# Inductive Logic Programming via Differentiable Deep Neural Logic Networks

arXiv: https://arxiv.org/abs/1906.03523 · HTML: https://mlsanity.com/paper/1906.03523/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

## Built on by (most cited first, 25 of 25)

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- [Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning](https://mlsanity.com/paper/2305.12738/) (2023)
- [A Critical Review of Inductive Logic Programming Techniques for Explainable AI](https://mlsanity.com/paper/2112.15319/) (2021)
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- [Rule Learning by Modularity](https://mlsanity.com/paper/2212.12335/) (2022)
- [Deep Inductive Logic Programming meets Reinforcement Learning](https://mlsanity.com/paper/2308.16210/) (2023)
- [Differentiable Inductive Logic Programming for Fraud Detection](https://mlsanity.com/paper/2410.21928/) (2024)
- [GLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoning](https://mlsanity.com/paper/2508.06716/) (2025)
- [Logic of Hypotheses: from Zero to Full Knowledge in Neurosymbolic Integration](https://mlsanity.com/paper/2509.21663/) (2025)
- [LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition](https://mlsanity.com/paper/2410.01929/) (2024)
- [Neuro-Symbolic Contrastive Learning for Cross-domain Inference](https://mlsanity.com/paper/2502.09213/) (2025)
- [Neuro-Logic Lifelong Learning](https://mlsanity.com/paper/2511.12793/) (2025)
- [ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming](https://mlsanity.com/paper/2605.04193/) (2026)

## Abstract (arXiv)

We propose a novel paradigm for solving Inductive Logic Programming (ILP) problems via deep recurrent neural networks. This proposed ILP solver is designed based on differentiable implementation of the deduction via forward chaining. In contrast to the majority of past methods, instead of searching through the space of possible first-order logic rules by using some restrictive rule templates, we directly learn the symbolic logical predicate rules by introducing a novel differentiable Neural Logic (dNL) network. The proposed dNL network is able to learn and represent Boolean functions efficiently and in an explicit manner. We show that the proposed dNL-ILP solver supports desirable features such as recursion and predicate invention. Further, we investigate the performance of the proposed ILP solver in classification tasks involving benchmark relational datasets. In particular, we show that our proposed method outperforms the state of the art ILP solvers in classification tasks for Mutagenesis, Cora and IMDB datasets.