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Autonomous search in constraint satisfaction via black hole: A performance evaluation using different choice functions

dc.contributor.authorSoto R.
dc.contributor.authorCrawford B.
dc.contributor.authorOlivares R.
dc.contributor.authorNiklander S.
dc.contributor.authorOlguín E.
dc.date.accessioned2020-09-02T22:28:44Z
dc.date.available2020-09-02T22:28:44Z
dc.date.issued2016
dc.identifier10.1007/978-3-319-41000-5_6
dc.identifier.citation9712 LNCS, , 56-65
dc.identifier.issn03029743
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6325
dc.descriptionAutonomous Search is a modern technique aimed at introducing self-adjusting features to problem-solvers. In the context of constraint satisfaction, the idea is to let the solver engine to autonomously replace its solving strategies by more promising ones when poor performances are identified. The replacement is controlled by a choice function, which takes decisions based on information collected during solving time. However, the design of choice functions can be done in very different ways, leading of course to very different resolution processes. In this paper, we present a performance evaluation of 16 rigorously designed choice functions. Our goal is to provide new and interesting knowledge about the behavior of such functions in autonomous search architectures. To this end, we employ a set of well-known benchmarks that share general features that may be present on most constraint satisfaction and optimization problems. We believe this information will be useful in order to design better autonomous search systems for constraint satisfaction. © Springer International Publishing Switzerland 2016.
dc.language.isoen
dc.publisherSpringer Verlag
dc.subjectAutonomous search
dc.subjectChoice functions
dc.subjectConstraint programming
dc.subjectConstraint satisfaction
dc.subjectOptimization
dc.subjectComputer programming
dc.subjectConstraint theory
dc.subjectCurricula
dc.subjectFunction evaluation
dc.subjectOptimization
dc.subjectSearch engines
dc.subjectAutonomous searches
dc.subjectChoice function
dc.subjectConstraint programming
dc.subjectConstraint Satisfaction
dc.subjectDifferent resolutions
dc.subjectModern techniques
dc.subjectOptimization problems
dc.subjectPoor performance
dc.subjectConstraint satisfaction problems
dc.titleAutonomous search in constraint satisfaction via black hole: A performance evaluation using different choice functions
dc.typeBook Chapter


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