CHR(PRISM)-based probabilistic logic learning

CHR(PRISM)-based probabilistic logic learning

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...

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Veröffentlicht in:Theory and practice of logic programming 2010-07, Vol.10 (4-6), p.433-447
Hauptverfasser: SNEYERS, JON, MEERT, WANNES, VENNEKENS, JOOST, KAMEYA, YOSHITAKA, SATO, TAISUKE
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container_end_page 447
container_issue 4-6
container_start_page 433
container_title Theory and practice of logic programming
container_volume 10
creator SNEYERS, JON
MEERT, WANNES
VENNEKENS, JOOST
KAMEYA, YOSHITAKA
SATO, TAISUKE
description 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.
doi_str_mv 10.1017/S1471068410000207
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title CHR(PRISM)-based probabilistic logic learning
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