringThe University of TokyoJapan

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