# Efficient rule induction by ignoring pointless rules

arXiv: https://arxiv.org/abs/2502.01232 · HTML: https://mlsanity.com/paper/2502.01232/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Inductive logic programming at 30: a new introduction](https://mlsanity.com/paper/2008.07912/) (2020)
- [Learning programs by learning from failures](https://mlsanity.com/paper/2005.02259/) (2020)
- [Inductive general game playing](https://mlsanity.com/paper/1906.09627/) (2019)
- [Learning MDL logic programs from noisy data](https://mlsanity.com/paper/2308.09393/) (2023)
- [Learning Higher-Order Programs without Meta-Interpretive Learning](https://mlsanity.com/paper/2112.14603/) (2021)
- [Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generation](https://mlsanity.com/paper/1707.02729/) (2017)
- [Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation](https://mlsanity.com/paper/2408.11367/) (2024)
- [Learning logic programs by finding minimal unsatisfiable subprograms](https://mlsanity.com/paper/2401.16383/) (2024)

## Abstract (arXiv)

The goal of inductive logic programming (ILP) is to find a set of logical rules that generalises training examples and background knowledge. We introduce an ILP approach that identifies pointless rules. A rule is pointless if it contains a redundant literal or cannot discriminate against negative examples. We show that ignoring pointless rules allows an ILP system to soundly prune the hypothesis space. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can reduce learning times by 99% whilst maintaining predictive accuracies.