Solving Dynamic Constraint Satisfaction Problems by Identifying Stable Features

This paper presents a new analysis of dynamic constraint satisfaction problems (DCSPs) with finite domains 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.

Richard J. Wallace, Diarmuid Grimes, Eugene C. Freuder