通过加权控制屏障函数实现多智能体系统安全验证，兼顾确定性与概率保障。
Hybrid Safety Verification of Multi-Agent Systems using $ψ$-Weighted CBFs and PAC Guarantees
- 引入ψ加权控制屏障函数，编码智能体间方向对齐约束。
- 结合蒙特卡洛仿真与PAC保证，提供可量化概率安全证书。
- 适用于存在随机扰动的多智能体协同场景，适合安全关键系统设计。
本文提出一种针对闭环多智能体系统在有界随机扰动下的混合安全验证框架。该方法通过新型ψ加权控制屏障函数，将智能体间的方向控制对齐关系编码进安全约束中。结合确定性可行性分析与基于蒙特卡洛滚动生成的实证验证，基于边缘感知的安全违规情况推导出类似PAC的保证，提供概率性安全证明。在不同有界随机扰动条件下进行的实验验证了所提方法的可行性。
原文摘要 · Abstract (English)
This study proposes a hybrid safety verification framework for closed-loop multi-agent systems under bounded stochastic disturbances. The proposed approach augments control barrier functions with a novel $ψ$-weighted formulation that encodes directional control alignment between agents into the safety constraints. Deterministic admissibility is combined with empirical validation via Monte Carlo rollouts, and a PAC-style guarantee is derived based on margin-aware safety violations to provide a probabilistic safety certificate. The results from the experiments conducted under different bounded stochastic disturbances validate the feasibility of the proposed approach.
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