The design and control of autonomous systems that operate in uncertain or adversarial
environments can be facilitated by formal modeling and analysis. Probabilistic model …

We launch the new probabilistic model checker S torm. It features the analysis of discrete-
and continuous-time variants of both Markov chains and MDPs. It supports the P rism and …

We present the probabilistic model checker Storm. Storm supports the analysis of discrete-
and continuous-time variants of both Markov chains and Markov decision processes. Storm …

We present an extensive collection of quantitative models to facilitate the development,
comparison, and benchmarking of new verification algorithms and tools. All models have a …

Markov decision processes are widely used for planning and verification in settings that
combine controllable or adversarial choices with probabilistic behaviour. The standard …

Stochastic hybrid systems (SHS) are a rich mathematical modelling framework capable of
describing complex systems, where uncertainty and hybrid (that is, both continuous and …

Quantitative formal models capture probabilistic behaviour, real-time aspects, or general
continuous dynamics. A number of tools support their automatic analysis with respect to …

Neural networks (NN) are taking over ever more decisions thus far taken by humans, even
though verifiable system-level guarantees are far out of reach. Neither is the verification …

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …

Quantitative verification tools compute probabilities, expected rewards, or steady-state
values for formal models of stochastic and timed systems. Exact results often cannot be …