Safety Verification of Stochastic Systems: A Set-Erosion Approach
Zishun Liu, Saber Jafarpour, Yongxin Chen

TL;DR
This paper introduces a set-erosion approach for safety verification of discrete-time stochastic systems, leveraging probabilistic bounds to adapt deterministic safety methods for stochastic contexts.
Contribution
The paper presents a novel set-erosion strategy that combines probabilistic bounds with deterministic safety verification, enabling tighter safety certificates for stochastic systems.
Findings
The method provides tighter barrier certificates than existing approaches.
Numerical experiments demonstrate the effectiveness and superiority of the proposed approach.
The approach is flexible and compatible with various deterministic safety verification techniques.
Abstract
We study the safety verification problem for discrete-time stochastic systems. We propose an approach for safety verification termed set-erosion strategy that verifies the safety of a stochastic system on a safe set through the safety of its associated deterministic system on an eroded subset. The amount of erosion is captured by the probabilistic bound on the distance between stochastic trajectories and their associated deterministic counterpart. Building on our recent work [1], we establish a sharp probabilistic bound on this distance. Combining this bound with the set-erosion strategy, we establish a general framework for the safety verification of stochastic systems. Our method is flexible and can work effectively with any deterministic safety verification techniques. We exemplify our method by incorporating barrier functions designed for deterministic safety verification, obtaining…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRisk and Safety Analysis · Fault Detection and Control Systems · Software Reliability and Analysis Research
MethodsSparse Evolutionary Training
