LPMLN, Weak Constraints, and P-log
This work provides theoretical connections and computational tools for researchers in knowledge representation and reasoning, but it is incremental as it builds on existing formalisms and translations.
The paper investigates relationships between LPMLN and two extensions of answer set programs, presenting translations that enable computing most probable stable models of LPMLN using standard ASP solvers and representing P-log's probabilistic nonmonotonicity in LPMLN.
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.