# Inductive Learning of Answer Set Programs from Noisy Examples

arXiv: https://arxiv.org/abs/1808.08441 · HTML: https://mlsanity.com/paper/1808.08441/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2018.

## Built on by (most cited first, 22 of 22)

- [Neuro-Symbolic Hierarchical Rule Induction](https://mlsanity.com/paper/2112.13418/) (2021)
- [Differentiable Logic Machines](https://mlsanity.com/paper/2102.11529/) (2021)
- [FF-NSL: Feed-Forward Neural-Symbolic Learner](https://mlsanity.com/paper/2106.13103/) (2021)
- [Learning MDL logic programs from noisy data](https://mlsanity.com/paper/2308.09393/) (2023)
- [Learning Differentiable Logic Programs for Abstract Visual Reasoning](https://mlsanity.com/paper/2307.00928/) (2023)
- [AI Descartes: Combining Data and Theory for Derivable Scientific Discovery](https://mlsanity.com/paper/2109.01634/) (2021)
- [Defining neurosymbolic AI](https://mlsanity.com/paper/2507.11127/) (2025)
- [Learning Robust Reward Machines from Noisy Labels](https://mlsanity.com/paper/2408.14871/) (2024)
- [The Need for Verification in AI-Driven Scientific Discovery](https://mlsanity.com/paper/2509.01398/) (2025)
- [Learning Logic Programs From Noisy Failures](https://mlsanity.com/paper/2201.03702/) (2021)
- [Online Learning Probabilistic Event Calculus Theories in Answer Set Programming](https://mlsanity.com/paper/2104.00158/) (2021)
- [Lifting Symmetry Breaking Constraints with Inductive Logic Programming](https://mlsanity.com/paper/2112.11806/) (2021)
- [Online inductive learning from answer sets for efficient reinforcement learning exploration](https://mlsanity.com/paper/2501.07445/) (2025)
- [HySTER: A Hybrid Spatio-Temporal Event Reasoner](https://mlsanity.com/paper/2101.06644/) (2021)
- [Efficient lifting of symmetry breaking constraints for complex combinatorial problems](https://mlsanity.com/paper/2205.07129/) (2022)
- [A Unifying Framework for Learning Argumentation Semantics](https://mlsanity.com/paper/2310.12309/) (2023)
- [A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming](https://mlsanity.com/paper/2208.03095/) (2022)
- [RECAP-KG: Mining Knowledge Graphs from Raw GP Notes for Remote COVID-19 Assessment in Primary Care](https://mlsanity.com/paper/2306.17175/) (2023)
- [An Empirical Comparison of Cost Functions in Inductive Logic Programming](https://mlsanity.com/paper/2503.07554/) (2025)
- [Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach](https://mlsanity.com/paper/2604.06838/) (2026)
- [GUIDE: Generative Utility Inference and Decision Engine](https://mlsanity.com/paper/2609.12137/) (2026)
- [VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails](https://mlsanity.com/paper/2610.12813/) (2026)

## Abstract (arXiv)

In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the datasets ILASP3 achieves a higher accuracy than other ILP systems that have previously been applied to the datasets, including a recently proposed differentiable learning framework.