Machine Learning

, Volume 86, Issue 1, pp 3–23

ILP turns 20

Biography and future challenges


    • Imperial College London
  • Luc De Raedt
    • Katholieke Universiteit Leuven
  • David Poole
    • University of British Columbia
  • Ivan Bratko
    • University of Ljubljana
  • Peter Flach
    • University of Bristol
  • Katsumi Inoue
    • National Institute of Informatics
  • Ashwin Srinivasan
    • South Asian University

DOI: 10.1007/s10994-011-5259-2

Cite this article as:
Muggleton, S., De Raedt, L., Poole, D. et al. Mach Learn (2012) 86: 3. doi:10.1007/s10994-011-5259-2


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

Copyright information

© The Author(s) 2011