# Learning logic programs by finding minimal unsatisfiable subprograms

arXiv: https://arxiv.org/abs/2401.16383 · HTML: https://mlsanity.com/paper/2401.16383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 49 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Neuro-Symbolic Hierarchical Rule Induction](https://mlsanity.com/paper/2112.13418/) (2021)
- [Predicate Invention by Learning From Failures](https://mlsanity.com/paper/2104.14426/) (2021)

## Built on by (most cited first, 1 of 1)

- [Efficient rule induction by ignoring pointless rules](https://mlsanity.com/paper/2502.01232/) (2025)

## Abstract (arXiv)

The goal of inductive logic programming (ILP) is to search for a logic program that generalises training examples and background knowledge. We introduce an ILP approach that identifies minimal unsatisfiable subprograms (MUSPs). We show that finding MUSPs allows us to efficiently and soundly prune the search space. Our experiments on multiple domains, including program synthesis and game playing, show that our approach can reduce learning times by 99%.