# The ILASP system for Inductive Learning of Answer Set Programs

arXiv: https://arxiv.org/abs/2005.00904 · HTML: https://mlsanity.com/paper/2005.00904/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 27 of 27)

- [Inductive logic programming at 30](https://mlsanity.com/paper/2102.10556/) (2021)
- [Neuro-Symbolic Hierarchical Rule Induction](https://mlsanity.com/paper/2112.13418/) (2021)
- [NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domains](https://mlsanity.com/paper/2503.00870/) (2025)
- [The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning](https://mlsanity.com/paper/2402.01889/) (2024)
- [pix2rule: End-to-end Neuro-symbolic Rule Learning](https://mlsanity.com/paper/2106.07487/) (2021)
- [Lifting Symmetry Breaking Constraints with Inductive Logic Programming](https://mlsanity.com/paper/2112.11806/) (2021)
- [Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks](https://mlsanity.com/paper/2310.13073/) (2023)
- [Question Answering with LLMs and Learning from Answer Sets](https://mlsanity.com/paper/2509.16590/) (2025)
- [Do Machine Learning Models Learn Statistical Rules Inferred from Data?](https://mlsanity.com/paper/2303.01433/) (2023)
- [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)
- [DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation](https://mlsanity.com/paper/2406.00611/) (2024)
- [CON-FOLD -- Explainable Machine Learning with Confidence](https://mlsanity.com/paper/2408.07854/) (2024)
- [Predicate Renaming via Large Language Models](https://mlsanity.com/paper/2510.25517/) (2025)
- [Rule Learning by Modularity](https://mlsanity.com/paper/2212.12335/) (2022)
- [Declarative Knowledge Distillation from Large Language Models for Visual Question Answering Datasets](https://mlsanity.com/paper/2410.09428/) (2024)
- [Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics](https://mlsanity.com/paper/2201.10266/) (2022)
- [A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming](https://mlsanity.com/paper/2208.03095/) (2022)
- [Inductive Learning of Declarative Domain-Specific Heuristics for ASP](https://mlsanity.com/paper/2308.15863/) (2023)
- [Improving ASP-based ORS Schedules through Machine Learning Predictions](https://mlsanity.com/paper/2507.16454/) (2025)
- [Inductive Learning for Possibilistic Logic Programs Under Stable Models](https://mlsanity.com/paper/2510.07069/) (2025)
- [XAI-LAW: A Logic Programming Tool for Modeling, Explaining, and Learning Legal Decisions](https://mlsanity.com/paper/2601.03844/) (2026)
- [BLAST: Benchmarking LLMs with ASP-based Structured Testing](https://mlsanity.com/paper/2604.22306/) (2026)
- [AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming](https://mlsanity.com/paper/2606.24245/) (2026)
- [An Unofficial FastLAS Tutorial: A Programmer's Guide](https://mlsanity.com/paper/2607.23557/) (2026)
- [GUIDE: Generative Utility Inference and Decision Engine](https://mlsanity.com/paper/2609.12137/) (2026)
- [HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification](https://mlsanity.com/paper/2609.37740/) (2026)

## Abstract (arXiv)

The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples in the context of some pre-existing background knowledge. Until recently, most research on ILP targeted learning Prolog programs. Our own ILASP system instead learns Answer Set Programs, including normal rules, choice rules and hard and weak constraints. Learning such expressive programs widens the applicability of ILP considerably; for example, enabling preference learning, learning common-sense knowledge, including defaults and exceptions, and learning non-deterministic theories. In this paper, we first give a general overview of ILASP's learning framework and its capabilities. This is followed by a comprehensive summary of the evolution of the ILASP system, presenting the strengths and weaknesses of each version, with a particular emphasis on scalability.