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On the Use of Possibilistic Bases for Local Computations in Product-Based Possibilistic Networks

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Advances in Artificial Intelligence (Canadian AI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4509))

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Abstract

Product-based possibilistic networks allow an efficient representation of possibility distributions. However, when the graph is multiply connected, the propagation may be unfeasible because of the high space complexity problem. In this paper, we propose a new inference approach on product-based possibilistic networks based on compact representations of possibility distributions, which are possibilistic knowledge bases.

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Authors and Affiliations

  1. CRIL, Université d’Artois, Faculté Jean Perrin, Rue Jean Souvraz, SP18, 62300 Lens, France

    Salem Benferhat & Salma Smaoui

Authors
  1. Salem Benferhat
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  2. Salma Smaoui
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Ziad Kobti Dan Wu

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© 2007 Springer Berlin Heidelberg

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Benferhat, S., Smaoui, S. (2007). On the Use of Possibilistic Bases for Local Computations in Product-Based Possibilistic Networks. In: Kobti, Z., Wu, D. (eds) Advances in Artificial Intelligence. Canadian AI 2007. Lecture Notes in Computer Science(), vol 4509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72665-4_31

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  • DOI: https://doi.org/10.1007/978-3-540-72665-4_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72664-7

  • Online ISBN: 978-3-540-72665-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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