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.

在最近和较早的文献中已经提出了几种在搜索过程中动态适应CP求解器所应用的局部一致性属性的方法。我们建议将这些方法分为三类，具体取决于粒度级别，从而决定要应用哪种本地一致性属性：node，variable和value面向。然后，我们将详细介绍每个类别中的现有方法，并根据若干标准从理论上对它们进行评估。从每个班级中采用一种最新的代表性方法，然后进行一项实验研究。结果表明，当使用较旧的dom / ddeg启发式方法进行变量排序时，简单的面向变量和值的方法非常有效，而与标准弧一致性传播相比，精心调整的面向节点的方法似乎没有显着改进。相反，在更实际的dom / wdeg设置下，面向变量和值的方法无法与标准传播竞争，而面向节点的方法非常有效。最后，