Adaptive Similarity Assessment for Case-Based Explanation

Guiding the generation of abductive explanations is a di cult problem. Applying casebased reasoning to abductive explanation generation|generating new explanations by retrieving and adapting explanations for prior episodes|o ers the bene t of re-using successful explanatory reasoning but raises new issues concerning how to perform similarity assessment to judge the relevance of prior explanations to new situations. Similarity assessment a ects two points in the case-based explanation process: deciding which explanations to retrieve and evaluating the retrieved candidates. We address the problem of identifying similar explanations to retrieve by basing that similarity assessment on a categorization of anomaly types. We show that the problem of evaluating retrieved candidate explanations is often impeded by incomplete information about the situation to be explained, and address that problem with a novel similarity assessment method which we call constructive similarity assessment. Constructive similarity assessment contrasts with traditional \feature-mapping" similarity assessment methods by using the contents of memory to hypothesize important features in the new situation, and in using a pragmatic criterion|the system's ability to adapt features of the old case into features that apply in the new circumstances|as the basis for comparing features. Thus constructive similarity assessment does not merely compare new cases to old; instead, based on adaptation of prior cases in memory, it addresses the problem of incomplete input cases by building up and reasoning about augmented descriptions of those cases.