Controlling effective introns for multi-agent learning by Genetic Programming
Hitoshi Iba, Makoto Terao · 2000
This paper presents the emergence of the cooperative behavior for multiple agents by means of Genetic Programming (GP). For the purpose of evolving the e#ective 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. 1 Introduction Recently intelligent agents and multi-agent systems have attracted much interest in Distributed Artificial Intelligence (DAI). GP and its variants have been applied to the multi-agent learning (see [Haynes et al.95],[Luke et al.96][Iba96],[Hara et al.99] for example). However, in the multi-agent application of GP, the computational burden is often problematic. This i...