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PhD Thesis Proposal- Xin Qin

PhD Thesis Proposal- Xin Qin
Title: Data-driven and Logic-based Analysis of Learning-enabled Cyber-Physical Systems
Committee: Jyotirmoy Deshmukh, Chao Wang, Souti Chattopadhyay, Yan Liu and Paul Bogdan
 

Abstract: Rigorous analysis of cyber-physical systems (CPS) is becoming increasingly important, especially for safety-critical applications that use learning-enabled components. In this proposal, we will discuss various pieces of a broad framework that enable scalable reasoning techniques tuned to modern software design practices in autonomous CPS applications. The proposal will center around three main pillars: (1) Statistical verification techniques to give probabilistic guarantees on system correctness; here, we treat the underlying CPS application as a black-box and use distribution-free and model-free techniques to provide probabilistic correctness guarantees. (2) Predictive monitoring techniques that use physics-based or data-driven models of the system to continuously monitor logic-based requirements of systems operating in highly uncertain environments; this allows us to design runtime mitigation approaches to take corrective actions before a safety violation can occur. (3) Robust testing for CPS using reinforcement learning. We train an agent to produce a policy to initiate unsafe behaviors in similar target systems without the need for retraining, thereby allowing for the elicitation of faulty behaviors across various systems.  The proposal hopes to demonstrate the scalability of our approaches on various realistic models of autonomous systems.

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 349

Audiences: Everyone Is Invited

Contact: Xin Qin