Controlling effective introns for multi-agent learning by means of genetic programming

Authors:
Hitoshi Iba;Makoto Terao
Affiliations:
Univ. of Tokyo, Tokyo, Japan;Univ. of Tokyo, Tokyo, Japan
Venue:
Soft computing agents
Year:
2002

Citing 7
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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.