Learning to integrate multiple knowledge sources for case-based reasoning

  • Authors:

  • David B. Leake;Andrew Kinley;David Wilson

  • Affiliations:

  • Computer Science Department, Indiana University, Bloomington, IN;Computer Science Department, Indiana University, Bloomington, IN;Computer Science Department, Indiana University, Bloomington, IN

  • Venue:
  • IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
  • Year:
  • 1997

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Abstract

The case based reasoning process depends on multiple overlapping knowledge sources, each of which provides an opportunity for learning. Exploiting these opportunities requires not only determining the learning mechanisms to use for each individual knowledge source, but also how the different learning mechanisms interact and their combined utility. This paper presents a case study examining the relative contributions and costs involved in learning processes for three different knowledge sources--cases, case adaptation knowledge, and similarity information--in a casebased planner. It demonstrates the importance of interactions between different learning processes and identifies a promising method for integrating multiple learning methods to improve case-based reasoning.