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

Vítor Santos CostaContact Information, David PageContact Information and James CussensContact Information

(1)  DCC-FCUP and LIACC, Universidade do Porto, Portugal
(2)  Dept. of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA
(3)  Department of Computer Science, University of York, UK
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 .

Contact Information Vítor Santos Costa
Email: vsc@dcc.fc.up.pt

Contact Information David Page
Email: page@biostat.wisc.edu

Contact Information James Cussens
Email: jc@cs.york.ac.uk
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