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Engineering safe and trustworthy autonomous systems demand formal methodologies that accommodate statistical models and assumptions. In the first part of this talk, I will present safety verification methods that use reachability analysis. This research explores a spectrum of model assumptions and the corresponding guarantees and utilities. The methods combine simulation data with sensitivity analysis. At one end, complete dynamical models offer strong guarantees, but their utility can be limited. Learning sensitivity from data comes with statistical guarantees, and the algorithms are applicable to real-world scenarios, and still confer the benefits of formal reasoning via composition and abstraction. This flexible approach informs our current work on the Verse framework which aims to make code-level analysis for autonomous multi-agent scenarios accessible to undergraduates in engineering design courses.
Sayan Mitra (Thu,) studied this question.