Proceedings of the Annual Meeting of the Cognitive Science Society

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.