{"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/"}]}