Towards Probabilistic (Logic) Programming for Robotics

Abstract

Recently, the field of AI has devoted a lot of attention to statistical relational learning and probabilistic programming, which provide rich representations for coping with uncertainty, with structure and for learning.  The reason for the current interest is that they promise to bridge the gap between high-level reasoning and low-level perception.

In this talk, I shall introduce the concepts underlying probabilistic logic programming, their semantics, as well as different inference and learning mechanisms, also for use in dynamic settings.  Most of all I shall sketch some emerging applications of these languages in robotics, where probabilistic programming is used for tracking relational worlds in which objects or their properties are occluded in real time, and for planning in complex (relational) Markov Decision Processes.  We have also employed probabilistic programs to learn complex "relational" affordance models involving multiple objects and their context in a robotics setting.

About the speaker

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.