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Machine Learning

, Volume 5, Issue 4, pp 383–406 | Cite as

CSM: A computational model of cumulative learning

  • Hayong Harry Zhou
Article

Abstract

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.

Key words

Long-term memory cumulative learning classifier systems learning by analogy 

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Copyright information

© Kluwer Academic Publishers 1990

Authors and Affiliations

  • Hayong Harry Zhou
    • 1
  1. 1.Department of Computer and Information Sciences, College of Natural and Mathematical SciencesTowson State UniversityTowson

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