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Abstract:

Recently, there has been a lot of attention for statistical relational learning and probabilistic programming, which provide rich representations for coping with uncertainty, with structure and for learning. In this talk I shall focus on probabilistic *logic* programming languages, which naturally belong to both of these paradigms as they combine the power of a programming language with a possible world semantics. They are typically based on Sato’s distribution semantics and they have been studied for over twenty years now. In this talk, I shall introduce the concepts underlying probabilistic logic programming, their semantics, different inference and learning mechanisms and I shall then present some recent extensions towards dealing with continuous distributions and dynamics. I shall also sketch some emerging applications in bioinformatics, where it is used to analyse molecular profiling data in networks, and in robotics, where it is used for tracking relational worlds in which objects or their properties are occluded in real time, and to planning. Finally, I shall discuss some open challenges and opportunities for research.