Abstract
Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming (ICON) loop. In this approach data is gathered and analyzed systematically in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand.
Siegfried Nijssen can currently be reached at the Institute of Information and Communication Technologies, Electronics and Applied Mathematics, UC Louvain, Belgium.
Tias Guns can currently be reached at the Vrije Universiteit Brussel.
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References
Bent, R., Hentenryck, P.: Online stochastic and robust optimization. In: Maher, M.J. (ed.) ASIAN 2004. LNCS, vol. 3321, pp. 286–300. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30502-6_21
Bessiere, C., Hebrard, E., O’Sullivan, B.: Minimising decision tree size as combinatorial optimisation. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 173–187. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04244-7_16
Coquery, E., Jabbour, S., Saïs, L., Salhi, Y.: A SAT-based approach for discovering frequent, closed and maximal patterns in a sequence. In: Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), Montpellier, France, pp. 258–263. IOS Press (2012)
Dechter, R., Dechter, A.: Belief maintenance in dynamic constraint networks. In: Proceedings of the 7th National Conference on Artificial Intelligence (AAAI 1888), St. Paul, MN, pp. 37–42. AAAI Press/The MIT Press (1988)
De Raedt, L., Guns, T., Nijssen, S.: Constraint programming for itemset mining. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Las Vegas, Nevada, pp. 204–212. ACM (2008)
Epstein, S.L., Freuder, E.C.: Collaborative learning for constraint solving. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 46–60. Springer, Heidelberg (2001). doi:10.1007/3-540-45578-7_4
Khiari, M., Boizumault, P., Crémilleux, B.: Constraint programming for mining n-ary patterns. In: Cohen, D. (ed.) CP 2010. LNCS, vol. 6308, pp. 552–567. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15396-9_44
Walsh, T.: Stochastic constraint programming. In: Proceedings of the 15th Eureopean Conference on Artificial Intelligence (ECAI 2002), Lyon, France, pp. 111–115. IOS Press (2002)
Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Satzilla: portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. (JAIR) 32, 565–606 (2008)
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Bessiere, C. et al. (2016). The Inductive Constraint Programming Loop. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_12
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DOI: https://doi.org/10.1007/978-3-319-50137-6_12
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