COMET: A coordinated multi-layer runtime monitoring framework for human-supervised AI-enabled safety systems· COMET：人监督的AI安全系统的多层运行监控框架
Incorporating AI systems into safety systems requires monitoring to avoid silent faults and ensure safe and reliable operation. Single monitors often do not provide actionable information for human supervisors, who act as the system's adaptive controller. We present COMET, a coordinated multi-layer runtime monitoring framework for human-supervised AI-enabled safety systems in which each monitor is anchored in a different fault mode. Monitor outputs are combined in a coordination layer and mapped by a communication layer to a traffic-light display. In this work, COMET consists of three monitoring layers: signal integrity, physics consistency, and the AI system's competence envelope. The framework is evaluated on an open dataset of compressed-air leaks, where an AI system detects leaks from
COMET框架通过多层监控提高AI安全系统的人类监督效率。
- 核心方法
- 设计了COMET多层运行监控框架，包含信号完整性、物理一致性、AI系统能力范围三个监控层，并通过协调层和通信层将监控结果综合为交通灯显示。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- AI系统集成到安全系统中时，单一监控机制无法为人类监督者提供有效信息，以避免隐性故障并确保系统的安全可靠运行。
- 关键实验
- 在关于压缩空气泄漏检测的开放数据集上评估了COMET框架的性能。
- 主要贡献
- 提出了一种多层运行时监控方法，增强了人类监督AI安全系统的能力。
- 意义与局限
- 该框架有助于提高AI安全系统的可监督性和可靠性，但可能受限于特定的应用场景和AI技术。