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

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.