CLP(BN ):  constraint logic programming for probabilistic knowledge

V. Santos Costa; D. Page; J. Cussens

doi:10.1007/978-3-540-78652-8_6

CLP(BN ): constraint logic programming for probabilistic knowledge

V. Santos Costa, D. Page, J. Cussens

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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.

Original language	English
Title of host publication	Probabilistic Inductive Logic Programming
Subtitle of host publication	Theory and Applications
Editors	Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen Muggleton
Place of Publication	Berlin
Publisher	Springer
Pages	156-188
Number of pages	32
ISBN (Print)	978-3-540-78651-1
DOIs	https://doi.org/10.1007/978-3-540-78652-8_6
Publication status	Published - 2008

Publication series

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	4911

Access to Document

10.1007/978-3-540-78652-8_6

http://dx.doi.org/10.1007/978-3-540-78652-8_6

Cite this

Santos Costa, V., Page, D., & Cussens, J. (2008). CLP(BN ): constraint logic programming for probabilistic knowledge. In L. De Raedt, P. Frasconi, K. Kersting, & S. Muggleton (Eds.), Probabilistic Inductive Logic Programming: Theory and Applications (pp. 156-188). (Lecture Notes in Artificial Intelligence; Vol. 4911). Springer. https://doi.org/10.1007/978-3-540-78652-8_6

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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.",

author = "{Santos Costa}, V. and D. Page and J. Cussens",

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CLP(BN ): constraint logic programming for probabilistic knowledge. / Santos Costa, V.; Page, D.; Cussens, J.
Probabilistic Inductive Logic Programming: Theory and Applications. ed. / Luc De Raedt; Paolo Frasconi; Kristian Kersting; Stephen Muggleton. Berlin: Springer, 2008. p. 156-188 (Lecture Notes in Artificial Intelligence; Vol. 4911).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - CLP(BN )

T2 - constraint logic programming for probabilistic knowledge

AU - Santos Costa, V.

AU - Page, D.

AU - Cussens, J.

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

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U2 - 10.1007/978-3-540-78652-8_6

DO - 10.1007/978-3-540-78652-8_6

M3 - Chapter

SN - 978-3-540-78651-1

T3 - Lecture Notes in Artificial Intelligence

SP - 156

EP - 188

BT - Probabilistic Inductive Logic Programming

A2 - De Raedt, Luc

A2 - Frasconi, Paolo

A2 - Kersting, Kristian

A2 - Muggleton, Stephen

PB - Springer

CY - Berlin

ER -

CLP(BN ): constraint logic programming for probabilistic knowledge

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