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Building Certifiably Safe and Correct Large-scale Autonomous Systems

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VSO2 - Verified software

The introduction of machine learning (ML) and artificial intelligence (AI) creates unprecedented opportunities for achieving full autonomy. However, learning-based methods in building autonomous systems can be extremely brittle in practice and are not designed to be verifiable. In this talk, I will present several of our recent efforts that combine ML with formal methods and control theory to enable the design of provably dependable and safe autonomous systems. I will introduce our techniques to generate safety certificates and certified decision and control for complex autonomous systems, even when the systems have a large number of agents and follow nonlinear or unknown dynamics.

This talk is part of the Isaac Newton Institute Seminar Series series.

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