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Using Kappas as Indicators of Strength in Qualitative Probabilistic Networks

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2711))

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Abstract

Qualitative probabilistic networks are designed for probabilistic inference in a qualitative way. They capture qualitative influences between variables, but do not provide for indicating the strengths of these influences. As a result, trade-offs between conflicting influences remain unresolved upon inference. In this paper, we investigate the use of order-of-magnitude kappa values to capture strengths of influences in a qualitative network. We detail the use of these kappas upon inference, thereby providing for trade-off resolution.

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Author information

Authors and Affiliations

  1. Institute of Information and Computing Sciences, Utrecht University, P.O. Box 80.089, 3508 TB, Utrecht, The Netherlands

    Silja Renooij & Pauline Pardieck

  2. Department of Computer and Information Science, Brooklyn College, City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210

    Simon Parsons

Authors
  1. Silja Renooij
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  2. Simon Parsons
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  3. Pauline Pardieck
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Aalborg University, Fredrik Bajersvej 7E, 9220, Aalborg, Denmark

    Thomas Dyhre Nielsen

  2. Department of Computer Science, Hong Kong University of Science and Technology, ClearWater Bay Road, Kowloon, Hong Kong, China,

    Nevin Lianwen Zhang

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© 2003 Springer-Verlag Berlin Heidelberg

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Renooij, S., Parsons, S., Pardieck, P. (2003). Using Kappas as Indicators of Strength in Qualitative Probabilistic Networks. In: Nielsen, T.D., Zhang, N.L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2003. Lecture Notes in Computer Science(), vol 2711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45062-7_7

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  • DOI: https://doi.org/10.1007/978-3-540-45062-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40494-1

  • Online ISBN: 978-3-540-45062-7

  • eBook Packages: Springer Book Archive

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