Abstract
Stochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several complete solution methods have been proposed, but the authors recently showed that an incomplete approach based on neuroevolution is more scalable. In this paper we hybridise neuroevolution with constraint filtering on hard constraints, and show both theoretically and empirically that the hybrid can learn more complex policies more quickly.
S. A. Tarim and B. Hnich are supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. SOBAG-108K027. S. A. Tarim is also supported by Hacettepe University (BAB). A version of this algorithm will used to further research in risk management as part of a collaboration with IBM Research, with partial support from the Irish Development Association and IRCSET.
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Prestwich, S.D., Tarim, S.A., Rossi, R., Hnich, B. (2010). Stochastic Constraint Programming by Neuroevolution with Filtering. In: Lodi, A., Milano, M., Toth, P. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2010. Lecture Notes in Computer Science, vol 6140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13520-0_30
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DOI: https://doi.org/10.1007/978-3-642-13520-0_30
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