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Inductive logic programming at 30: a new introduction

arXiv 2008.07912 ↗First posted 18 Aug 2020cs.AI cs.LGcited by 83

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Abstract (arXiv)

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Aug 2020.

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Citing papers per year: 2021: 8; 2022: 11; 2023: 15; 2024: 13; 2025: 19; 2026: 17.

83 papers.

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  1. 2023Generalized Planning in PDDL Domains with Pretrained Large Language Modelscited by 139
  2. 2023Scallop: A Language for Neurosymbolic Programmingcited by 63
  3. 2021Learning Symbolic Operators for Task and Motion Planningcited by 57
  4. 2021Inductive logic programming at 30cited by 46
  5. 2023Learning Transformer Programscited by 40
  6. 2024Planning with Multi-Constraints via Collaborative Language Agentscited by 26
  7. 2021Neuro-Symbolic Hierarchical Rule Inductioncited by 23
  8. 2021EvoLearner: Learning Description Logics with Evolutionary Algorithmscited by 17
  9. 2022FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainabilitycited by 15
  10. 2023Learning MDL logic programs from noisy datacited by 15
  11. 2025NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domainscited by 15
  12. 2024Neuro-symbolic Learning Yielding Logical Constraintscited by 12
38 more
  1. 2026AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMscited by 12
  2. 2024Learning with Logical Constraints but without Shortcut Satisfactioncited by 10
  3. 2021Learning Symbolic Rules for Reasoning in Quasi-Natural Languagecited by 9
  4. 2024DeiSAM: Segment Anything with Deictic Promptingcited by 9
  5. 2023V-LoL: A Diagnostic Dataset for Visual Logical Learningcited by 8
  6. 2021A Critical Review of Inductive Logic Programming Techniques for Explainable AIcited by 7
  7. 2022FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Datacited by 7
  8. 2022Learning logic programs by combining programscited by 7
  9. 2023Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approachcited by 7
  10. 2024Learning Guided Automated Reasoning: A Brief Surveycited by 7
  11. 2024Relational decomposition for program synthesiscited by 6
  12. 2023Generalisation Through Negation and Predicate Inventioncited by 5
  13. 2024Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxationcited by 5
  14. 2021Lifting Symmetry Breaking Constraints with Inductive Logic Programmingcited by 4
  15. 2022Learning logic programs by discovering where not to searchcited by 4
  16. 2025Question Answering with LLMs and Learning from Answer Setscited by 4
  17. 2024Questionable practices in machine learningcited by 3
  18. 2025Abductive Logical Rule Induction by Bridging Inductive Logic Programming and Multimodal Large Language Modelscited by 3
  19. 2022An Interactive Explanatory AI System for Industrial Quality Controlcited by 2
  20. 2022Efficient lifting of symmetry breaking constraints for complex combinatorial problemscited by 2
  21. 2022Learning programs with magic valuescited by 2
  22. 2023Reason to explain: Interactive contrastive explanations (REASONX)cited by 2
  23. 2023Generating by Understanding: Neural Visual Generation with Logical Symbol Groundingscited by 2
  24. 2025Hypothesis Generation via LLM-Automated Language Bias for ILPcited by 2
  25. 2025Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysiscited by 2
  26. 2025Predicate Renaming via Large Language Modelscited by 2
  27. 2026Differentiable Rule Induction from Raw Sequence Inputscited by 2
  28. 2022PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agentcited by 1
  29. 2022Rule Learning by Modularitycited by 1
  30. 2023Current and Future Challenges in Knowledge Representation and Reasoningcited by 1
  31. 2023Learning logic programs by discovering higher-order abstractionscited by 1
  32. 2023Symbolic Imitation Learning: From Black-Box to Explainable Driving Policiescited by 1
  33. 2024STAND: Self-Aware Precondition Induction for Interactive Task Learningcited by 1
  34. 2025Modelling Program Spaces in Program Synthesis with Constraintscited by 1
  35. 2025Learning Logical Rules using Minimum Message Lengthcited by 1
  36. 2025GLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoningcited by 1
  37. 2025Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challengescited by 1
  38. 2026From Circuit Evidence to Mechanistic Theory: An Inductive Logic Approachcited by 1

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