Three-valued possibilistic networks
Salem Benferhat, Karim Tabia · Frontiers in artificial intelligence and applications · 2012
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.