Computing LPMLN using ASP and MLN solvers

LPMLN is a recent addition to probabilistic logic programming languages. Its main idea is to overcome the rigid nature of the stable model semantics by assigning a weight to each rule in a way similar to Markov Logic is defined. We present two implementations of LPMLN, lpmln2asp and lpmln2mln. Syste...

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Bibliographic Details
Published in:Theory and practice of logic programming Vol. 17; no. 5-6; pp. 942 - 960
Main Authors: LEE, JOOHYUNG, TALSANIA, SAMIDH, WANG, YI
Format: Journal Article
Language:English
Published: Cambridge, UK Cambridge University Press 01.09.2017
Subjects:
Computation
Constraint modelling
Enumeration
Logic programming
Markov processes
Probabilistic inference
Programming languages
Regular Papers
Semantics
Solvers
Answer Set Programming
Markov Logic
LPMLN
ISSN:1471-0684, 1475-3081
Online Access:Get full text
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Summary:LPMLN is a recent addition to probabilistic logic programming languages. Its main idea is to overcome the rigid nature of the stable model semantics by assigning a weight to each rule in a way similar to Markov Logic is defined. We present two implementations of LPMLN, lpmln2asp and lpmln2mln. System lpmln2asp translates LPMLN programs into the input language of answer set solver clingo, and using weak constraints and stable model enumeration, it can compute most probable stable models as well as exact conditional and marginal probabilities. System lpmln2mln translates LPMLN programs into the input language of Markov Logic solvers, such as alchemy, tuffy, and rockit, and allows for performing approximate probabilistic inference on LPMLN programs. We also demonstrate the usefulness of the LPMLN systems for computing other languages, such as ProbLog and Pearl's Causal Models, that are shown to be translatable into LPMLN.
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ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068417000400