Computing LPMLN Using ASP and MLN Solvers

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, $\text{LPMLN2ASP}$ and $\text{LPMLN2MLN}$. System $\text{LPMLN2ASP}$ translates LPMLN programs into the input language of answer set solver $\text{CLINGO}$, and using weak constraints and stable model enumeration, it can compute most probable stable models as well as exact conditional and marginal probabilities. System $\text{LPMLN2MLN}$ translates LPMLN programs into the input language of Markov Logic solvers, such as $\text{ALCHEMY}$, $\text{TUFFY}$, and $\text{ROCKIT}$, 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. (Under consideration for acceptance in TPLP)