Heuristic-based learning

  • Stuart H. Rubin
Track 2: Artificial Intelligence
Part of the Lecture Notes in Computer Science book series (LNCS, volume 507)


Knowledge-based systems are becoming increasingly model oriented. Models enable the system a deeper understanding — something which is impractical to attain when all the system has are rules. Furthermore, it has become apparent that knowledge representations must become increasingly domain-specific in order to facilitate more sophisticated problem solving. The task of automating the solution of sophisticated problems in turn implies the use of analogic reasoning towards the goal of automatic knowledge acquisition.

The approach taken here is to investigate new machine learning algorithms focusing on lateral model-based transformative induction methods similar to Quinlan's ID3 and Michalski's AQ algorithms — except that models are the generalized object(s) rather than simply decision trees or rules.


Case-based reasoning distributed computation machine learning transformation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Stuart H. Rubin
    • 1
  1. 1.Central Michigan UniversityUSA

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