Experiential solving: Towards a unified autonomous search constraint solving approach

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dc.contributor.authorCrawford, B.es_ES
dc.contributor.authorSoto, R.es_ES
dc.contributor.authorCrawford, K.es_ES
dc.contributor.authorJohnson, F.es_ES
dc.contributor.authorde la Barra, C.L.es_ES
dc.contributor.authorGaldames, S.es_ES
dc.date.accessioned6/22/2022 14:20
dc.date.accessioned2022-06-22T20:03:34Z
dc.date.available6/22/2022 14:20
dc.date.available2022-06-22T20:03:34Z
dc.date.issued2015
dc.description.abstractTo 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.es_ES
dc.formatapplication/pdfes_ES
dc.identifier.doi10.1007/978-3-319-21380-4_97es_ES
dc.identifier.urihttps://doi.org/10.1007/978-3-319-21380-4_97
dc.language.isoen_USes_ES
dc.publisherSpringer Verlages_ES
dc.publisher.countryDEes_ES
dc.relation.ispartofCommunications in Computer and Information Sciencees_ES
dc.rightsinfo:eu-repo/semantics/closedAccesses_ES
dc.sourceCommunications in Computer and Information Sciencees_ES
dc.subjectComputer Sciencees_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#2.02.04es_ES
dc.titleExperiential solving: Towards a unified autonomous search constraint solving approaches_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
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