Interactive classifier system for real robot learning

In this paper, toe describe a fast leaning method for a mobile robot which acquires autononous behaviors from interaction between a human and a robot. We develop a behavior learning method ICS (Interactive Classifier System) using evolutionary computation and a mobile robot is cable to quickly lean rules so Mat a human operator can directly teach a physical robot. Also the ICS is a novel evolutionary robotics approach using an adaptive classifier system to environmental changes. The ICS has two major charateristics for evolutionary robotics. For one thing, it can speedup leaning by means of generating initial individuals front human-robot interaction. For another, it is a kind of incremental learning methods which adds new acquired rules to priori knowledge by teaching from human-robot interaction at any time.