A Fuzzy Classifier System for evolutionary learning of robot behaviors

摘要

This paper presents an evolutionary learning of robot behaviors by Fuzzy Classifier System (FCS). The FCS introduces a fuzzy rule base and a fuzzy inference system in place of the rule base and the production system of the Classifier System (CS). The FCS has a feature in that it is robust to environmental changes. The FCS is applied to behavioral learning of a mobile robot for evolving fuzzy control rules. Simulations are done under eight different conditions. Robustness of the acquired fuzzy rules is compared to that of the rules obtained by the CS.