Non-parametric Formal Synthesis of Unknown Stochastic Systems: Asymptotic Convergence Guarantees

Abstract

Data-driven techniques have shown promising potential for checking behavior of complex systems operating in safety-critical domains against safety and other temporal requirements. This paper studies a class of data-driven techniques that are based on learning a representation of the system from data using non-parametric estimation. The proposed approach is able to formally verify discrete-time stochastic dynamical systems against temporal logic specifications only using observation samples and without the knowledge of the model, and provides a probabilistic guarantee on the satisfaction of the specification. We first consider finite abstract representations of the system in the form of Markov decision processes (MDPs) and derive asymptotic convergence guarantees between the transition probabilities of the abstract MDP and their estimation using Bernstein's inequality and statistical properties of non-parametric estimators. We then propose theoretical results for estimating the asymptotic upper bound of the \emph{Lipschitz constant} (LC) of the stochastic system, which can determine the size of the finite abstract MDP for a given precision error. Under appropriate assumptions, our results prove that the asymptotic convergence rate of the estimations is $O(n^{-1/(3+d)})$ for both the transition probabilities and the LC, where $\mathsf d$ is the dimension of the system and $n$ is the data scale. By integrating these results, we can guarantee the asymptotic closeness in formal verification and policy synthesis performed on the original system and its finite abstraction based on the size of the dataset. Multiple case studies are presented to validate the effectiveness of the proposed method.