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. This paper provides a theoretical framework for a research agenda in the area of interactive constraint acquisition, automated modelling and automated constraint programming.
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R. Coletta, C. Bessière, B. O’Sullivan, E.C. Freuder, S. O’Connell, and J. Quinqueton. Semi-automatic modeling by constraint acquisition. In CP-03 Second Workshop on Reformulating Constraint Satisfaction Problems, 2003.
E.C. Freuder and B. O’Sullivan. Generating tradeoffs for interative constraintbased configuration. In Toby Walsh, editor, Proceedings ofthe Seventh International Conference on Principles and Practice of Constraint Programming, pages 590–594, November 2001.
E.C. Freuder and RJ. Wallace. Suggestion strategies for constraint-based matchmaker agents. In Principles and Practice of Constraint Programming-CP98, pages 192–204, October 1998.
Haym Hirsh. Polynomial-time learning with version spaces. In National Conference on Artificial Intelligence, pages 117–122, 1992.
J. Little, C. Gebruers, D. Bridge, and E.C. Freuder. Capturing constraint programming experience: A case-based approach. In CP-02 Workshop on Reformulating Constraint Satisfaction Problems, 2002.
T. Mitchell. Concept learning and the general-to-specific ordering. In Machine Learning, chapter 2, pages 20–51. McGraw Hill, 1997.
U. Montanari. Networks of constraints: Fundamental properties and applications to picture processing. Information Sciences, 7(95–132), 1974.
S. O’Connell, B. O’Sullivan, and E.C. Freuder. Query generation for interactive constraint acquisition. In Proceedings of the 4th International Conference on Recent Advances in Soft Computing (RASC-2002), pages 295–300, December 2002.
F. Rossi and A. Sperduti. Learning solution preferences in constraint problems. Journal ofexperimental and theoretical computer science, 10, 1998.
M. Wallace. Practical applications of constraint programming. Constraints, 1(12): 139–168, 1996.
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© 2004 Springer-Verlag London
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Coletta, R., Bessiere, C., O’Sullivan, B., Freuder, E.C., O’Connell, S., Quinqueton, J. (2004). Constraint Acquisition as Semi-Automatic Modeling. In: Coenen, F., Preece, A., Macintosh, A. (eds) Research and Development in Intelligent Systems XX. SGAI 2003. Springer, London. https://doi.org/10.1007/978-0-85729-412-8_9
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DOI: https://doi.org/10.1007/978-0-85729-412-8_9
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