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{ "pk": 33109, "title": "Retrieval and Learning in Analogical Problem Solving", "subtitle": null, "abstract": "Eureka is a problem-solving system that operates through \na form of analogical reasoning. The system was designed to \nstudy how relatively low-level memory, reasoning, and learning mechanisms can account for high-level learning in human \nproblem solvers. Thus, Eureka's design has focused on issues of memory representation and retrieval of analogies, at the \nexpense of complex problem-solving ability or sophisticated \nanalogical elaboration techniques. Two computational systems \nfor analogical reasoning, ARCS/ACM E and MAC/FAC, are \nrelatively powerful and well-known in the cognitive science literature. However, they have not addressed issues of learning, \nand they have not been implemented in the context of a performance task that can dictate what makes an analogy \"good\". \nThus, it appears that these different research directions have \nmuch to offer each other W e describe the Eureka system \nand compare its analogical retrieval mechanism with those in \nARC S and MAC/FAC. W e then discuss the issues involved in \nincorporating ARC S and MAC/FAC into a learning problem \nsolver such as Eureka.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [], "section": "17", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/23f6x8c5", "frozenauthors": [ { "first_name": "Randolph", "middle_name": "M .", "last_name": "Jones", "name_suffix": "", "institution": "University of Michigan", "department": "" }, { "first_name": "Pat", "middle_name": "", "last_name": "Langley", "name_suffix": "", "institution": "Stanford University", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "1995-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/33109/galley/24170/download/" } ] }