# Probabilistic Inductive Logic Programming Based on Answer Set Programming

arXiv: https://arxiv.org/abs/1405.0720 · HTML: https://mlsanity.com/paper/1405.0720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2014.

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

- [A Relational Tsetlin Machine with Applications to Natural Language Understanding](https://mlsanity.com/paper/2102.10952/) (2021)
- [LPMLN, Weak Constraints, and P-log](https://mlsanity.com/paper/2506.12784/) (2025)
- [Weighted Rules under the Stable Model Semantics](https://mlsanity.com/paper/2605.09519/) (2026)
- [Inductive Learning for Possibilistic Logic Programs Under Stable Models](https://mlsanity.com/paper/2510.07069/) (2025)

## Abstract (arXiv)

We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference by optionally utilizing so-called streamlining XOR constraints, in order to reduce the number of computed answer sets. Our approach is prototypically implemented. Examples illustrate the introduced concepts and point at issues and topics for future research.