| 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. Based on their research, 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.; However, in this thesis, 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 often is much closer. 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 similarity metrics in order to guarantee the best case base coverage. We demonstrate through case-based planning how to construct high-quality similarity metrics that lead to highly competent case bases, and discuss various implications of our result in practical implementation of case base maintenance systems. |
