Nowadays, AI-based techniques, such as deep neural networks (DNNs), are widely
deployed in autonomous systems for complex mission requirements (eg, motion planning in …

We present SySCoRe, a MATLAB toolbox that synthesizes controllers for stochastic
continuous-state systems to satisfy temporal logic specifications. Starting from a system …

Automatically synthesizing controllers for continuous-state nonlinear stochastic systems,
while giving guarantees on the probability of satisfying (infinite-horizon) temporal logic …

In this paper, we present an approach for designing correct-by-design controllers for cyber-
physical systems composed of multiple dynamically interconnected uncertain systems. We …

This paper addresses the problem of data-driven computation of controllers that are correct
by design for safety-critical systems and can provably satisfy (complex) functional …

Traditional hierarchical control methodologies refine controllers from an abstract to a
concrete system using a simulation relation established over the state sets of both systems …

This paper introduces a novel abstraction-based framework for controller synthesis of
nonlinear discrete-time stochastic systems. The focus is on probabilistic reach-avoid …

Designing controllers to satisfy temporal requirements has proven to be challenging for
dynamical systems that are affected by uncertainty. This is mainly due to the states evolving …

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

In this ongoing work, we address data-driven computation of controllers that are correct by
design for safety-critical systems and can provably satisfy complex functional requirements …