Probabilistic Inductive Logic Programming
(invited lecture for ALT 2004)

Author: Luc De Raedt

Affiliation: Institute for Computer Science, Universität Freiburg, Freiburg, Germany

Abstract. 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.

In this talk, I shall start from an inductive logic programming perspective and sketch how it can be extended with probabilistic methods. More specifically, I shall outline three settings for inductive logic programming: learning from entailment, learning from interpretations and learning from proofs or traces and show how they can be used to learn different types of probabilistic representations.

The learning from entailment setting is natural when learning stochastic context free grammars and their upgrade, stochastic logic programs, the learning from interpretations settings is the method of choice when learning bayesian networks or bayesian logic programs, and learning from proofs or traces correspond to learning (hidden) markov models and their first order upgrades. The resulting settings will also be illustrated using various real-life examples from the field of bio-informatics.

This is joint work with Kristian Kersting and part of the EU project APRIL II (Application of Probabilistic Inductive Logic Programming II).

The talk will partly be based on

De Raedt, L., Kersting, K., Probabilistic Logic Learning, SIGKDD Explorations, Vol. 5(1), 2003.

©Copyright 2004 Author