An inductive logic programming approach to statistical relational learning

Article type: Other

Authors: Kersting, Kristian

Affiliations: Institut für Informatik, Albert-Ludwigs-Universität, Georges-Köhler-Allee, Building 079, 79110 Freiburg, Germany E-mail: [email protected]

Abstract: Statistical relational learning (SRL) addresses one of the central open questions of AI: the combination of relational or first-order logic with principled probabilistic and statistical approaches to inference and learning. This thesis approaches SRL from an inductive logic programming (ILP) perspective and starts with developing a general framework for SRL: probabilistic ILP. 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 ILP over time and for making complex decision in relational domains. Finally, it is shown that SRL approaches naturally yield kernels for structured data. The resulting approaches are illustrated using examples from genetics, bioinformatics, and planning domains.

Keywords: Machine Learning, Statistical Learning, Reinforcement Learning, Relational Learning, Inductive Logic Programming, Statistical Relational Learning

Journal: AI Communications, vol. 19, no. 4, pp. 389-390, 2006