"Probabilistic ILP" - Tutorial

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Abstract

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Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed.

This tutorial provides an introduction to and an overview of the state-of-the-art in statistical relational learning. We start from classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs, and show how they can be extended with probabilistic methods. While doing so, we review state-of-the-art statistical relational learning approaches and show how they fit the discussed learning settings for probabilistic inductive logic programming.

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Keywords

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Tutorial notes

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• L. De Raedt, K. Kersting. Probabilistic Inductive Logic Programming. Invited paper in S. Ben-David, J. Case and A. Maruoka, editors, Proceedings of the 15th International Conference on Algorithmic Learning Theory (ALT-2004), pages 19-36. Padova, Italy, October 2-5, 2004.


• L. De Raedt, K. Kersting. Probabilistic Logic Learning. In ACM-SIGKDD Explorations, special issue on Multi-Relational Data Mining, Vol. 5(1), pp. 31-48, July 2003, [link]

• IDA 2005 tutorial notes [.pdf]

• ECML/PKDD 2005 tutorial handouts (2 slides/page)[.pdf]
• ECML/PKDD 2005 tutorial webouts (1 slide / page)[.pdf]