Possibilistic Local Structure for Compiling Min-Based Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 190)

Abstract

Compiling graphical models has recently been triggered much research. First investigations were established in the probabilistic framework. This paper studies compilation-based inference in min-based possibilistic networks. We first take advantage of the idempotency property of the min operator to enhance an existing compilation-based inference method in the possibilistic framework. Then, we propose a new CNF encoding which fits well with the particular case of binary networks.

Keywords

Compilation inference possibilistic reasoning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Raouia Ayachi
    • 1
  • Nahla Ben Amor
    • 1
  • Salem Benferhat
    • 2
  1. 1.LARODEC, Institut Supérieur de GestionUniversity of TunisTunisTunisia
  2. 2.CRIL-CNRSUniversité d’ArtoisLens CedexFrance

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