Intelligent and Dependable Decision-Making Under Uncertainty

This talk highlights our vision of foundational and application-driven research toward safety, dependability, and correctness in artificial intelligence (AI). We take a broad stance on AI that combines formal methods, machine learning, and control theory. As part of this research line, we study problems inspired by autonomous systems, planning in robotics, and industrial applications. We consider reinforcement learning (RL) as a specific machine learning technique for decisionmaking under uncertainty. RL generally learns to behave optimally via trial and error. Consequently, and despite its massive success in the past years, RL lacks mechanisms to ensure safe and correct behavior. Formal methods, in particular formal verification, is a research area that provides formal guarantees of a system's correctness and safety based on rigorous methods and precise specifications. Yet, fundamental challenges have obstructed the effective application of verification to reinforcement learning. Our main objective is to devise novel, data-driven verification methods that tightly integrate with RL. In particular, we develop techniques that address real-world challenges to the safety of AI systems in general: Scalability, expressiveness, and robustness against the uncertainty that occurs when operating in the real world. The overall goal is to advance the real-world deployment of reinforcement learning.