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Evaluating the Effectiveness of Exploration and Accumulated Experience in Automatic Case Elicitation

  • Jay H. Powell
  • Brandon M. Hauff
  • John D. Hastings
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3620)

Abstract

Non-learning problem solvers have been applied to many interesting and complex domains. Experience-based learning techniques have been developed to augment the capabilities of certain non-learning problem solvers in order to improve overall performance. An alternative approach to enhancing pre-existing systems is automatic case elicitation, a learning technique in which a case-based reasoning system with no prior domain knowledge acquires knowledge automatically through real-time exploration and interaction with its environment. In empirical testing in the domain of checkers, results suggest not only that experience can substitute for the inclusion of pre-coded model-based knowledge, but also that the ability to explore is crucial to the performance of automatic case elicitation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jay H. Powell
    • 1
  • Brandon M. Hauff
    • 2
  • John D. Hastings
    • 1
  1. 1.Dept. of Computer Science & Information SystemsUniversity of Nebraska at KearneyKearneyU.S.A
  2. 2.Dept. of Computer Science & EngineeringUniversity of Nebraska at LincolnLincolnU.S.A

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