# Computing LPMLN Using ASP and MLN Solvers

arXiv: https://arxiv.org/abs/1707.06325 · HTML: https://mlsanity.com/paper/1707.06325/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

## Built on by (most cited first, 8 of 8)

- [RuleBert: Teaching Soft Rules to Pre-trained Language Models](https://mlsanity.com/paper/2109.13006/) (2021)
- [Rushing and Strolling among Answer Sets -- Navigation Made Easy](https://mlsanity.com/paper/2112.07596/) (2021)
- [Online Learning Probabilistic Event Calculus Theories in Answer Set Programming](https://mlsanity.com/paper/2104.00158/) (2021)
- [An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems](https://mlsanity.com/paper/2209.13043/) (2022)
- [plingo: A system for probabilistic reasoning in clingo based on lpmln](https://mlsanity.com/paper/2206.11515/) (2022)
- [Counting Answer Sets of Disjunctive Answer Set Programs](https://mlsanity.com/paper/2507.11655/) (2025)
- [Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models](https://mlsanity.com/paper/2607.08136/) (2026)
- [How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming](https://mlsanity.com/paper/2607.21208/) (2026)

## Abstract (arXiv)

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. (Under consideration for acceptance in TPLP)