Probabilistic Temporal Logic

Probabilistic Temporal Logic (PTL) extends traditional temporal logic to reason about systems with inherent uncertainty, aiming to formally verify the satisfaction of temporal properties under probabilistic behavior. Current research focuses on efficient model-checking algorithms for various PTL variants, particularly within the context of multi-agent systems and Markov Decision Processes (MDPs), often employing techniques like abstraction and causal reasoning to handle complexity. This field is crucial for developing provably safe and reliable autonomous systems, particularly in safety-critical applications like robotics and reinforcement learning, by providing probabilistic guarantees on system behavior according to specified temporal logic properties. The development of robust and scalable verification methods remains a key challenge.

Papers