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

The analysis of formal models that include quantitative aspects such as timing or
probabilistic choices is performed by quantitative verification tools. Broad and mature tool …

We apply reinforcement learning to approximate the optimal probability that a stochastic
hybrid system satisfies a temporal logic formula. We consider systems with (non) linear …

Robust autonomy and interaction of robots with their environment, even in rare or new
situations, is an ultimate goal of robotics research. We settle on Statistical Model Checking …

In delay-tolerant networks (DTNs) with uncertain contact plans, the communication episodes
and their reliabilities are known a priori. To maximize the end-to-end delivery probability, a …

Markov automata are a compositional modelling formalism with continuous stochastic time,
discrete probabilities, and nondeterministic choices. In this article, we present extensions to …

Statistical model checking estimates probabilities and expectations of interest in probabilistic
system models by using random simulations. Its results come with statistical guarantees …

We introduce a formal model of transportation in an open-pit mine for the purpose of
optimising the mine's operations. The model is a network of Markov automata (MA); the …

Probabilistic model checking (PMC) is a verification technique for analyzing the properties of
probabilistic systems. However, existing techniques face challenges in verifying large …

Optimal decision-making under stochastic uncertainty is a core problem tackled in artificial
intelligence/machine learning (AI), planning, and verification. Planning and AI methods aim …