{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T18:46:33Z","timestamp":1648665993688},"reference-count":24,"publisher":"Cambridge University Press (CUP)","issue":"4","license":[{"start":{"date-parts":[[2006,11,9]],"date-time":"2006-11-09T00:00:00Z","timestamp":1163030400000},"content-version":"unspecified","delay-in-days":8,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2006,11]]},"abstract":"<jats:p>In constraint-based design, components are modeled by variables \ndescribing their properties and subject to physical or mechanical \nconstraints. However, some other constraints are difficult to represent, \nlike comfort or user satisfaction. Partially defined constraints can be \nused to model the incomplete knowledge of a concept or a relation. Instead \nof only computing with the known part of the constraint, we propose to \ncomplete its definition by using machine-learning techniques. Because \nconstraints are actively used during solving for pruning domains, building \na classifier for instances is not enough: we need a solver able to reduce \nvariable domains. Our technique is composed of two steps: first we learn a \nclassifier for the constraint's projections and then we transform the \nclassifier into a propagator. We show that our technique not only has good \nlearning performances but also yields a very efficient solver for the \nlearned constraint.<\/jats:p>","DOI":"10.1017\/s0890060406060227","type":"journal-article","created":{"date-parts":[[2006,11,9]],"date-time":"2006-11-09T20:24:31Z","timestamp":1163103871000},"page":"297-311","source":"Crossref","is-referenced-by-count":1,"title":["Partially defined constraints in constraint-based design"],"prefix":"10.1017","volume":"20","author":[{"given":"ARNAUD","family":"LALLOUET","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ANDRE\u00cf","family":"LEGTCHENKO","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2006,11,9]]},"reference":[{"key":"S0890060406060227_ref019","doi-asserted-by":"publisher","DOI":"10.1023\/B:CONS.0000049206.43218.5f"},{"key":"S0890060406060227_ref009","unstructured":"Coletta, R. , Bessi\u00e8re, C. , O'Sullivan, B. , Freuder, E.C. , O'Connell, S. , & Quinqueton, J. 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Menlo Park, CA: AAAIPress."},{"key":"S0890060406060227_ref017","unstructured":"Moore, R.E. (1966).Interval Analysis.Englewood Cliffs, NJ:Prentice Hall."},{"key":"S0890060406060227_ref003","doi-asserted-by":"crossref","unstructured":"Apt, K.R. (1999).The essence of constraint propagation.Theoretical Computer Science 221(1\u20132),179\u2013210.","DOI":"10.1016\/S0304-3975(99)00032-8"},{"key":"S0890060406060227_ref018","unstructured":"O'Sullivan, B. (2002).Constraint-Aided Conceptual Design.London:Professional Engineering Publishing."},{"key":"S0890060406060227_ref007","unstructured":"Bessi\u00e8re, C. & R\u00e9gin, J.-C. (1997).Arc-consistency for general constraint networks: preliminaryresults. InIJCAI, pp.398\u2013404, Nagoya, San Francisco, CA: MorganKaufmann."},{"key":"S0890060406060227_ref005","unstructured":"Bessi\u00e8re, C. , Coletta, R. , Freuder, E.C. , & O'Sullivan, B. (2004).Leveraging the learning power of examples in automated constraintacquisition. 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