Knowledge represented as generalizations is insufficient for problem solving in many domains,such as legal reasoning, because of a gap between the language of case-descriptions and the language in which generalizations are expressed, and because of the graded structure of domain categories. Exemplar-based representation addresses these problems, but accurate assessment of similarity between an exemplar of a category and a new case requires reasoning both with general domain theory and with the explanation of the exemplar's membership in the category. G R E B E is a system that integrates generalizations and exemplars in a cooperative manner. Exemplar-based explanations are used to bridge the gap between case-descriptions and generalizations, and domain theory in the form of general rules and specific explanations is used to explain the equivalence of new cases to exemplars.

In domains requiring complex relational representations, simply expressing a new problem may be a complex, error-prone, and time-consuming task. This paper presents an approach to problem formulation, termed case-based formulation (CBF), that uses previous cases as a model and a guide for expressing new cases. By expressing new problems in terms of old, C B F can potentially increase the speed and accuracy of problem formulation, reduce the computational expense of retrieval, and determine the relevant similarities and differences between a new case and and the most similar old cases as a side-effect of expressing the new case. Three forms of C B F can be distinguished by the extent to which the retrieval and adaptation of previous cases are automated and the extent to which the facts of multiple cases Can be combined. A n initial implementation of one form of C B F is described and its ability to use previous cases to increase the efficiency and accuracy of new-case formalization is illustrated with a complex relational case.

This paper presents a model of the use of expert knowledge to improve accuracy of aneJog retrieval. This model, match refinement by structural difference links ( M R S D L ) , is based upon the assumption that expertise in domains requiring analogicad reasoning consists in part of knowledge of the structural similarities and differences between some pairs of the source analogs. In an empirical evaluation on four data sets, M R S D L consistently retrieved the most similar or nearly most similar source analog. Achieving comparable accuracy on these data sets with a two-stage retrieval technique such as M A C / F A C would require exhaustive matching with more than half of the source anedogs. The evaluation also showed that parallel competitive matching is often substantially faster than exhaustive matching or M R S D L .