Computing LPMLN using ASP and MLN solvers*
By Joohyung Lee, Samidh Talsania, Yi Wang. 2017
37 citations recorded by the source corpus.
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, lpmln2asp and lpmln2mln. System lpmln2asp translates LPMLN programs into the input language of answer set solver 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 lpmln2mln translates LPMLN programs into the input language of Markov Logic solvers, such as alchemy, tuffy, and 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.
Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods
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In Artificial Intelligence
- Naser Ahmadi
- Preslav Nakov
- Paolo Papotti
- Mohammed Saeed
- Francesca Toni
- Neema Kotonya
- Ihab F. Ilyas
- Xu Chu
In Probabilistic & Bayesian Methods
Citations in the corpus
- RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021, background): “LPMLN programs are transformed to meet the needs of an ASP solver (Lee et al., 2017; Gebser et al., 2014).”