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Computing LPMLN using ASP and MLN solvers*

Published online by Cambridge University Press:  30 August 2017

JOOHYUNG LEE ,
SAMIDH TALSANIA  and
YI WANG
JOOHYUNG LEE
Affiliation:
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA (e-mail: [email protected], [email protected], [email protected])
SAMIDH TALSANIA
Affiliation:
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA (e-mail: [email protected], [email protected], [email protected])
YI WANG
Affiliation:
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA (e-mail: [email protected], [email protected], [email protected])
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Abstract

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.

Keywords

Answer Set ProgrammingMarkov LogicLPMLN
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 17 , Special Issue 5-6: 33rd International Conference on Logic Programming , September 2017 , pp. 942 - 960
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

*

This work was partially supported by the National Science Foundation under Grants IIS-1319794 and IIS-1526301.

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