8 April 2019 - Angelika Kimmig (Cardiff University)

Abstract

Reasoning with relational data, learning, and dealing with uncertainty are central to many aspects of AI. Their combination is studied under a variety of names, and a broad range of languages and tools have been developed. Probabilistic logic programming achieves this combination by extending the representation and reasoning capabilities of logic programming to settings with uncertain data. This talk provides a gentle introduction to the field, and also touches upon applications and challenges.

Biography

Angelika Kimmig is a Lecturer at Cardiff University, UK. She obtained her Ph.D. from KU Leuven, Belgium, and was a postdoctoral fellow at KU Leuven and the University of Maryland, College Park. Her research interests include symbolic AI, reasoning under uncertainty, machine learning, logic programming, and especially combinations thereof such as probabilistic programming and statistical relational learning. She is a key contributor to the probabilistic logic programming language ProbLog.