Stochastic constraint programming by neuroevolution with filtering

Steven Prestwich, S. Armagan Tarim, R. Rossi, Brahim Hnich

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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.
Original languageEnglish
Title of host publicationIntegration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Subtitle of host publication7th International Conference, CPAIOR 2010, Bologna, Italy, June 14-18, 2010, Proceedings
Pages282-286
Number of pages5
Volume6140 LNCS
DOIs
Publication statusPublished - 14 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Berlin / Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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