Poster Abstract: Controller Synthesis for Nonlinear Stochastic Games via Approximate Probabilistic Relations

Abstract

Motivations. In the past two decades, synthesizing correct-by-construction controllers for continuous-space stochastic systems has received significant attentions in real-life safety-critical ap-plications, such as self-driving cars, power grids, etc. However, formal controller synthesis for complex stochastic systems with continuous state and input sets is very challenging since there is no closed-form solutions of these controllers in general. To cope with this difficulty, a promising approach is to approximate the original continuous-space systems by simpler ones with finite-state sets ( a.k.a., finite abstractions). A critical step during this approximation phase is to provide formal guarantees when refining the controller synthesized over (simpler) finite models back to original complex systems. Related Works and Contributions . An abstraction-based ap- proach for synthesizing controllers over stochastic systems with continuous state and input sets was initially proposed in [2]. Later on, this approach was improved and extended in terms of scalability,