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.
|Title of host publication||Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems|
|Subtitle of host publication||7th International Conference, CPAIOR 2010, Bologna, Italy, June 14-18, 2010, Proceedings|
|Number of pages||5|
|Publication status||Published - 14 Jun 2010|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publisher||Springer Berlin / Heidelberg|