A preliminary study of SOAR, a general intelligent architecture for automated problem solving and learning, is presented. The underlying principles of universal subgoaling and chunking were applied to a simple, yet representative, problem in artificial intelligence. A number of problem space representations were examined and compared. It is concluded that learning is an inherent and beneficial aspect of problem solving. Additional studies are suggested in domains relevant to mission planning and to SOAR itself.


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    Title :

    Production system chunking in SOAR: Case studies in automated learning


    Contributors:

    Published in:

    Publication date :

    1989-02-01


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English


    Keywords :



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