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"Probabilistic Logic Learning" - Tutorial
Abstract
The past few years have witnessed a significant interest in probabilistic logic learning,
i.e. in research lying at the intersection of probabilistic reasoning, logical representations,
and machine learning. A rich variety of different formalisms and learning techniques have
been developed. This tutorial provides an introductory survey and overview of the
state-of-the-art in probabilistic logic learning through the identification of
a number of important probabilistic, logical and learning concepts.
Keywords
Inductive Logic Porgamming, Bayesian Networks, Hidden Markov Models, Stochastic grammars,
Logic Programming
Tutorial notes
The tutorial builds on
Probabilistic Logic Learning, L. De Raedt, K. Kersting, in ACM-SIGKDD Explorations,
special issue on Multi-Relational Data Mining, Vol. 5(1), pp. 31-48, July 2003.
The tutorial nodes will be a sub-sample of the following material.
Material will be added and might be modified.
 Supported by the European Comission, APrIL II project "Application of Probabilistic Inductive Logic Programming II", Contract no. FP6-508861,under the "Sixth Framework Programme (2002-2006); Information Society Technologies"",Future and Emerging Technologies" arm.
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