Dagstuhl seminar 14381

Probabilistic logic programs combine the power of a programming language with a possible world semantics, typically based on Sato's distribution semantics and they have been studied for over twenty years. In this talk, I introduced the concepts underlying probabilistic programming, their semantics, different inference and learning mechanisms. I then reported on recent progress within this paradigm. This was concerned with an extension towards dealing with continuous distributions as well as coping with dynamics. This is the framework of distributional clauses that has been applied to several applications in robotics, for tracking relational worlds in which objects or their properties are occluded in real time. Finally, some remaining open challenges were discussed.status: publishe