CP-网中的秩剪枝
Rank Pruning for Dominance Queries in CP-Nets
摘要：
条件偏好网络（英语：Conditional preference networks）是一个人对一组离散变量的（条件）偏好的图形表示。在本文中，我们介绍了一种新的方法，量化偏好的任何给定的结果的基础上，CP-网络表示的用户的喜好。我们证明，这些值是有用的推理用户的喜好。特别是，它们允许我们根据用户的偏好对可能的结果（的任何子集）进行排序。此外，这些值可以用于提高结果优势检验的效率。也就是说，给定一对结果，我们可以更有效地确定用户更喜欢哪一个。实验结果表明，该方法比现有的方法更有效地提高了优势度测试的效率。我们表明，上述结果也适用于CP-网，表示变量值之间的无差异。
英文摘要：
Conditional preference networks (CP-nets) are a graphical representation of a person's (conditional) preferences over a set of discrete variables. In this paper, we introduce a novel method of quantifying preference for any given outcome based on a CP-net representation of a user's preferences. We demonstrate that these values are useful for reasoning about user preferences. In particular, they allow us to order (any subset of) the possible outcomes in accordance with the user's preferences. Further, these values can be used to improve the efficiency of outcome dominance testing. That is, given a pair of outcomes, we can determine which the user prefers more efficiently. Through experimental results, we show that this method is more effective than existing techniques for improving dominance testing efficiency. We show that the above results also hold for CP-nets that express indifference between variable values.
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