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 …

Statistical model checking avoids the state space explosion problem in verification and
naturally supports complex non-Markovian formalisms. Yet as a simulation-based approach …

Stochastic hybrid automata (SHA) are a powerful tool to evaluate the dependability and
safety of critical infrastructures. However, the resolution of nondeterminism, which is present …

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 …

MoGym, is an integrated toolbox enabling the training and verification of machine-learned
decision-making agents based on formal models, for the purpose of sound use in the real …

Stochastic Hybrid automata (SHA) are increasingly used to evaluate the dependability and
safety of critical infrastructures. Nondeterminism, which is present in many purely hybrid …

Motion planning for autonomous vehicles is commonly implemented via graph-search
methods, which pose limitations to the model accuracy and environmental complexity that …

Lightweight scheduler sampling brings statistical model checking to nondeterministic
formalisms with undiscounted properties, in constant memory. Its direct application to …

This report presents the results of a friendly competition for formal verification and policy
synthesis of stochastic models. It also introduces new benchmarks within this category, and …