Solution reuse in dynamic constraint satisfaction problems
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Constraint (Logic) Programming: A Survey on Research and Applications
Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
Branching Constraint Satisfaction Problems for Solutions Robust under Likely Changes
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Robust solutions in changing constraint satisfaction problems
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Dynamic SAT with decision change costs: formalization and solutions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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An important extension of constraint technology involves problems that undergo changes that may invalidate the current solution. Previous work on dynamic problems sought methods for efficiently finding new solutions. We take a more proactive approach, exploring methods for finding solutions more likely to remain valid after changes that temporarily alter the set of valid assignments (stable solutions). To this end, we examine strategies for tracking changes in a problem and incorporating this information to guide search to solutions that are more likely to be stable. In this work search is carried out with a min-conflicts hill climbing procedure, and information about change is used to bias value selection, either by distorting the objective function or by imposing further criteria on selection. We study methods that track either value losses or constraint additions, and incorporate information about relative frequency of change into search. Our experiments show that these methods are generally effective in finding stable solutions, and in some cases handle the tradeoff between solution stability and search efficiency quite well. In addition, we identify one condition in which these methods markedly reduce the effort to find a stable solution.