改进分解方法的精确解计数

Philippe Jégou, H. Kanso, C. Terrioux
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引用次数: 6

摘要

CSP中计算解的问题,称为#CSP,是一个极其困难的问题,在人工智能中有许多应用。这个问题可以用精确方法来解决,但更经典的是用近似方法来解决。在这里,我们主要关注精确的计数。我们展示了如何改进基于结构分解的方法,通过提供增强对新解的搜索,这是计数的关键步骤,特别是对于此类方法。此外,如果时间或空间资源不足,我们表明我们的方法仍然能够提供结果的下界。在CSP基准测试上的实验显示了我们的方法的实际优势,而不是文献中最好的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Exact Solution Counting for Decomposition Methods
The problem of counting solutions in CSP, called #CSP, is an extremely difficult problem that has many applications in Artificial Intelligence. This problem can be addressed by exact methods, but more classically it is solved by approximate methods. Here, we focus primarily on the exact counting. We show how it is possible to improve the methods based on structural decomposition by offering to enhance the search for a new solution which is a critical step for counting, particularly for such methods. Moreover, if the resources in time or in space are insufficient, we show that our approach is still able to provide a lower bound of the result. Experiments on CSP benchmarks show the practical advantage of our approach w.r.t. the best methods of the literature.
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