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Conceptual learning and classifier systems with long-term memory

This paper presents a description and an empirical evaluation of a classifier system with longterm memory. It is a rule-based, conceptual learning system tested in a letter prediction domain. The present investigation focuses on knowledge adaptation, rule construction, and conceptual reasoning. Experimental results are presented that exhibit forms of intelligent behavior not generally observed in classical classifier systems and expert systems.