Abstract
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 solutions or non-desirable assignments of the problem we would like to express. Such an approach has the potential to be of assistance to a novice who is trying to articulate her constraints. By restricting the language of constraints used to build the network, this could also assist an expert to develop an efficient model of a given problem.
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The collaboration between LIRMM and the Cork Constraint Computation Centre is supported by a Ulysses Travel Grant from Enterprise Ireland, the Royal Irish Academy and CNRS (Grant Number FR/2003/022). This work has also received support from Science Foundation Ireland under Grant 00/PI.1/C075.
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References
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Coletta, R., Bessière, C., O’Sullivan, B., Freuder, E.C., O’Connell, S., Quinqueton, J. (2003). Semi-automatic Modeling by Constraint Acquisition. In: Rossi, F. (eds) Principles and Practice of Constraint Programming – CP 2003. CP 2003. Lecture Notes in Computer Science, vol 2833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45193-8_58
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DOI: https://doi.org/10.1007/978-3-540-45193-8_58
Publisher Name: Springer, Berlin, Heidelberg
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