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Inductive logic programming at 30

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Cropper, Andrew, Dumančić, Sebastijan, Evans, Richard, Muggleton, Stephen H.·2022·Machine Learning
DOI 10.1007/s10994-021-06089-1
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Abstract

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 and background knowledge. 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.

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  • As BK grows, the system faces the relevance problem: without a mechanism to judge which prior knowledge applies to a new task, accumulation leads to catastrophic remembering, where irrelevant BK degrades both search efficiency and learning performance.

    “However, as BK grows, the system faces the relevance problem [1]: without a mechanism to judge which prior knowledge applies to a new task, accumulation leads to catastrophic remembering [5], where irrelevant BK degrades both search efficiency and learning performance.”

    From ADVENT: LLM-Driven Automatic Predicate Invention for ILP (2026) · cites this paper as [1]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • ILP's performance depends heavily on predefined background knowledge (BK).

    “However, ILP's performance depends heavily on predefined background knowledge (BK) [1], a set of predicates that encode known facts and relational structures about the domain, analogous to features in traditional ML.”

    From ADVENT: LLM-Driven Automatic Predicate Invention for ILP (2026) · cites this paper as [1]

  • Existing PI methods rely on domain expertise.

    “Existing methods rely on domain expertise and produce semantically opaque predicates, hindering adaptation to unfamiliar domains and cross-task reuse.”

    From ADVENT: LLM-Driven Automatic Predicate Invention for ILP (2026) · cites this paper as [1]

  • ILP's compositional structure naturally supports knowledge reusability across tasks: hypotheses learned from earlier tasks can serve as BK for subsequent ones.

    “ILP's compositional structure naturally supports this: hypotheses learned from earlier tasks can serve as BK for subsequent ones [1].”

    From ADVENT: LLM-Driven Automatic Predicate Invention for ILP (2026) · cites this paper as [1]

  • Predicate invention (PI) remains a critical bottleneck in Inductive Logic Programming (ILP).

    “Predicate invention (PI), the creation of new predicates to extend the hypothesis space, remains a critical bottleneck in Inductive Logic Programming (ILP).”

    From ADVENT: LLM-Driven Automatic Predicate Invention for ILP (2026) · cites this paper as [1]

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Cited by

Sorted by citations

Authors
Cropper, A., Dumančić, S., Evans, R., & Muggleton, S. H.
Affiliations
  • University of Oxford (GB)
  • KU Leuven (BE)
  • Imperial College London (GB)
Corresponding author
Cropper, Andrew
ORCID
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
2021-11-09
Volume
111
Issue
1
Pages
147-172
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
DOI
10.1007/s10994-021-06089-1
OpenAlex
W3212891530
Cited by
61
References
16
Citations by year
2026: 9 · 2025: 17 · 2024: 13 · 2023: 10 · 2022: 10 · 2021: 1 · 2019: 1
FWCI
2.98
Type
journal article
Language
en
Open access
Licence
Locations
  • Machine Learning (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • Lirias (KU Leuven) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
Engineering and Physical Sciences Research Council (EP/V040340/1)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
Keywords
inductive logic programming, predicate invention, prior knowledge

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