An Inductive Logic Programming Approach
to Statistical Relational Learning

Kristian Kersting
Albert-Ludwigs University
Institute for Computer Science
Freiburg i. Brg., Germany

IOS Press
Frontiers in Artificial Intelligence
and its Applications series (Dissertations) Series, Volume 148
Amsterdam, The Netherlands, 2006.
ISBN 1-58603-674-2
LCCN 2006932504

[@IOS Press]


Statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representation and machine learning. Recently, this questions has received a lot of attention. Several statistical relational learning approaches have been developed in related, but different areas including machine learning, statistics, databases, and reasoning under uncertainty. This thesis starts from an inductive logic programming perspective and firstly develops a general framework for statistical relational learning: probabilistic inductive logic programming. Based on this foundation, the thesis shows how to incorporate the logical concepts of objects and relations among these objects into Bayesian networks. As time and actions are not just other relations, it afterwards develops approaches to probabilistic inductive logic programming over time and for making complex decision in relational domains. More specifically, Bayesian networks are upgraded to Bayesian logic programs, hidden Markov models to logical hidden Markov models; and Markov decision processes to Markov decision programs. Furthermore, it will be shown that statistical relational learning approaches naturally yield kernels for structured data. The resulting approaches will be illustrated using examples from genetics, bio-informatics, and classical planning domains.


Preface by Stephen H. Muggleton [.pdf]
Acknowledgements [.pdf]
Table of Content [.pdf]