# plingo: A system for probabilistic reasoning in clingo based on lpmln

arXiv: https://arxiv.org/abs/2206.11515 · HTML: https://mlsanity.com/paper/2206.11515/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Explainable Fact Checking with Probabilistic Answer Set Programming](https://mlsanity.com/paper/1906.09198/) (2019)
- [How to build your own ASP-based system?!](https://mlsanity.com/paper/2008.06692/) (2020)
- [Computing LPMLN Using ASP and MLN Solvers](https://mlsanity.com/paper/1707.06325/) (2017)
- [SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation](https://mlsanity.com/paper/2110.01990/) (2021)
- [Weight Learning in a Probabilistic Extension of Answer Set Programs](https://mlsanity.com/paper/1808.04527/) (2018)
- [Solution Enumeration by Optimality in Answer Set Programming](https://mlsanity.com/paper/2108.03474/) (2021)

## Abstract (arXiv)

We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes. Plingo is centered upon LP^MLN, a probabilistic extension of ASP based on a weight scheme from Markov Logic. This choice is motivated by the fact that the core probabilistic reasoning modes can be mapped onto optimization problems and that LP^MLN may serve as a middle-ground formalism connecting to other probabilistic approaches. As a result, plingo offers three alternative frontends, for LP^MLN, P-log, and ProbLog. The corresponding input languages and reasoning modes are implemented by means of clingo's multi-shot and theory solving capabilities. The core of plingo amounts to a re-implementation of LP^MLN in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality. We evaluate plingo's performance empirically by comparing it to other probabilistic systems.