Strong Equivalence for LPMLN Programs

Joohyung Lee
(Arizona State University)
Man Luo
(Arizona State University)

LPMLN is a probabilistic extension of answer set programs with the weight scheme adapted from Markov Logic. We study the concept of strong equivalence in LPMLN, which is a useful mathematical tool for simplifying a part of an LPMLN program without looking at the rest of it. We show that the verification of strong equivalence in LPMLN can be reduced to equivalence checking in classical logic via a reduct and choice rules as well as to equivalence checking under the "soft'' logic of here-and-there. The result allows us to leverage an answer set solver for LPMLN strong equivalence checking. The study also suggests us a few reformulations of the LPMLN semantics using choice rules, the logic of here-and-there, and classical logic.

In Bart Bogaerts, Esra Erdem, Paul Fodor, Andrea Formisano, Giovambattista Ianni, Daniela Inclezan, German Vidal, Alicia Villanueva, Marina De Vos and Fangkai Yang: Proceedings 35th International Conference on Logic Programming (Technical Communications) (ICLP 2019), Las Cruces, NM, USA, September 20-25, 2019, Electronic Proceedings in Theoretical Computer Science 306, pp. 196–209.
Published: 19th September 2019.

ArXived at: https://dx.doi.org/10.4204/EPTCS.306.24 bibtex PDF
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