Problem-Structure vs. Solution-Based Methods for Solving Dynamic Constraint Satisfaction Problems

A new type of reasoning-reuse technique for dynamic constraint satisfaction problems (DCSPs) is based on a form of the weighted degree strategy known as random probing, in which failures are sampled prior to search. This approach is effective with DCSPs because it can locate major bottlenecks in a problem, and because bottlenecks tend to remain stable after small or moderate changes. Here, we show that this approach is effective with various kinds of change including changes in constraint-relations and changes that transform a problem from one with solutions to one without, or vice versa. The latter, in particular, is especially troublesome for a solution reuse method like Local Changes. We also examine the second quality metric for DCSP methods, solution stability. We show that an enhancement of probing-based search, which begins with values in the solution found before perturbation and continues to choose values that minimise conflicts with the original solution, actually improves on Local Changes for the problems tested, as well as improving average search performance further. Probing-based methods can, therefore, solve DCSPs very efficiently after many types of change, while also meeting the criterion of high solution stability.