Machine Learning

, Volume 86, Issue 1, pp 3-23

First online:

ILP turns 20

Biography and future challenges
  • Stephen MuggletonAffiliated withImperial College London Email author 
  • , Luc De RaedtAffiliated withKatholieke Universiteit Leuven
  • , David PooleAffiliated withUniversity of British Columbia
  • , Ivan BratkoAffiliated withUniversity of Ljubljana
  • , Peter FlachAffiliated withUniversity of Bristol
  • , Katsumi InoueAffiliated withNational Institute of Informatics
  • , Ashwin SrinivasanAffiliated withSouth Asian University


Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the development of novel and challenging real-world applications. Lastly, by projection we suggest directions for research which will help the subject coming of age.


Inductive Logic Programming (Statistical) relational learning Structured data in Machine Learning