# Characterization of Safety in Stochastic Difference Inclusions using Barrier Functions

## TL;DR

This paper develops a method using barrier functions to analyze safety probabilities in stochastic systems modeled by difference inclusions, simplifying proofs under certain assumptions.

## Contribution

It introduces a novel approach that leverages set-valued map properties and expectation substitution to verify safety in stochastic difference inclusions.

## Key findings

- Barrier functions can be effectively used for safety verification.
- The approach simplifies proofs under convexity and semi-continuity assumptions.
- Numerical examples demonstrate the method's applicability.

## Abstract

We study stochastic systems characterized by difference inclusions. Such stochastic differential inclusions are defined by set-valued maps involving the current state and stochastic input. For such systems, we investigate the problem of proving bounds on the worst-case probability of violating safety properties. Our approach uses the well-known concept of barrier functions from the study of stochastic control systems. However, barrier functions are hard to prove in the presence of stochastic inputs and adversarial choices due to the set-valued nature of the dynamics. In this paper, we show that under some assumptions on the set-valued map including upper semi-continuity and convexity combined with a concave barrier function vastly simplifies the proof of barrier conditions, allowing us to effectively substitute each random input in terms of its expectation. We prove key results based on the theory of set-valued maps and provide some interesting numerical examples. The ideas proposed here will contribute to the growing interest in problems of robust control and verification of stochastic systems in the presence of uncertain distributions and unmodeled dynamics.