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AIC Seminar Series

From Probabilistic Logics to Neuro-Symbolic Artificial Intelligence

Luc De RaedtKU Leuven[Home Page]

Notice:  Hosted by Rodrigo de Salvo Braz

Date:  Wednesday, October 21st 2020 at 10:00am

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   Abstract

A central challenge to contemporary AI is to integrate learning and reasoning. The integration of learning and reasoning has been studied for decades already in the fields of statistical relational artificial intelligence and probabilistic programming. StarAI has focussed on unifying logic and probability, the two key frameworks for reasoning, and has extended this probabilistic logics machine learning principles. I will argue that StarAI and Probabilistic Logics form an ideal basis for developing neuro-symbolic artificial intelligence techniques. Thus neuro-symbolic computation = StarAI + Neural Networks. Many parallels will be drawn between these two fields and will be illustrated using the Deep Probabilistic Logic Programming language DeepProbLog.

   Bio for Luc De Raedt

Prof. Dr. Luc De Raedt is currently Director of Leuven.AI, the KU Leuven Institute for AI, full professor of Computer Science at KU Leuven, and guestprofessor at Örebro University (Sweden) at the Center for Applied Autonomous Sensor Systems in the Wallenberg AI, Autonomous Systems and Software Program.

Luc De Raedt obtained his PhD in Computer Science from the KU Leuven (1991), was post-doctoral researcher of the Fund for Scientific Research, Flanders (FWO) (1991-99) and part-time assistant/associate professor (1993-1999) KU Leuven; full professor (C4) and Chair of the Machine Learning and Natural Language Processing Lab at the Albert-Ludwigs-University Freiburg, Germany (1999-2006); head of the Lab for Declarative Languages and Artificial intelligence at KU Leuven from (2015-2019).

Luc De Raedt’s research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is well known for his contributions in the areas of learning and reasoning, in particular, for his contributions to statistical relational learning, probabilistic and inductive programming. Today he is working on the next generation of programming languages, which can automatically learn from data, on combining probabilistic and logical reasoning and learning, on the automation of (data) science, and on verifying learning artificial intelligence systems and robotics. He is also now also focusing on integrating the probabilistic logics with neural networks and wants to apply these to reinforcement learning as well as program induction.

Luc De Raedt is proud of his students. He has (co)advised 27 Phd students and 17 post-docs. Many of his students have won prestigious awards, including an ERC Starting Grant (Jan Ramon), four ECCAI Dissertation awards for the best European thesis in AI (Kristian Kersting, Guy Van den Broeck, Niels Landwehr, Tias Guns), two IBM Belgium awards (Guy Van den Broeck and Niels Landwehr), the Dissertation Award of the Association for Constraint Programming (Tias Guns) and the Prize of the KU Leuven Research Council (Guy Van den Broeck). Guy Van den Broeck is also the 2019 winner of the IJCAI Computers and Thought Award, the premier award in the field of AI for researchers under the age of 35. Ten former mentees are now faculty members or independent research directors.

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