Constraint acquisition
Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user...
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| Published in: | Artificial intelligence Vol. 244; pp. 315 - 342 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Amsterdam
Elsevier B.V
01.03.2017
Elsevier Science Ltd Elsevier |
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| ISSN: | 0004-3702, 1872-7921 |
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| Abstract | Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user in the modeling task. This paper presents the basic architecture for acquiring constraint networks from examples classified by the user. The theoretical questions raised by constraint acquisition are stated and their complexity is given. We then propose Conacq, a system that uses a concise representation of the learner's version space into a clausal formula. Based on this logical representation, our architecture uses strategies for eliciting constraint networks in both the passive acquisition context, where the learner is only provided a pool of examples, and the active acquisition context, where the learner is allowed to ask membership queries to the user. The computational properties of our strategies are analyzed and their practical effectiveness is experimentally evaluated. |
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| AbstractList | Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user in the modeling task. This paper presents the basic architecture for acquiring constraint networks from examples classified by the user. The theoretical questions raised by constraint acquisition are stated and their complexity is given. We then propose Conacq, a system that uses a concise representation of the learner's version space into a clausal formula. Based on this logical representation, our architecture uses strategies for eliciting constraint networks in both the passive acquisition context, where the learner is only provided a pool of examples, and the active acquisition context, where the learner is allowed to ask membership queries to the user. The computational properties of our strategies are analyzed and their practical effectiveness is experimentally evaluated. Constraint programming is used to model and solve complex combina- torial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user in the modelling task. This paper presents the basic architecture for acquiring constraint networks from examples classified by the user. The theoretical questions raised by constraint acquisition are stated and their complexity is given. We then propose Conacq, a sys- tem that uses a concise representation of the learner’s version space into a clausal formula. Based on this logical representation, our architecture uses strategies for eliciting constraint networks in both the passive acquisition context, where the learner is only provided a pool of examples, and the active acquisition context, where the learner is allowed to ask membership queries to the user. The computational properties of our strategies are analyzed and their practical effectiveness is experimentally evaluated. |
| Author | O'Sullivan, Barry Lazaar, Nadjib Bessiere, Christian Koriche, Frédéric |
| Author_xml | – sequence: 1 givenname: Christian surname: Bessiere fullname: Bessiere, Christian email: bessiere@lirmm.fr organization: University of Montpellier, France – sequence: 2 givenname: Frédéric surname: Koriche fullname: Koriche, Frédéric organization: University of Artois, France – sequence: 3 givenname: Nadjib surname: Lazaar fullname: Lazaar, Nadjib organization: University of Montpellier, France – sequence: 4 givenname: Barry surname: O'Sullivan fullname: O'Sullivan, Barry organization: University College Cork, Ireland |
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| Cites_doi | 10.1016/j.tcs.2003.11.004 10.1016/0004-3702(82)90040-6 10.1016/j.artint.2003.04.003 10.1006/jcss.1996.0021 10.1016/0004-3702(88)90002-1 10.1007/s10601-006-9010-8 10.1145/1968.1972 10.1016/S0304-3975(97)86737-0 10.1007/978-3-540-68856-3 10.1006/jcss.1995.1075 10.1023/A:1007361123060 10.1038/22055 10.1016/0743-1066(84)90014-1 |
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| Keywords | Constraint learning Constraint programming |
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| SubjectTerms | Artificial Intelligence Combinatorial analysis Complexity Computer architecture Computer Science Constraint learning Constraint programming Theory of constraints |
| Title | Constraint acquisition |
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