Statistical Relational Learning: an Inductive Logic Programming Perspective

In the past few years there has been a lot of work lying at the intersection of probability theory, logic programming and machine learning [14, 18, 13, 9, 6, 1, 11]. This work is known under the names of statistical relational learn-ing [7, 5], probabilistic logic learning [4], or probabilistic inductive logic pro-gramming. Whereas most of the existing works have started from a probabilistic learning perspective and extended probabilistic formalisms with relational as-pects, I shall take a different perspective, in which I shall start from inductive logic programming and study how inductive logic programming formalisms, set-tings and techniques can be extended to deal with probabilistic issues. This tradition has already contributed a rich variety of valuable formalisms and tech-niques, including probabilistic Horn abduction by David Poole, PRISMs by Sato, stochastic logic programs by Muggleton [13] and Cussens [2], Bayesian logic pro-grams [10, 8] by Kersting and De Raedt, and Logical Hidden MarkovModels [11]. The main contribution of this talk is the introduction of three probabilistic inductive logic programming settings which are derived from the learning fro