Friday, September 30 | 10:00 AM - 11:00 AM
Preston M. Green Hall, Rodin Auditorium, Room 0120
135 N Skinker Blvd, St. Louis, MO 63112, USA
Autonomous systems are poised to become an integral part of our economy, infrastructure, and society. They are rapidly gaining more capabilities via AI components and serving in ever more safety-critical roles. However, as their complexities grow, the more impossible it becomes to provide safety guarantees for their decisions and overall performance. In this talk, I argue that formal methods combined with control theory and machine learning techniques can form powerful tools to address this problem. To this end, I present our recent contributions that focus on data-driven approaches to formal analysis and control of highly uncertain systems. Specifically, I focus on a framework for correct-by-construction control synthesis based on Gaussian regression, finite abstraction, and automata learning. I also discuss the importance of explanations in autonomous decision making and present our recently developed explanation scheme that provides easily-interpretable and verifiable explanations in the context of multi-agent planning.
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