Inductive logic programming at 30

Andrew Cropper, Sebastijan Dumančić, Richard Evans, Stephen H. Muggleton

Research output: Contribution to journalArticleScientificpeer-review

22 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)147-172
Number of pages26
JournalMachine Learning
Issue number1
Publication statusPublished - 2021
Externally publishedYes


  • Inductive logic programming
  • Program induction
  • Program synthesis
  • Relational learning


Dive into the research topics of 'Inductive logic programming at 30'. Together they form a unique fingerprint.

Cite this