Runtime Monitoring for Markov Decision Processes
This addresses runtime safety monitoring for complex systems like autonomous vehicles or robotics, though it appears incremental as it builds on existing model checking approaches.
The paper tackles the problem of runtime monitoring for partially observable systems with nondeterministic and probabilistic dynamics, where states have associated risks like crash probabilities, and shows that while state estimation extensions fail to scale due to exponential memory issues, a tractable algorithm based on model checking conditional reachability probabilities is presented and validated on benchmarks.
We investigate the problem of monitoring partially observable systems with nondeterministic and probabilistic dynamics. In such systems, every state may be associated with a risk, e.g., the probability of an imminent crash. During runtime, we obtain partial information about the system state in form of observations. The monitor uses this information to estimate the risk of the (unobservable) current system state. Our results are threefold. First, we show that extensions of state estimation approaches do not scale due the combination of nondeterminism and probabilities. While convex hull algorithms improve the practical runtime, they do not prevent an exponential memory blowup. Second, we present a tractable algorithm based on model checking conditional reachability probabilities. Third, we provide prototypical implementations and manifest the applicability of our algorithms to a range of benchmarks. The results highlight the possibilities and boundaries of our novel algorithms.