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Asian Computing Science Conference

ACSC 1995: Algorithms, Concurrency and Knowledge pp 286–300Cite as

Probabilistic logic programming and Bayesian networks

  • Knowledge Representation and Programming Languages
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1023))

Abstract

We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We define a fixpoint theory, declarative semantics, and proof procedure for the new class of probabilistic logic programs. Compared to other approaches to quantitative logic programming, we provide a true probabilistic framework with potential applications in probabilistic expert systems and decision support systems. We also discuss the relationship between such programs and Bayesian networks, thus moving toward a unification of two major approaches to automated reasoning.

This work was partially supported by NSF grant IRI-9509165.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, 53201, Milwaukee, WI, USA

    Liem Ngo & Peter Haddawy

Authors
  1. Liem Ngo
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  2. Peter Haddawy
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Kanchana Kanchanasut Jean-Jacques Lévy

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© 1995 Springer-Verlag Berlin Heidelberg

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Ngo, L., Haddawy, P. (1995). Probabilistic logic programming and Bayesian networks. In: Kanchanasut, K., Lévy, JJ. (eds) Algorithms, Concurrency and Knowledge. ACSC 1995. Lecture Notes in Computer Science, vol 1023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60688-2_51

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  • DOI: https://doi.org/10.1007/3-540-60688-2_51

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60688-8

  • Online ISBN: 978-3-540-49262-7

  • eBook Packages: Springer Book Archive

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