Evaluating the effectiveness of exploration and accumulated experience in automatic case elicitation

  • Authors:

  • Jay H. Powell;Brandon M. Hauff;John D. Hastings

  • Affiliations:

  • Dept. of Computer Science & Information Systems, University of Nebraska at Kearney, Kearney, NE;Dept. of Computer Science & Engineering, University of Nebraska at Lincoln, Lincoln, NE;Dept. of Computer Science & Information Systems, University of Nebraska at Kearney, Kearney, NE

  • Venue:
  • ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
  • Year:
  • 2005

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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.