Experiments on a Pittsburgh-style fuzzy classifier system for mobile robotics

We report on experiments designed to highlight the strengths and weaknesses of an autonomous rule acquisition algorithm for the fuzzy controller of a simulated mobile robot. The algorithm is a Pittsburgh-style fuzzy classifier system. The fuzzy classifier system paradigm is an elegant and versatile combination of evolutionary and lifetime reinforcement learning based on an underlying fuzzy logic structure. It possesses a powerful potential to be a general-purpose linguistically interpretable problem-solver for continuous real-valued domains. We have tested the performance and robustness of many controllers using this approach, a sample of which, are presented here. We find that, although the robot controllers can often be quite robust to environmental changes after learning, they also sometimes display critical weaknesses in certain scenarios. We also compare performance of this algorithm with that of a hand-coded fuzzy controller. We find that it is more difficult than one would initially expect to derive a set of hand-coded rules that are as versatile as the best of those acquired by the fuzzy classifier system. It is worth noting that the task to be learned here is itself quite simple, there are far more complex problems within the mobile robot domain, thus providing a forward impetus to the work.