CSM: A computational model of cumulative learning

摘要

This paper presents a description and an empirical evaluation of a rule-based, cumulative learning system called CSM (classifier system with memory), tested in the robot navigation domain. The significance of this research is to augment the current model of classifier systems with analogical problem solving capabilities and chunking mechanisms. The present investigation focuses on knowledge acquisition, learning by analogy, and knowledge retention. Experimental results are presented that exhibit forms of intelligent behavior not yet observed in classified systems and expert systems.