Probabilistic System

Probabilistic systems research focuses on modeling and analyzing systems exhibiting inherent randomness or uncertainty, aiming to predict their behavior and synthesize robust controllers. Current research emphasizes efficient algorithms for verification and control synthesis, often employing Markov models (e.g., Markov Decision Processes, Continuous-Time Markov Chains) and leveraging data-driven approaches for increased robustness and generalization. These advancements are crucial for developing reliable systems in diverse fields, including robotics, software engineering, and epidemiology, where uncertainty is a significant factor. The development of tools and techniques for specifying and verifying properties of these systems, along with efficient parameter estimation methods, are key areas of ongoing investigation.