  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)
