Learning problem solving strategies using refinement and macro generation

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dc.citation.epage243en_US
dc.citation.issueNumber1-2en_US
dc.citation.spage209en_US
dc.citation.volumeNumber44en_US
dc.contributor.authorGüvenir, H. A.en_US
dc.contributor.authorErnst, G. W.en_US
dc.date.accessioned2016-02-08T10:56:33Zen_US
dc.date.available2016-02-08T10:56:33Zen_US
dc.date.issued1990en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractIn this paper we propose a technique for learning efficient strategies for solving a certain class of problems. The method, RWM, makes use of two separate methods, namely, refinement and macro generation. The former is a method for partitioning a given problem into a sequence of easier subproblems. The latter is for efficiently learning composite moves which are useful in solving the problem. These methods and a system that incorporates them are described in detail. The kind of strategies learned by RWM are based on the GPS problem solving method. Examples of strategies learned for different types of problems are given. RWM has learned good strategies for some problems which are difficult by human standards. © 1990.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T10:56:33Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 1990en
dc.identifier.doi10.1016/0004-3702(90)90102-6en_US
dc.identifier.issn0004-3702
dc.identifier.urihttp://hdl.handle.net/11693/26215en_US
dc.language.isoEnglishen_US
dc.publisherElsevier BVen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/0004-3702(90)90102-6en_US
dc.source.titleArtificial Intelligenceen_US
dc.subjectLearning Systemsen_US
dc.subjectMacro Generationen_US
dc.subjectProblem Solving Strategiesen_US
dc.subjectRefinementen_US
dc.subjectArtificial Intelligenceen_US
dc.titleLearning problem solving strategies using refinement and macro generationen_US
dc.typeArticleen_US

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