Data-Driven Abstraction of Stochastic Dynamical Systems for Robust Control Synthesis

This paper presents a novel scheme to obtain data-driven abstractions of discrete-time stochastic processes as richer discrete stochastic models, capturing nondeterminism in the probability space through a collection of Markov Processes. This approach can improve upon existing abstraction techniques in terms of satisfying temporal properties, such as safety or reach-avoid.