# Inductive Learning for Possibilistic Logic Programs Under Stable Models

arXiv: https://arxiv.org/abs/2510.07069 · HTML: https://mlsanity.com/paper/2510.07069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [Inductive logic programming at 30](https://mlsanity.com/paper/2102.10556/) (2021)
- [The ILASP system for Inductive Learning of Answer Set Programs](https://mlsanity.com/paper/2005.00904/) (2020)
- [Probabilistic Inductive Logic Programming Based on Answer Set Programming](https://mlsanity.com/paper/1405.0720/) (2014)
- [Heuristic Based Induction of Answer Set Programs: From Default theories to combinatorial problems](https://mlsanity.com/paper/1802.06462/) (2018)
- [Weight Learning in a Probabilistic Extension of Answer Set Programs](https://mlsanity.com/paper/1808.04527/) (2018)
- [Model Explanation via Support Graphs](https://mlsanity.com/paper/2310.01626/) (2023)
- [Encoding Markov Logic Networks in Possibilistic Logic](https://mlsanity.com/paper/1506.01432/) (2015)
- [Induction of Interpretable Possibilistic Logic Theories from Relational Data](https://mlsanity.com/paper/1705.07095/) (2017)
- [Graph-based Interpretation of Normal Logic Programs](https://mlsanity.com/paper/2111.13249/) (2021)

## Abstract (arXiv)

Possibilistic logic programs (poss-programs) under stable models are a major variant of answer set programming (ASP). While its semantics (possibilistic stable models) and properties have been well investigated, the problem of inductive reasoning has not been investigated yet. This paper presents an approach to extracting poss-programs from a background program and examples (parts of intended possibilistic stable models). To this end, the notion of induction tasks is first formally defined, its properties are investigated and two algorithms ilpsm and ilpsmmin for computing induction solutions are presented. An implementation of ilpsmmin is also provided and experimental results show that when inputs are ordinary logic programs, the prototype outperforms a major inductive learning system for normal logic programs from stable models on the datasets that are randomly generated.