# Parallel Constraint-Driven Inductive Logic Programming

arXiv: https://arxiv.org/abs/2109.07132 · HTML: https://mlsanity.com/paper/2109.07132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [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)
- [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)

## Abstract (arXiv)

Multi-core machines are ubiquitous. However, most inductive logic programming (ILP) approaches use only a single core, which severely limits their scalability. To address this limitation, we introduce parallel techniques based on constraint-driven ILP where the goal is to accumulate constraints to restrict the hypothesis space. Our experiments on two domains (program synthesis and inductive general game playing) show that (i) parallelisation can substantially reduce learning times, and (ii) worker communication (i.e. sharing constraints) is important for good performance.