Paper Info
Title
Chr(prism)-based probabilistic logic learning
Abstract
PRISM is an extension of Prolog with probabilistic predicates and built-in support for expectation-maximization learning. Constraint Handling Rules (CHR) is a high-level programming language based on multi-headed multiset rewrite rules. In this paper, we introduce a new probabilistic logic formalism, called CHRiSM, based on a combination of CHR and PRISM. It can be used for high-level rapid prototyping of complex statistical models by means of “chance rules”. The underlying PRISM system can then be used for several probabilistic inference tasks, including probability computation and parameter learning. We define the CHRiSM language in terms of syntax and operational semantics, and illustrate it with examples. We define the notion of ambiguous programs and define a distribution semantics for unambiguous programs. Next, we describe an implementation of CHRiSM, based on CHR(PRISM). We discuss the relation between CHRiSM and other probabilistic logic programming languages, in particular PCHR. Finally, we identify potential application domains.
Year
DOI
Venue
2010
10.1017/S1471068410000207
TPLP
Keywords
DocType
Volume
new probabilistic logic formalism,underlying prism system,probabilistic predicate,probabilistic inference task,chrism language,expectation-maximization learning,probabilistic logic learning,distribution semantics,high-level programming language,high-level rapid prototyping,probabilistic logic programming language,artificial intelligent,operational semantics,probabilistic logic,programming language,statistical model,expectation maximization
Journal
10
Issue
ISSN
Citations 
4-6
Theory and Practice of Logic Programming, 10(4-6), 433-447, 2010
10
PageRank 
References 
Authors
0.55
14
5
Name
Order
Citations
PageRank
jon sneyers11179.47
Wannes Meert235834.44
Joost Vennekens343437.36
Yoshitaka Kameya438625.00
T. Sato51506137.10