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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms, Evolutionary Computation, vol.6, no.4, MIT Press, 1998
Angeline,P.J., Subtree Crossover Causes Bloat in Proc. of Genetic Programming Conference 1998 (GP98), 1998
Banzhaf,W., Nordin,P., Keller,R.E., and Francone,F.D., Genetic Programming, An Introduction, Morgan Kaufmann, 1998
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, 1999
Haynes, T., Wainwright,R., and Sen,S., Evolving a Team, in Working Notes of the AAAI-95 Fall Symposium on Genetic Programming, AAAI Press, 1995
Iba,H., Emergent Cooperation for Multiple Agents using Genetic Programming, in Parallel Problem Solving form Nature IV (PPSN96), 1996
Iba,H., Evolutionary Learning of Communicating Agents, Information Sciences, 108 (1–4), 1998
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, 1996
Koza, J., Genetic Programming, On the Programming of Computers by means of Natural Selection, MIT Press, 1992
Luke,S. and Spector,L., Evolving Teamwork and Coordination with Genetic Programming, in Genetic Programming 96, MIT Press, 1996
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, 1999
Soule,T., Foster,J.A., and Dickinson,J., Code Growth in Genetic Programming, in Genetic Programming 96, 1996
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Iba, H., Terao, M. (2001). Controlling Effective Introns for Multi-Agent Learning by Means of Genetic Programming. In: Loia, V., Sessa, S. (eds) Soft Computing Agents. Studies in Fuzziness and Soft Computing, vol 75. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1815-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1815-4_3
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-00350-3
Online ISBN: 978-3-7908-1815-4
eBook Packages: Springer Book Archive