Inference Using Compiled Product-Based Possibilistic Networks

  • Raouia Ayachi
  • Nahla Ben Amor
  • Salem Benferhat
Part of the Communications in Computer and Information Science book series (CCIS, volume 299)

Abstract

Possibilistic networks are important graphical tools for representing and reasoning under uncertain pieces of information. In possibility theory, there are two kinds of possibilistic networks depending if possibilistic conditioning is based on the minimum or on the product operator. This paper explores inference in product-based possibilistic networks using compilation. This paper also reports on a set of experimental results comparing product-based possibilistic networks and min-based possibilistic networks from a spatial point of view.

Keywords

Bayesian Network Conjunctive Normal Form Propositional Variable Possibility Distribution Possibility Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Raouia Ayachi
    • 1
    • 2
  • Nahla Ben Amor
    • 1
  • Salem Benferhat
    • 2
  1. 1.LARODECInstitut Supérieur de Gestion TunisLe BardoTunisie
  2. 2.CRIL-CNRSUniversité d’ArtoisFrance62307

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