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

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

Discrete-time stochastic systems are an essential modeling tool for many engineering
systems. We consider stochastic control systems that are evolving over continuous spaces …

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 …

This paper introduces a model-based approach for training feedback controllers for an
autonomous agent operating in a highly nonlinear (albeit deterministic) environment. We …

We propose a compositional approach for constructing abstractions of general Markov
decision processes (gMDPs) using approximate probabilistic relations. The abstraction …

In this work, we propose an abstraction and refinement methodology for the controller
synthesis of discrete-time stochastic systems to enforce complex logical properties …

The synthesis of controllers guaranteeing linear temporal logic specifications on partially
observable Markov decision processes (POMDP) via their belief models causes …

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 …

This paper studies formal synthesis of control policies for continuous-state MDPs. In the
quest to satisfy complex combinations of probabilistic temporal logic specifications, we …