mlsanityData to 7 Oct 2026

Turning 30: New Ideas in Inductive Logic Programming

arXiv 2002.11002 ↗First posted 25 Feb 2020cs.AI cs.LGcited by 42

Not on the map. Why?

Abstract (arXiv)

Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work in inductive logic programming (ILP), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to learning background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 19; 2022: 8; 2023: 8; 2024: 3; 2025: 4.

42 papers.

Sort:

By area:

  1. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  2. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  3. 2021Incorporating Domain Knowledge into Deep Neural Networkscited by 56
  4. 2021Inductive logic programming at 30cited by 46
  5. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  6. 2021Differentiable Logic Machinescited by 18
  7. 2022CrossBeam: Learning to Search in Bottom-Up Program Synthesiscited by 18
  8. 2021FF-NSL: Feed-Forward Neural-Symbolic Learnercited by 15
  9. 2021Learning Security Classifiers with Verified Global Robustness Propertiescited by 14
  10. 2023Learning Differentiable Logic Programs for Abstract Visual Reasoningcited by 12
  11. 2023Assessing SATNet's Ability to Solve the Symbol Grounding Problemcited by 10
  12. 2021Learning Symbolic Rules for Interpretable Deep Reinforcement Learningcited by 9
30 more
  1. 2021Predicate Invention by Learning From Failurescited by 9
  2. 2024Transformers Meet Relational Databasescited by 9
  3. 2021A Relational Tsetlin Machine with Applications to Natural Language Understandingcited by 8
  4. 2021Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programmingcited by 8
  5. 2023V-LoL: A Diagnostic Dataset for Visual Logical Learningcited by 8
  6. 2025REDELEX: A Framework for Relational Deep Learning Explorationcited by 8
  7. 2021A Critical Review of Inductive Logic Programming Techniques for Explainable AIcited by 7
  8. 2022R5: Rule Discovery with Reinforced and Recurrent Relational Reasoningcited by 7
  9. 2021Learning Logic Programs From Noisy Failurescited by 5
  10. 2022Learning First-Order Rules with Differentiable Logic Program Semanticscited by 5
  11. 2024Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxationcited by 5
  12. 2021Lifting Symmetry Breaking Constraints with Inductive Logic Programmingcited by 4
  13. 2025Task-Agnostic Contrastive Pretraining for Relational Deep Learningcited by 4
  14. 2023Shrinking the Inductive Programming Search Space with Instruction Subsetscited by 3
  15. 2023Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approachcited by 3
  16. 2024Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AIcited by 3
  17. 2021Learning compositional programs with arguments and samplingcited by 2
  18. 2022Efficient lifting of symmetry breaking constraints for complex combinatorial problemscited by 2
  19. 2022Learning programs with magic valuescited by 2
  20. 2022PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agentcited by 1
  21. 2023Explaining with Attribute-based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgustcited by 1
  22. 2023Symbolic Imitation Learning: From Black-Box to Explainable Driving Policiescited by 1
  23. 2025Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challengescited by 1
  24. 2021A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networkscited by 0
  25. 2021Incorporation of Deep Neural Network & Reinforcement Learning with Domain Knowledgecited by 0
  26. 2021On the Complexity of Inductively Learning Guarded Rulescited by 0
  27. 2021Preprocessing in Inductive Logic Programmingcited by 0
  28. 2022A Model-Oriented Approach for Lifting Symmetries in Answer Set Programmingcited by 0
  29. 2022Programming by Example and Text-to-Code Translation for Conversational Code Generationcited by 0
  30. 2025Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoningcited by 0