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CLP(BN): constraint logic programming for probabilistic knowledge

Santos Costa, V., Page, D. and Cussens, J. (2008) CLP(BN): constraint logic programming for probabilistic knowledge. In: De Raedt, L., Frasconi, P., Kersting, K. and Muggleton, S., (eds.) Probabilistic Inductive Logic Programming: Theory and Applications. Lecture Notes in Computer Science (4911). Springer , Berlin / Heidelberg , pp. 156-188. ISBN 978-3-540-78651-1

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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(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(BN) programs. An implementation of CLP(BN) is publicly available as part of YAP Prolog at http://www.ncc.up.pt/~vsc/Yap.

Item Type: Book Section
Institution: The University of York
Academic Units: The University of York > Computer Science (York)
Depositing User: York RAE Import
Date Deposited: 16 Mar 2009 18:33
Last Modified: 16 Mar 2009 18:33
Published Version: http://dx.doi.org/10.1007/978-3-540-78652-8_6
Status: Published
Publisher: Springer
Identification Number: 10.1007/978-3-540-78652-8_6
URI: http://eprints.whiterose.ac.uk/id/eprint/7395

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