Remembering to add: competence-preserving case-addition policies for case-base maintenance

  • Authors:

  • Jim Zhu;Qiang Yang

  • Affiliations:

  • School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada;School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada

  • Venue:
  • IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
  • Year:
  • 1999

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Abstract

Case-base maintenance is gaining increasing recognition in research and the practical applications of case-based reasoning (CBR). This intense interest is highlighted by Smyth and Keane's research on case deletion policies. In their work, Smyth and Keane advocated a case deletion policy, whereby the cases in a case base are classified and deleted based on their coverage potential and adaptation power. The algorithm was empirically shown to improve the competence of a CBR system and outperform a number of previous deletion-based strategies. In this paper, we present a different case-base maintenance policy that is based on case addition rather than deletion. The advantage of our algorithm is that we can place a lower bound on the competence of the resulting case base; we demonstrate that the coverage of the computed case base cannot be worse than the optimal case base in coverage by a fixed lower bound, and the coverage is often much closer to optimum. We also show that the Smyth and Keane's deletion based policy cannot guarantee any such lower bound. Our result highlights the importance of finding the right case-base maintenance algorithm in order to guarantee the best case-base coverage. We demonstrate the effectiveness of our algorithm through an experiment in case-based planning.