{"pk":30769,"title":"Integrating Generalizations with Exemplar-Based Reasoning","subtitle":null,"abstract":"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.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Paper Presentations","is_remote":true,"remote_url":"https://escholarship.org/uc/item/5hw6s0kn","frozenauthors":[{"first_name":"L.","middle_name":"Karl","last_name":"Branting","name_suffix":"","institution":"University of Texas","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1989-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/30769/galley/20618/download/"}]}