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Conference Paper: Constraint Networks: A Survey

TitleConstraint Networks: A Survey
Authors
KeywordsComputers
Cybernetics
Issue Date1997
PublisherIEEE.
Citation
Computational Cybernetics and Simulation, IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, Orlando, Florida, USA, 12-15 October 1997, v. 2, p. 1930-1935 How to Cite?
AbstractA constraint satisfaction problem (CSP) involves a set of variables, a domain of potential values for each variable, and a set of constraints, which specifies the acceptable combinations of values. One popular approach is to represent the original problem as a constraint network where nodes represent variables and arcs represent constraints between variables. Node consistency and arc consistency techniques are first applied to prune the domains of variables. Constraint propagation techniques are then applied to solve the problem. Many AI and engineering problems can be formulated as CSPs and solved by various CSP algorithms such as constraint propagation, backtracking, forward checking, and hybrids. This paper gives an overview of these algorithms. In particular, we present a review of the interval constraint satisfaction problems. Real intervals or sets of discrete values label the variables. The constraint can be binary relationships or n-ary mathematical operations. The techniques for solving the interval constraint satisfaction problem such as Waltz filtering and tolerance propagation are presented.
Persistent Identifierhttp://hdl.handle.net/10722/45589
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYang, CCCen_HK
dc.contributor.authorYang, MHen_HK
dc.date.accessioned2007-10-30T06:29:49Z-
dc.date.available2007-10-30T06:29:49Z-
dc.date.issued1997en_HK
dc.identifier.citationComputational Cybernetics and Simulation, IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, Orlando, Florida, USA, 12-15 October 1997, v. 2, p. 1930-1935en_HK
dc.identifier.issn1062-922Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/45589-
dc.description.abstractA constraint satisfaction problem (CSP) involves a set of variables, a domain of potential values for each variable, and a set of constraints, which specifies the acceptable combinations of values. One popular approach is to represent the original problem as a constraint network where nodes represent variables and arcs represent constraints between variables. Node consistency and arc consistency techniques are first applied to prune the domains of variables. Constraint propagation techniques are then applied to solve the problem. Many AI and engineering problems can be formulated as CSPs and solved by various CSP algorithms such as constraint propagation, backtracking, forward checking, and hybrids. This paper gives an overview of these algorithms. In particular, we present a review of the interval constraint satisfaction problems. Real intervals or sets of discrete values label the variables. The constraint can be binary relationships or n-ary mathematical operations. The techniques for solving the interval constraint satisfaction problem such as Waltz filtering and tolerance propagation are presented.en_HK
dc.format.extent579328 bytes-
dc.format.extent3082 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectComputersen_HK
dc.subjectCyberneticsen_HK
dc.titleConstraint Networks: A Surveyen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1062-922X&volume=2&spage=1930&epage=1935&date=1997&atitle=Constraint+Networks:+A+Surveyen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICSMC.1997.638351en_HK
dc.identifier.hkuros31119-
dc.identifier.citeulike3545005-

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