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Logic Programming and Uncertainty

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Scalable Uncertainty Management (SUM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6929))

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Abstract

In recent years Logic programming based languages and features–such as rules and non-monotonic constructs–have become important in various knowledge representation paradigms. While the early logic programming languages, such as Horn logic programs and Prolog did not focus on expressing and reasoning with uncertainty, in recent years logic programming languages have been developed that can express both logical and quantitative uncertainty. In this paper we give an overview of such languages and the kind of uncertainty they can express and reason with. Among those, we slightly elaborate on the language P-log that not only accommodates probabilistic reasoning, but also respects causality and distinguishes observational and action updates.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Arizona State University, Tempe, AZ, 85287-8809

    Chitta Baral

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  1. Chitta Baral
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Editor information

Editors and Affiliations

  1. CRIL-CNRS, University of Artois, 62307, France

    Salem Benferhat

  2. Department of Mathematics, Towson University, 21252, Towson, MD, USA

    John Grant

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Baral, C. (2011). Logic Programming and Uncertainty. In: Benferhat, S., Grant, J. (eds) Scalable Uncertainty Management. SUM 2011. Lecture Notes in Computer Science(), vol 6929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23963-2_3

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  • DOI: https://doi.org/10.1007/978-3-642-23963-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23962-5

  • Online ISBN: 978-3-642-23963-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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