Adaptive constraint propagation in constraint satisfaction: review and evaluation

Abstract

Several methods for dynamically adapting the local consistency property applied by a CP solver during search have been put forward in recent and older literature. We propose the classification of such methods in three categories depending on the level of granularity where decisions about which local consistency property to apply are taken: node, variable, and value oriented. We then present a detailed review of existing methods from each category, and evaluate them theoretically according to several criteria. Taking one recent representative method from each class, we then perform an experimental study. Results show that simple variable and value oriented methods are quite efficient when the older dom/ddeg heuristic is used for variable ordering, while a carefully tuned node oriented method does not seem to offer notable improvement compared to standard arc consistency propagation. In contrast, under the more realistic setting of dom/wdeg, the variable and value oriented methods cannot compete with standard propagation, while the node oriented method is very efficient. Finally, we obtain a new adaptive propagation method by integrating the variable and value oriented approaches and adding an amount of randomization The resulting method is simple, competitive, and almost parameter-free.