Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [EC98]
    Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms, Evolutionary Computation, vol.6, no.4, MIT Press, 1998Google Scholar
  2. [Angeline98]
    Angeline,P.J., Subtree Crossover Causes Bloat in Proc. of Genetic Programming Conference 1998 (GP98), 1998Google Scholar
  3. [Banzhaf et aí.98]
    Banzhaf,W., Nordin,P., Keller,R.E., and Francone,F.D., Genetic Programming, An Introduction, Morgan Kaufmann, 1998Google Scholar
  4. [Hara et aí.99]
    Hara,A., and Nagao,T., Emergence of Cooperative Behavior using ADG; Automatically Defined Groups, in Proc. of the Genetic and Evolutionary Computation Conference (GECCO99), Morgan Kaufmann, 1999Google Scholar
  5. [Haynes et aí.95]
    Haynes, T., Wainwright,R., and Sen,S., Evolving a Team, in Working Notes of the AAAI-95 Fall Symposium on Genetic Programming, AAAI Press, 1995Google Scholar
  6. [Iba96]
    Iba,H., Emergent Cooperation for Multiple Agents using Genetic Programming, in Parallel Problem Solving form Nature IV (PPSN96), 1996Google Scholar
  7. [Iba98]
    Iba,H., Evolutionary Learning of Communicating Agents, Information Sciences, 108 (1–4), 1998Google Scholar
  8. [Ito et a1.96]
    Ito,T., Iba,H. and Kimura,M., Robot Programs Generated by Genetic Programming, Japan Advanced Institute of Science and Technology, IS-RR-9600011, in Genetic Programming 96, 1996Google Scholar
  9. [Koza 92]
    Koza, J., Genetic Programming, On the Programming of Computers by means of Natural Selection, MIT Press, 1992Google Scholar
  10. [Luke et a1.96]
    Luke,S. and Spector,L., Evolving Teamwork and Coordination with Genetic Programming, in Genetic Programming 96, MIT Press, 1996Google Scholar
  11. [Smith et a1.96]
    Smith,P.W.H, and Harries,K., Code Growth, Explicitly Defined Introns, and Alternative Selection Schemes, in Evolutionary Computation, vol.6, no,4, MIT Press, 1999Google Scholar
  12. [Soule et al.96]
    Soule,T., Foster,J.A., and Dickinson,J., Code Growth in Genetic Programming, in Genetic Programming 96, 1996Google Scholar

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

Personalised recommendations