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Three-valued possibilistic networks

机译:三值可能性网络

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摘要

Possibilistic networks are graphical models that compactly encode joint possibility distributions. This paper studies a new form of possibilistic graphical models called three-valued possibilistic networks. Contrary to standard belief networks where the beliefs are encoded using belief degrees within the interval [0, 1], three-valued possibilistic networks only allow three values: 0, 1 and {0, 1}. The first part of this paper addresses foundational issues of three-valued possibilistic networks. In particular, we show that the semantics that can be associated with a three-valued possibilistic network is a family of compatible boolean networks. The second part of the paper deals with inference issues where we propose an extension to the min-based chain rule for three-valued networks. Then, we show that the well-known junction tree algorithm can be directly adapted for the three-valued possibilistic setting.
机译:可能性网络是图形模型,可以紧凑地编码联合可能性分布。本文研究了一种新的形式的可能性图形模型,称为三值可能性网络。与标准信念网络相反,在标准信念网络中,信念是使用区间[0,1]内的信念度进行编码的,三值可能性网络仅允许三个值:0、1和{0,1}。本文的第一部分讨论了三值可能性网络的基本问题。特别是,我们表明可以与三值可能性网络相关联的语义是兼容布尔网络家族。本文的第二部分处理推理问题,其中我们建议对三值网络的基于最小的链规则进行扩展。然后,我们证明了众所周知的结点树算法可以直接适用于三值可能性设置。

著录项

  • 来源
  • 会议地点 Montpellier(FR)
  • 作者

    Salem Benferhat; Karim Tabia;

  • 作者单位

    Univ Lille Nord de France, F-59000 Lille, France. UArtois, CRIL UMR CNRS 8188, F-62300 Lens, France;

    Univ Lille Nord de France, F-59000 Lille, France. UArtois, CRIL UMR CNRS 8188, F-62300 Lens, France;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

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