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Improving the Performance of Consistency Algorithms by Localizing and Bolstering Propagation in a Tree Decomposition

机译:通过在树分解中本地化和借助借助借助传播来提高一致性算法的性能

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The tractability of a Constraint Satisfaction Problem (CSP) is guaranteed by a direct relationship between its consistency level and a structural parameter of its constraint network such as the treewidth. This result is not widely exploited in practice because enforcing higher-level consistencies can be costly and can change the structure of the constraint network and increase its width. Recently, R(*,m)C was proposed as a relational consistency property that does not modify the structure of the graph and, thus, does not affect its width. In this paper, we explore two main strategies, based on a tree decomposition of the CSP, for improving the performance of enforcing R(*,m)C and getting closer to the above tractability condition. Those strategies are: a) localizing the application of the consistency algorithm to the clusters of the tree decomposition, and b) bolstering constraint propagation between clusters by adding redundant constraints at their separators, for which we propose three new schemes. We characterize the resulting consistency properties by comparing them, theoretically and empirically, to the original R(*,m)C and the popular GAC and maxRPWC, and establish the benefits of our approach for solving difficult problems.
机译:通过其一致性水平与其约束网络的结构参数(如树宽)之间的直接关系,保证了约束满足问题(CSP)的易用性。此结果在实践中没有广泛利用,因为强制执行更高级别的常量可能是昂贵的并且可以改变约束网络的结构并增加其宽度。最近,R(*,m)c被提出为关系的关系,不修改图表的结构,因此不会影响其宽度。在本文中,我们根据CSP的树分解探讨了两个主要策略,用于改善r(*,m)c并越接近上述途径条件的性能。这些策略是:a)通过在其分离器中添加冗余约束,本地化一致性算法在树分解的簇中的应用,B)通过在其分离器中添加冗余约束,为此提出三种新方案。我们通过将它们的理论和经验与原始R(*,M)C和流行的GAC和MaxRPWC进行比较来表征产生的一致性属性,并建立了解决困难问题的方法的好处。

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