On Retention of Adaptation Rules.

The difficulty of acquiring case adaptation knowledge is a classic problem for case-based reasoning (CBR). One method for addressing this problem is to use the cases in the case base as data from which to learn adaptation rules. For numeric prediction tasks, adaptation rules have been successfully learned from the case base by using the case difference heuristic, which generates rules based on comparisons of pairs of cases. However, because the case difference heuristic could potentially generate a rule for each pair of cases in the case base, controlling growth of adaptation rules is potentially an even more acute problem than controlling case base growth. This raises the question of how to select adaptation rules to retain. The ability to generate adaptation rules from cases also raises questions about the relative benefit of learning cases, learning the adaptation rules generated from them, or learning both. This paper proposes and evaluates a new adaptation rule retention approach and presents a case study assessing the relative benefits of learning cases versus learning adaptation rules derived from the cases, at different points in the growth of the case base.