Machine Learning

, Volume 86, Issue 1, pp 3–23

ILP turns 20

Biography and future challenges
  • Stephen Muggleton
  • Luc De Raedt
  • David Poole
  • Ivan Bratko
  • Peter Flach
  • Katsumi Inoue
  • Ashwin Srinivasan
Article

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

Abstract

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.

Keywords

Inductive Logic Programming(Statistical) relational learningStructured data in Machine Learning
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Copyright information

© The Author(s) 2011

Authors and Affiliations

  • Stephen Muggleton
    • 1
  • Luc De Raedt
    • 2
  • David Poole
    • 3
  • Ivan Bratko
    • 4
  • Peter Flach
    • 5
  • Katsumi Inoue
    • 6
  • Ashwin Srinivasan
    • 7
  1. 1.Imperial College LondonLondonUK
  2. 2.Katholieke Universiteit LeuvenLeuvenBelgium
  3. 3.University of British ColumbiaVancouverCanada
  4. 4.University of LjubljanaLjubljanaSlovenia
  5. 5.University of BristolBristolUK
  6. 6.National Institute of InformaticsTokyoJapan
  7. 7.South Asian UniversityNew DelhiIndia