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Experiential solving: Towards a unified autonomous search constraint solving approach

dc.contributor.authorCrawford B.
dc.contributor.authorSoto R.
dc.contributor.authorCrawford K.
dc.contributor.authorJohnson F.
dc.contributor.authorde la Barra C.L.
dc.contributor.authorGaldames S.
dc.date.accessioned2020-09-02T22:15:36Z
dc.date.available2020-09-02T22:15:36Z
dc.date.issued2015
dc.identifier10.1007/978-3-319-21380-4_97
dc.identifier.citation528, , 573-577
dc.identifier.issn18650929
dc.identifier.urihttps://hdl.handle.net/20.500.12728/4127
dc.descriptionTo solve many problems modeled as Constraint Satisfaction Problems there are no known efficient algorithms. The specialized literature offers a variety of solvers, which have shown good performance. Nevertheless, despite the efforts of the scientific community in developing new strategies, there is no algorithm that is the best for all possible situations. This paper analyses recent developments of Autonomous Search Constraint Solving Systems. Showing that the design of the most efficient and recent solvers is very close to the Experiential Learning Cycle from organizational psychology. © Springer International Publishing Switzerland 2015.
dc.language.isoen
dc.publisherSpringer Verlag
dc.sourceStephanidis C.
dc.subjectAutonomous search
dc.subjectExperiential learning
dc.subjectMetaheuristics
dc.subjectProblem solving
dc.subjectAlgorithms
dc.subjectCodes (symbols)
dc.subjectConstraint satisfaction problems
dc.subjectHuman computer interaction
dc.subjectLogic programming
dc.subjectAutonomous searches
dc.subjectConstraint Solving
dc.subjectConstraint solving system
dc.subjectExperiential learning
dc.subjectMeta heuristics
dc.subjectOrganizational psychology
dc.subjectPaper analysis
dc.subjectScientific community
dc.subjectProblem solving
dc.titleExperiential solving: Towards a unified autonomous search constraint solving approach
dc.typeConference Paper


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