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Probabilistic Inductive Logic Programming pp 156–188Cite as

CLP(\(\cal{BN}\)): Constraint Logic Programming for Probabilistic Knowledge

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4911))

Abstract

In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP(\(\cal{BN}\)) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP(\(\cal{BN}\)) programs. An implementation of CLP(\(\cal{BN}\)) is publicly available as part of YAP Prolog at http://www.ncc.up.pt/~vsc/Yap .

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Author information

Authors and Affiliations

  1. DCC-FCUP and LIACC, Universidade do Porto, Portugal

    Vítor Santos Costa

  2. Dept. of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA

    David Page

  3. Department of Computer Science, University of York, UK

    James Cussens

Authors
  1. Vítor Santos Costa
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  2. David Page
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  3. James Cussens
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Luc De Raedt Paolo Frasconi Kristian Kersting Stephen Muggleton

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Santos Costa, V., Page, D., Cussens, J. (2008). CLP(\(\cal{BN}\)): Constraint Logic Programming for Probabilistic Knowledge. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds) Probabilistic Inductive Logic Programming. Lecture Notes in Computer Science(), vol 4911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78652-8_6

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  • DOI: https://doi.org/10.1007/978-3-540-78652-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78651-1

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