Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Solution reuse in dynamic constraint satisfaction problems
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Stability of Solutions in Constraint Satisfaction Problems
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Stability of Solutions in Constraint Satisfaction Problems
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
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
CSCLP'09 Proceedings of the 14th Annual ERCIM international conference on Constraint solving and constraint logic programming
Hi-index | 0.00 |
This paper presents a new analysis of dynamic constraint satisfaction problems (DCSPs) with finite domans and a new approach to solving them. We first show that even very small changes in a CSP, in the form of addition of constraints or changes in constraint relations, can have profound effects on search performance. These effects are reflected in the amenability of the problem to different forms of heuristic action as well as overall quality of search. In addition, classical DCSP methods perform poorly on these problems because there are sometimes no solutions similar to the original one found. We then show that the same changes do not markedly affect the locations of the major sources of contention in the problem. A technique for iterated sampling that performs a careful assessment of this property and uses the information during subsequent search, performs well even when it only uses information based on the original problem in the DCSP sequence. The result is a new approach to solving DCSPs that is based on a robust strategy for ordering variables rather than on robust solutions.