# Weight Learning in a Probabilistic Extension of Answer Set Programs

arXiv: https://arxiv.org/abs/1808.04527 · HTML: https://mlsanity.com/paper/1808.04527/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text](https://mlsanity.com/paper/2307.07696/) (2023)
- [plingo: A system for probabilistic reasoning in clingo based on lpmln](https://mlsanity.com/paper/2206.11515/) (2022)
- [Inductive Learning for Possibilistic Logic Programs Under Stable Models](https://mlsanity.com/paper/2510.07069/) (2025)
- [CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2607.19996/) (2026)

## Abstract (arXiv)

LPMLN is a probabilistic extension of answer set programs with the weight scheme derived from that of Markov Logic. Previous work has shown how inference in LPMLN can be achieved. In this paper, we present the concept of weight learning in LPMLN and learning algorithms for LPMLN derived from those for Markov Logic. We also present a prototype implementation that uses answer set solvers for learning as well as some example domains that illustrate distinct features of LPMLN learning. Learning in LPMLN is in accordance with the stable model semantics, thereby it learns parameters for probabilistic extensions of knowledge-rich domains where answer set programming has shown to be useful but limited to the deterministic case, such as reachability analysis and reasoning about actions in dynamic domains. We also apply the method to learn the parameters for probabilistic abductive reasoning about actions.