Anytime propagation algorithm for min-based possibilistic graphs

Abstract

This paper proposes a new anytime possibilistic inference algorithm for min-based directed networks. Our algorithm departs from a direct adaptation of probabilistic propagation algorithms since it avoids the transformation of the initial network into a junction tree which is known to be a hard problem. The proposed algorithm is composed of several, local stabilization, procedures. Stabilization procedures aim to guarantee that local distributions defined on each node are coherent with respect to those of its parents. We provide experimental results which, for instance, compare our algorithm with the ones based on a direct adaptation of probabilistic propagation algorithms.

Modeling positive and negative information in possibility theory

“…29 They can be adapted to our bipolar framework. Efficient algorithms recently developed for uncertainty propagation in possibilistic graphs 51 could be extended to bipolar possibilistic graphs as well.…”

Three Scenarios for the Revision of Epistemic States

“…In this framework, iterating belief change just means accumulating consistent observations and reasoning from them using the background knowledge. Interestingly, plausibility orderings, encoded as possibility distributions can be represented using the same graphical structures as joint probability distributions (see (Benferhat et al 2002a)), and local methods for reasoning in such graphs can be devised (BenAmor, Benferhat, & Mellouli 2003). These graphical representations are equivalent to the use of possibilistic logic, but not necessarily more computationally efficient.…”

Possibility Theory and Its Applications: Where Do We Stand?

“…Ben Amor and Benferhat [12] investigate the properties of qualitative independence that enable local inferences to be performed in possibilistic nets. Uncertainty propagation algorithms suitable for possibilistic graphical structures have been studied [13].…”