Probabilistic Verification

Probabilistic verification aims to formally analyze the reliability and safety of systems, particularly AI systems like neural networks and robotic controllers, by quantifying the probability of satisfying or violating specified properties under uncertainty. Current research focuses on developing efficient algorithms, such as branch and bound, weighted model integration, and Markov Chain Monte Carlo methods, to handle various model architectures and properties (e.g., fairness, robustness, temporal logic specifications) while addressing scalability challenges. This field is crucial for ensuring the trustworthiness and safe deployment of increasingly complex AI-powered systems in diverse applications, from autonomous robots to critical infrastructure.