CLP(BN): Constraint Logic Programming for Probabilistic Knowledge
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.
Cite
Text
Costa et al. "CLP(BN): Constraint Logic Programming for Probabilistic Knowledge." Conference on Uncertainty in Artificial Intelligence, 2003. doi:10.1007/978-3-540-78652-8_6Markdown
[Costa et al. "CLP(BN): Constraint Logic Programming for Probabilistic Knowledge." Conference on Uncertainty in Artificial Intelligence, 2003.](https://mlanthology.org/uai/2003/costa2003uai-clp/) doi:10.1007/978-3-540-78652-8_6BibTeX
@inproceedings{costa2003uai-clp,
title = {{CLP(BN): Constraint Logic Programming for Probabilistic Knowledge}},
author = {Costa, Vítor Santos and Page, David and Qazi, Maleeha and Cussens, James},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2003},
pages = {517-524},
doi = {10.1007/978-3-540-78652-8_6},
url = {https://mlanthology.org/uai/2003/costa2003uai-clp/}
}