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Constraint programming is a technology which is now widely used to solve combinatorial problems in industrial applications. However, using it requires considerable knowledge and expertise in the field of constraint reasoning. This paper introduces a framework for automatically learning constraint networks from sets of instances that are either acceptable(More)
In this paper we view interactive constraint acquisition as the process of learning constraints from examples and focus on the roles played by both the user and the system during an interactive session. We consider our user as a teacher who provides positive examples to an automated constraint acquisition system. Each positive example represents a solution(More)
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