约束满足的强局部一致性

R. Debruyne
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引用次数: 15

摘要

过滤技术是约束网络中高效解搜索的关键技术。然而,长期以来,人们一直认为,为了有效地缩小搜索空间,最好的选择是通过前向检查实现有限的局部一致性。然而,最近的研究表明,在搜索过程中保持弧一致性(这是一种更有修剪性的局部一致性)优于在硬约束和大型约束网络上进行前向检查。在本文中,我们证明了在搜索过程中保持比弧一致性强的局部一致性是有利的。通过对在大型神经网络上使用的局部一致性比弧一致性更具剪枝性的比较,最大限制路径一致性(Max-RPC)是最有前途的局部一致性之一。我们提出了一种新的本地一致性,称为Max-RPC增强(Max-RPC Enhanced),它比Max-RPC更强,并且具有几乎相同的CPU时间要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A strong local consistency for constraint satisfaction
Filtering techniques are essential for efficient solution searching in a constraint network (CN). However, for a long time, it has been considered that to efficiently reduce the search space, the best choice is the limited local consistency achieved by forward checking. However, more recently, it has been shown that maintaining arc consistency (which is a more pruningful local consistency) during searching outperforms forward checking on hard and large constraint networks. In this paper, we show that maintaining a local consistency which is stronger than arc consistency during searching can be advantageous. According to a comparison of local consistencies that are more pruningful than the arc consistency which can be used on large CNs, max-restricted path consistency (Max-RPC) is one of the most promising local consistencies. We propose a new local consistency, called Max-RPCEn (Max-RPC Enhanced), that is stronger than Max-RPC and that has almost the same CPU time requirements.
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