Learning constraints through partial queries
Learning constraint networks is known to require a number of membership queries exponential in the number of variables. In this paper, we learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative...
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| Veröffentlicht in: | Artificial intelligence Jg. 319; S. 103896 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.06.2023
Elsevier |
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| ISSN: | 0004-3702, 1872-7921 |
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| Abstract | Learning constraint networks is known to require a number of membership queries exponential in the number of variables. In this paper, we learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm, called QuAcq2, that, given a negative example, elucidates a constraint of the target network in a number of queries logarithmic in the size of the example. The whole constraint network can then be learned with a polynomial number of partial queries. We give information theoretic lower bounds for learning some simple classes of constraint networks and show that our generic algorithm is optimal in some cases. We provide a version of QuAcq2 with a cutoff mechanism that controls the time to generate a query. Our experiments illustrate the good behavior of QuAcq2 in practice, especially in the case where QuAcq2 is executed to learn the missing constraints in a partially filled constraint model. Our experiments also show that QuAcq2 requires significantly fewer queries to learn a network than its predecessor QuAcq1. |
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| AbstractList | Learning constraint networks is known to require a number of membership queries exponential in the number of variables. In this paper, we learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm, called QuAcq2, that, given a negative example, elucidates a constraint of the target network in a number of queries logarithmic in the size of the example. The whole constraint network can then be learned with a polynomial number of partial queries. We give information theoretic lower bounds for learning some simple classes of constraint networks and show that our generic algorithm is optimal in some cases. We provide a version of QuAcq2 with a cutoff mechanism that controls the time to generate a query. Our experiments illustrate the good behavior of QuAcq2 in practice, especially in the case where QuAcq2 is executed to learn the missing constraints in a partially filled constraint model. Our experiments also show that QuAcq2 requires significantly fewer queries to learn a network than its predecessor QuAcq1. |
| ArticleNumber | 103896 |
| Author | Lazaar, Nadjib Walsh, Toby Hebrard, Emmanuel Quimper, Claude-Guy Dries, Anton Tsouros, Dimosthenis C. Bessiere, Christian Katsirelos, George Narodytska, Nina Carbonnel, Clément Stergiou, Kostas |
| Author_xml | – sequence: 1 givenname: Christian orcidid: 0000-0003-4059-6403 surname: Bessiere fullname: Bessiere, Christian email: bessiere@lirmm.fr organization: CNRS, University of Montpellier, France – sequence: 2 givenname: Clément orcidid: 0000-0003-2312-2687 surname: Carbonnel fullname: Carbonnel, Clément organization: CNRS, University of Montpellier, France – sequence: 3 givenname: Anton surname: Dries fullname: Dries, Anton organization: Nokia Bell Labs, Belgium – sequence: 4 givenname: Emmanuel orcidid: 0000-0003-3131-0709 surname: Hebrard fullname: Hebrard, Emmanuel organization: LAAS-CNRS, Toulouse, France – sequence: 5 givenname: George orcidid: 0000-0002-3727-6698 surname: Katsirelos fullname: Katsirelos, George organization: Université Paris-Saclay, INRAE, AgroParisTech, UMR MIA Paris-Saclay, France – sequence: 6 givenname: Nadjib surname: Lazaar fullname: Lazaar, Nadjib organization: LIRMM, University of Montpellier, CNRS, Montpellier, France – sequence: 7 givenname: Nina surname: Narodytska fullname: Narodytska, Nina organization: VMware Research, USA – sequence: 8 givenname: Claude-Guy surname: Quimper fullname: Quimper, Claude-Guy organization: Université Laval, Quebec City, Canada – sequence: 9 givenname: Kostas surname: Stergiou fullname: Stergiou, Kostas organization: University of Western Macedonia, Kozani, Greece – sequence: 10 givenname: Dimosthenis C. surname: Tsouros fullname: Tsouros, Dimosthenis C. organization: University of Western Macedonia, Kozani, Greece – sequence: 11 givenname: Toby surname: Walsh fullname: Walsh, Toby organization: UNSW Sydney, Australia |
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| Keywords | Active learning Constraint programming Constraint acquisition |
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| Title | Learning constraints through partial queries |
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