First results from experiments in fuzzy classifier system architectures for mobile robotics

Abstract

We present first results from a comparison between a Fuzzy Classifier System operating at the level of whole rule-bases, and three variants of one that operates at the level of individual rules. The application domain is mobile robotics, and the problem is autonomous acquisition of an “investigative” obstacle avoidance competency. The Fuzzy Classifier Systems operate on the rules of fuzzy controllers with pre-defined fuzzy membership functions. Generally, all of the methods used were capable of producing fuzzy controllers with competencies that exceeded that of a simple hand-coded fuzzy controller that we had devised. The approach operating at the level of whole rule-bases yielded more robust and stable convergence on high performance solutions than any other architecture presented here. It is clear from the results that more work needs to be done to unravel the disappointing convergence dynamics of the algorithms operating at the level of individual rules.