LPMLN, Weak Constraints, and P-log

TL;DR

This paper explores the relationships between LPMLN, weak constraints, and P-log, providing translations that enable computation of probabilistic and preference-based answer set models using standard ASP and MLN solvers.

Contribution

It introduces translations between LPMLN, weak constraints, and P-log, facilitating the use of existing solvers for probabilistic and preference reasoning in answer set programming.

Findings

LPMLN can be translated into programs with weak constraints for MAP estimation.

P-log can be translated into LPMLN to represent probabilistic nonmonotonicity.

These translations enable the use of standard ASP and MLN solvers for complex probabilistic and preference reasoning.

Abstract

LPMLN is a recently introduced formalism that extends answer set programs by adopting the log-linear weight scheme of Markov Logic. This paper investigates the relationships between LPMLN and two other extensions of answer set programs: weak constraints to express a quantitative preference among answer sets, and P-log to incorporate probabilistic uncertainty. We present a translation of LPMLN into programs with weak constraints and a translation of P-log into LPMLN, which complement the existing translations in the opposite directions. The first translation allows us to compute the most probable stable models (i.e., MAP estimates) of LPMLN programs using standard ASP solvers. This result can be extended to other formalisms, such as Markov Logic, ProbLog, and Pearl's Causal Models, that are shown to be translatable into LPMLN. The second translation tells us how probabilistic nonmonotonicity (the ability of the reasoner to change his probabilistic model as a result of new information) of P-log can be represented in LPMLN, which yields a way to compute P-log using standard ASP solvers and MLN solvers.