Paper Info
Title
clp(pdf(y)): Constraints for Probabilistic Reasoning in Logic Programming
Abstract
We argue that the clp(X) framework is a suitable vehicle for extending logic programming (LP) with probabilistic reasoning. This paper presents such a generic framework, clp(pdf(Y)), and proposes two promising instances. The first provides a seamless integration of Bayesian Networks, while the second defines distributions over variables and employs conditional constraints over predicates. The generic methodology is based on attaching probability distributions over finite domains. We illustrate computational benefits of this approach by comparing program performances with a clp(fd) program on a cryptographic problem.
Year
DOI
Venue
2003
10.1007/978-3-540-45193-8_53
Lecture Notes in Computer Science
Keywords
Field
DocType
probabilistic reasoning,probability distribution,bayesian network
Mathematical optimization,Conditional probability distribution,Cryptography,Computer science,Theoretical computer science,Bayesian network,Probability distribution,Artificial intelligence,Logic programming,Predicate (grammar),Probabilistic logic,Stochastic programming
Conference
Volume
ISSN
Citations 
2833
0302-9743
1
PageRank 
References 
Authors
0.34
6
1
Name
Order
Citations
PageRank
Nicos Angelopoulos15311.48