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Efficient Inference for Expressive Comparative Preference Languages

机译:高效推论表达比较偏好语言

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A fundamental task for reasoning with preferences is the following: given input preference information from a user, and outcomes α and β, should we infer that the user will prefer α to β? For CP-nets and related comparative preference formalisms, inferring a preference of α over β using the standard definition of derived preference appears to be extremely hard, and has been proved to be PSPACE-complete in general for CP-nets. Such inference is also rather conservative, only making the assumption of transitivity. This paper defines a less conservative approach to inference which can be applied for very general forms of input. It is shown to be efficient for expressive comparative preference languages, allowing comparisons between arbitrary partial tuples (including complete assignments), and with the preferences being ceteris paribus or not.
机译:有关偏好的推理的基本任务是以下:如果我们推断用户愿意,给出来自用户的输入偏好信息,以及结果α和β应该更喜欢α到β?对于CP-Net和相关的比较偏好形式主义,使用衍生偏好的标准定义推断出α上β的偏好似乎是非常艰难的,并且已被证明是CP-Net的PSPace-Cleant。这种推断也是不保守的,只能追究传递率。本文定义了较少保守的推理方法,可用于非常一般的输入形式。它被证明是有效的比较偏好语言有效,允许在任意部分元组(包括完整分配)之间的比较,并且偏好是Ceteris Paribus。

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