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Interval-Based Possibilistic Networks

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Scalable Uncertainty Management (SUM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8720))

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Abstract

In this paper, we study foundations of interval-based possibilistic networks where possibility degrees associated with nodes are no longer singletons but sub-intervals of [0,1]. This extension allows to compactly encode and reason with epistemic uncertainty and imprecise beliefs as well as with multiple expert knowledge. We propose a natural semantics based on compatible possibilistic networks. The last part of the paper shows that computing the uncertainty bounds of any event can be computed in interval-based networks without extra computational cost with respect to standard possibilistic networks.

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Author information

Authors and Affiliations

  1. Univ Lille Nord de France, F-59000, Lille, France

    Salem Benferhat, Sylvain Lagrue & Karim Tabia

  2. UArtois, CRIL UMR CNRS 8188, F-62300, Lens, France

    Salem Benferhat, Sylvain Lagrue & Karim Tabia

Authors
  1. Salem Benferhat
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  2. Sylvain Lagrue
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  3. Karim Tabia
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Editor information

Editors and Affiliations

  1. Istituto di Scienza e Tecnologie dell’Informazione (ISTI - CNR), Pisa, Italy

    Umberto Straccia

  2. Department of Computer Science and Information Systems, University of London, London, United Kingdom

    Andrea Calì

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Benferhat, S., Lagrue, S., Tabia, K. (2014). Interval-Based Possibilistic Networks. In: Straccia, U., Calì, A. (eds) Scalable Uncertainty Management. SUM 2014. Lecture Notes in Computer Science(), vol 8720. Springer, Cham. https://doi.org/10.1007/978-3-319-11508-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-11508-5_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11507-8

  • Online ISBN: 978-3-319-11508-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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