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 analyze 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.
Luc De Raedt is a full professor (of research) at the University of Leuven (KU Leuven) in the Department of Computer Science and a former chair of Machine Learning at the Albert-Ludwigs-University in Freiburg. Luc De Raedt has been working in the areas of artificial intelligence and computer science, especially on computational logic, machine learning and data mining, probabilistic reasoning and constraint programming and their applications in bio- and chemo-informatics, vision and robotics, natural language processing, and engineering. His work has typically crossed boundaries between different research areas, often working towards an integration of their principles. He is well-known for his work on inductive logic programming (combining logic with learning). Since 2000, he has been working towards a further integration of logical and relational learning with probabilistic reasoning (statistical relational learning and probabilistic programming), on inductive querying in databases, and on using declarative languages for data mining and machine learning. He was program (co)-chair of ECAI 2012, ICML 2005 and ECML/PKDD 2001 and he is an ECCAI fellow.