The best of both worlds: Machine learning meets logical reasoning

May 16, 2018

Versione stampabile

Time: 2pm-3pm 
Location: Room A208, Via Sommarive 5 - Polo Ferrari 1 (Povo, TN)

Speaker

  • Prof. Holger H. Hoos - Leiden Institute of Advanced Computer Science (LIACS), The Netherlands and University of British Columbia, Canada

Abstract

Machine learning and logical inference are foundational pillars of artificial intelligence. Research in both areas has blossomed over the last twenty years and produced significant, rapidly increasing impact in many important applications. While machine learning and logical inference both play a key role in the AI revolution that is currently underway, on the surface, they seem rather different worlds. In this talk, I will explore an intriguing connection between those worlds: the use of machine learning for the automated design and analysis of logical inference engines. I will use prominent work from SAT solving to illustrate how this connection can be leveraged, from performance modelling and prediction to automated selection, configuration and parallelisation of state-of-the-art SAT solvers. I will then explain how this line of work brings about a fundamental change in the way in which we design and deploy not only cutting-edge solvers for SAT and its important generalisations, but algorithms and software for a broad range of problems from AI and beyond. I will also introduce the concept of automated AI (or AutoAI), which promises to make cutting edge AI techniques more accessible, more effective and more broadly applicable. 

About the Speaker 

Holger H. Hoos is Professor of Machine Learning at Universiteit Leiden (the Netherlands) and Adjunct Professor of Computer Science at the University of British Columbia (Canada). He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and past president of the Canadian Association for Artificial Intelligence / Association pour l’intelligence artificielle au Canada (CAIAC). His main research interests span empirical algorithmics, artificial intelligence, bioinformatics and computer music. He is known for his work on machine learning and optimisation methods for the automated design of high-performance algorithms and for his work on stochastic local search. Holger is a co-author of the book "Stochastic Local Search: Foundations and Applications", and his research has been published in numerous book chapters, journals, and at major conferences in artificial intelligence, operations research, molecular biology and computer music. For further information, see http://ada.liacs.nl

Contact Person Regarding this Talk: andrea.passerini [at] unitn.it (Andrea Passerini)