# Learning logic programs through divide, constrain, and conquer

arXiv: https://arxiv.org/abs/2109.07818 · HTML: https://mlsanity.com/paper/2109.07818/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Learning Explanatory Rules from Noisy Data](https://mlsanity.com/paper/1711.04574/) (2017)
- [Learning programs by learning from failures](https://mlsanity.com/paper/2005.02259/) (2020)
- [Clingo = ASP + Control: Preliminary Report](https://mlsanity.com/paper/1405.3694/) (2014)
- [Inductive logic programming at 30](https://mlsanity.com/paper/2102.10556/) (2021)
- [Making sense of sensory input](https://mlsanity.com/paper/1910.02227/) (2019)
- [Logical reduction of metarules](https://mlsanity.com/paper/1907.10952/) (2019)
- [Inductive general game playing](https://mlsanity.com/paper/1906.09627/) (2019)
- [Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learning](https://mlsanity.com/paper/1805.00068/) (2018)
- [Predicate Invention by Learning From Failures](https://mlsanity.com/paper/2104.14426/) (2021)

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

- [Learning logic programs by discovering where not to search](https://mlsanity.com/paper/2202.09806/) (2022)

## Abstract (arXiv)

We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and supports predicate invention. Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times.