A fundamental problem for case-based reasoning systems is how to select relevant prior cases. Numerous strategies have been developed for determining the similarity of prior cases, given full descriptions of the problem at hand, and situation assessment methods have been developed for formulating appropriate initial case descriptions. However, in real-world applications, attempting to determine all relevant features of a new problem before retrieval may be impractical or impossible. Consequently, how to guide retrieval based on partial problem descriptions is an important question for CBR. This paper examines the problem of assessing similarity in partially-described cases. It proposes a set of similarity assessment strategies for handling missing information, evaluates their performance and efficiency on sample data sets, and discusses their tradeoffs.

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