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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Bibliographic Details
Published in:Artificial intelligence Vol. 244; pp. 315 - 342
Main Authors: Bessiere, Christian, Koriche, Frédéric, Lazaar, Nadjib, O'Sullivan, Barry
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.03.2017
Elsevier Science Ltd
Elsevier
Subjects:
Artificial Intelligence
Combinatorial analysis
Complexity
Computer architecture
Computer Science
Constraint learning
Constraint programming
Theory of constraints
Constraint learning
Constraint programming
ISSN:0004-3702, 1872-7921
Online Access:Get full text
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Summary: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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ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2015.08.001