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Inductive Logic Programming

Inductive Logic Programming: Derivations, Successes and Shortcomings.
Muggleton, S.
ECML93. 1993. 21-38.

Inductive Logic Programming.
Muggleton, S.
Muggleton, S. AP. 1992. 3-28.

Guest editorial to the special Issue on Inductive Logic Programming.
L. De Raedt and C.D. Page and S. Wrobel.
2001. 5-6.

  • Knowledge Discovery and Machine Learning
  • Computational Bioinformatics Laboratory (CBL)
  • University of York
  • Machine Learning and Natural Language Processing Lab

  • Jim Cunningham
  • Stephen H. Muggleton
  • James Cussens
  • Luc de Raedt
  • Stefan Wrobel

  • Inductive Logic Programming European Scientific Network (ILPNET)

Inductive Logic Programming (ILP) is a research area formed at the intersection of Machine Learning and Logic Programming. ILP systems develop predicate descriptions from examples and background knowledge. The examples, background knowledge and final descriptions are all described as logic programs. A unifying theory of Inductive Logic Programming is being built up around lattice-based concepts such as refinement, least general generalisation, inverse resolution and most specific corrections. In addition to a well established tradition of learning-in-the-limit results, some results within Valiant's PAC-learning framework have been demonstrated for ILP systems. U-learnabilty, a new model of learnability, has also been developed.