Learning and applying competitive strategies

Authors:
Esther Lock;Susan L. Epstein
Affiliations:
Hunter College and The Graduate Center of The City University of New York, Department of Computer Science, New York, New York;Hunter College and The Graduate Center of The City University of New York, Department of Computer Science, New York, New York
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

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Abstract

Learning reusable sequences can support the development of expertise in many domains, either by improving decision-making quality or decreasing execution speed. This paper introduces and evaluates a method to learn action sequences for generalized states from prior problem experience. From experienced sequences, the method induces the context that underlies a sequence of actions. Empirical results indicate that the sequences and contexts learned for a class of problems are actually those deemed important by experts for that particular class, and can be used to select appropriate action sequences when solving problems there.