Probabilistic logic programming concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. In this talk I shall focus on probabilistic extensions of logic programming languages such as Prolog, which have been developed since more than 20 years. One advantage of such languages is that they naturally fit both the statistical relational learning / artificial intelligence perspective as they define probability distributions over logical interpretations as well as that of probabilistic programming languages. During this talk I shall give an overview of the underlying concepts and semantics of these languages as well as sketch their current inference and learning mechanisms. This talk will be largely based on joint tutorials with Angelika Kimmig and the forthcoming survey paper : Luc De Raedt, Angelika Kimmig, Probabilistic (Logic) Programming Concepts, Machine Learning, in press. An early version of this paper can be found at http://arxiv.org/abs/1312.4328.Invited talk.status: publishe