Safe AI for CPS (Invited Paper)

Abstract

Autonomous cyber-physical systems-such as self-driving cars and autonomous drones-often leverage artificial intelligence and machine learning algorithms to act well in open environments. Although testing plays an important role in ensuring safety and robustness, modern autonomous systems have grown so complex that achieving safety via testing alone is intractable. Formal verification reduces this testing burden by ruling out large classes of errant behavior at design time. This paper reviews recent work toward developing formal methods for cyber-physical systems that use AI for planning and control by combining the rigor of formal proofs with the flexibility of reinforcement learning.