This position paper reflects on the state-of-the-art in decision-making under uncertainty. A
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …

Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

Controllers for dynamical systems that operate in safety-critical settings must account for
stochastic disturbances. Such disturbances are often modeled as process noise in a …

Capturing uncertainty in models of complex dynamical systems is crucial to designing safe
controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of …

This paper studies formal synthesis of controllers for continuous-space systems with
unknown dynamics to satisfy requirements expressed as linear temporal logic formulas …

Reinforcement learning has shown promising results in learning neural network policies for
complicated control tasks. However, the lack of formal guarantees about the behavior of …

This letter is concerned with a data-driven technique for constructing finite Markov decision
processes (MDPs) as finite abstractions of discrete-time stochastic control systems with …

We introduce a novel approach for the construction of symbolic abstractions-simpler, finite-
state models-which mimic the behaviour of a system of interest, and are commonly utilized to …

Leveraging autonomous systems in safety-critical scenarios requires verifying their
behaviors in the presence of uncertainties and black-box components that influence the …

A common technique to verify complex logic specifications for dynamical systems is the
construction of symbolic abstractions: simpler, finite-state models whose behaviour mimics …