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CHR(PRISM)-based probabilistic logic learning

Published online by Cambridge University Press:  09 July 2010

JON SNEYERS ,
WANNES MEERT ,
JOOST VENNEKENS ,
YOSHITAKA KAMEYA  and
TAISUKE SATO
JON SNEYERS
Affiliation:
Department of Computer Science, K.U. Leuven, Belgium (e-mail: jon.sneyers@cs.kuleuven.be, wannes.meert@cs.kuleuven.be, joost.vennekens@cs.kuleuven.be)
WANNES MEERT
Affiliation:
Department of Computer Science, K.U. Leuven, Belgium (e-mail: jon.sneyers@cs.kuleuven.be, wannes.meert@cs.kuleuven.be, joost.vennekens@cs.kuleuven.be)
JOOST VENNEKENS
Affiliation:
Department of Computer Science, K.U. Leuven, Belgium (e-mail: jon.sneyers@cs.kuleuven.be, wannes.meert@cs.kuleuven.be, joost.vennekens@cs.kuleuven.be)
YOSHITAKA KAMEYA
Affiliation:
Tokyo Institute of Technology, Japan (e-mail: kameya@mi.cs.titech.ac.jp, sato@mi.cs.titech.ac.jp)
TAISUKE SATO
Affiliation:
Tokyo Institute of Technology, Japan (e-mail: kameya@mi.cs.titech.ac.jp, sato@mi.cs.titech.ac.jp)
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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.

Keywords

probabilistic logic learningconstraint handling rules
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 10 , Special Issue 4-6: 26th International Conference on Logic Programming , July 2010 , pp. 433 - 447
Copyright
Copyright © Cambridge University Press 2010

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