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The ILASP system for Inductive Learning of Answer Set Programs

arXiv 2005.00904 ↗First posted 2 May 2020cs.AI cs.LGcited by 27

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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.

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

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Citing papers per year: 2021: 5; 2022: 4; 2023: 3; 2024: 4; 2025: 5; 2026: 6.

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  1. 2021Inductive logic programming at 30cited by 47
  2. 2021Neuro-Symbolic Hierarchical Rule Inductioncited by 23
  3. 2025NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domainscited by 15
  4. 2024The Role of Foundation Models in Neuro-Symbolic Learning and Reasoningcited by 12
  5. 2021pix2rule: End-to-end Neuro-symbolic Rule Learningcited by 9
  6. 2021Lifting Symmetry Breaking Constraints with Inductive Logic Programmingcited by 4
  7. 2023Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networkscited by 4
  8. 2025Question Answering with LLMs and Learning from Answer Setscited by 4
  9. 2023Do Machine Learning Models Learn Statistical Rules Inferred from Data?cited by 3
  10. 2021HySTER: A Hybrid Spatio-Temporal Event Reasonercited by 2
  11. 2022Efficient lifting of symmetry breaking constraints for complex combinatorial problemscited by 2
  12. 2024DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimationcited by 2
15 more
  1. 2024CON-FOLD -- Explainable Machine Learning with Confidencecited by 2
  2. 2025Predicate Renaming via Large Language Modelscited by 2
  3. 2022Rule Learning by Modularitycited by 1
  4. 2024Declarative Knowledge Distillation from Large Language Models for Visual Question Answering Datasetscited by 1
  5. 2022Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Roboticscited by 0
  6. 2022A Model-Oriented Approach for Lifting Symmetries in Answer Set Programmingcited by 0
  7. 2023Inductive Learning of Declarative Domain-Specific Heuristics for ASPcited by 0
  8. 2025Improving ASP-based ORS Schedules through Machine Learning Predictionscited by 0
  9. 2025Inductive Learning for Possibilistic Logic Programs Under Stable Modelscited by 0
  10. 2026XAI-LAW: A Logic Programming Tool for Modeling, Explaining, and Learning Legal Decisionscited by 0
  11. 2026BLAST: Benchmarking LLMs with ASP-based Structured Testingcited by 0
  12. 2026AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programmingcited by 0
  13. 2026An Unofficial FastLAS Tutorial: A Programmer's Guidecited by 0
  14. 2026GUIDE: Generative Utility Inference and Decision Enginecited by 0
  15. 2026HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classificationcited by 0