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

In this paper, we provide a compositional approach for constructing finite abstractions (aka
finite Markov decision processes (MDPs)) of interconnected discrete-time stochastic …

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

In this paper, we propose a compositional framework for the construction of discrete-time
finite abstractions, also known as finite Markov decision processes, from continuous-time …

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

This paper provides a compositional scheme based on dissipativity approaches for
constructing finite abstractions of continuous-time continuous-space stochastic control …

We consider turn-based stochastic 2-player games on graphs with ω-regular winning
conditions. We provide a direct symbolic algorithm for solving such games when the winning …

This paper focuses on optimizing probabilities of events of interest defined over general
controlled discrete-time Markov processes. It is shown that the optimization over a wide …

This dissertation provides efficient (data-driven) techniques to design highly-reliable
stochastic hybrid systems with mathematical guarantees by bringing together …

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