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Inductive logic programming at 30

arXiv 2102.10556 ↗First posted 21 Feb 2021cs.AI cs.LGcited by 46

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Abstract (arXiv)

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Builds on 32 of its 125 references that are in the map. First posted on arXiv 21 Feb 2021.

Builds on

32 of 125 references.

References · 32

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Neurosymbolic AI: The 3rd Wavecited by 253
  3. 2018Deep Learning: A Critical Appraisalcited by 214
  4. 2017Learning Explanatory Rules from Noisy Datacited by 210
  5. 2017End-to-End Differentiable Provingcited by 159
  6. 2016DeepCoder: Learning to Write Programscited by 145
  7. 2019Neural Logic Machinescited by 144
  8. 2015Neural GPUs Learn Algorithmscited by 118
  9. 2015Neural Programmer-Interpreterscited by 96
  10. 2020Inductive logic programming at 30: a new introductioncited by 83
  11. 2020Learning programs by learning from failurescited by 73
  12. 2020Turning 30: New Ideas in Inductive Logic Programmingcited by 42
20 more
  1. 2020Abductive Knowledge Induction From Raw Datacited by 38
  2. 2019Making sense of sensory inputcited by 29
  3. 2020The ILASP system for Inductive Learning of Answer Set Programscited by 27
  4. 2016Solving and Verifying the boolean Pythagorean Triples problem via Cube-and-Conquercited by 23
  5. 2020Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisationcited by 22
  6. 2019Logical reduction of metarulescited by 19
  7. 2019Inductive general game playingcited by 18
  8. 2019Learning Relational Representations with Auto-encoding Logic Programscited by 13
  9. 2020Beneficial and Harmful Explanatory Machine Learningcited by 13
  10. 2018Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learningcited by 12
  11. 2017Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generationcited by 11
  12. 2019Learning higher-order logic programscited by 11
  13. 2013Structure Learning of Probabilistic Logic Programs by Searching the Clause Spacecited by 10
  14. 2019Playgol: learning programs through playcited by 8
  15. 2019Forgetting to learn logic programscited by 8
  16. 2021Top Program Construction and Reduction for polynomial time Meta-Interpretive Learningcited by 6
  17. 2019Towards meta-interpretive learning of programming language semanticscited by 5
  18. 2014Incremental Learning of Event Definitions with Inductive Logic Programmingcited by 4
  19. 2016Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representationcited by 3
  20. 2018Active Inductive Logic Programming for Code Searchcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 7; 2023: 12; 2024: 11; 2025: 7; 2026: 5.

46 papers.

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  1. 2023Scallop: A Language for Neurosymbolic Programmingcited by 63
  2. 2023Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstractioncited by 45
  3. 2021From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligencecited by 35
  4. 2022Language Models as Inductive Reasonerscited by 32
  5. 2026LLMs Gaming Verifiers: RLVR can Lead to Reward Hackingcited by 24
  6. 2023Human-like Few-Shot Learning via Bayesian Reasoning over Natural Languagecited by 21
  7. 2023Can Transformers Learn to Solve Problems Recursively?cited by 17
  8. 2023Interpretable Multimodal Misinformation Detection with Logic Reasoningcited by 16
  9. 2023Learning MDL logic programs from noisy datacited by 15
  10. 2021Learning Higher-Order Programs without Meta-Interpretive Learningcited by 14
  11. 2023Scalable Neural-Probabilistic Answer Set Programmingcited by 14
  12. 2024Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursioncited by 14
34 more
  1. 2023Anti-unification and Generalization: A Surveycited by 7
  2. 2023Generalisation Through Negation and Predicate Inventioncited by 5
  3. 2024Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxationcited by 5
  4. 2022Learning logic programs by discovering where not to searchcited by 4
  5. 2024Learning big logical rules by joining small rulescited by 4
  6. 2024Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learningcited by 4
  7. 2024Program Synthesis using Inductive Logic Programming for the Abstraction and Reasoning Corpuscited by 4
  8. 2025Neural DNF-MT: A Neuro-symbolic Approach for Learning Interpretable and Editable Policiescited by 4
  9. 2022Relational program synthesis with numerical reasoningcited by 2
  10. 2023A Voting Approach for Explainable Classification with Rule Learningcited by 2
  11. 2024Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelscited by 2
  12. 2024Aligning Generalisation Between Humans and Machinescited by 2
  13. 2025When No Paths Lead to Rome: Benchmarking Systematic Neural Relational Reasoningcited by 2
  14. 2026Differentiable Rule Induction from Raw Sequence Inputscited by 2
  15. 2021Learning logic programs through divide, constrain, and conquercited by 1
  16. 2022Constraint-driven multi-task learningcited by 1
  17. 2022Trust in Language Grounding: a new AI challenge for human-robot teamscited by 1
  18. 2022Rule Learning by Modularitycited by 1
  19. 2023Learning logic programs by discovering higher-order abstractionscited by 1
  20. 2024Boolean Matrix Logic Programming on the GPUcited by 1
  21. 2021Parallel Constraint-Driven Inductive Logic Programmingcited by 0
  22. 2022An Algebraic Approach to Learning and Groundingcited by 0
  23. 2023Solving Witness-type Triangle Puzzles Faster with an Automatically Learned Human-Explainable Predicatecited by 0
  24. 2024Incremental Bootstrapping and Classification of Structured Scenes in a Fuzzy Ontologycited by 0
  25. 2024Algebraic anti-unificationcited by 0
  26. 2024Graph Learning for Planning: The Story Thus Far and Open Challengescited by 0
  27. 2025Disentangling Neural Disjunctive Normal Form Modelscited by 0
  28. 2025Inductive Learning for Possibilistic Logic Programs Under Stable Modelscited by 0
  29. 2025RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Modelscited by 0
  30. 2025Student Engagement in AI Assisted Complex Problem Solving: A Pilot Study of Human AI Rubik's Cube Collaborationcited by 0
  31. 2025Satisfiability Modulo Theory Meets Inductive Logic Programmingcited by 0
  32. 2026Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answeringcited by 0
  33. 2026Project Auto-World: Towards Automated Benchmarking of Neural Relational Reasonerscited by 0
  34. 2026ADVENT: LLM-Driven Automatic Predicate Invention for ILPcited by 0