提出协作智能系统的韧性定义与量化方法，提升复杂环境下的可靠性。
Cooperative Resilience in Artificial Intelligence Multiagent Systems
- 定义并量化协作智能系统的韧性，基于强化学习与大模型代理的实验环境。
- 在环境变化和不可持续行为代理干扰下，系统展现准备、抵抗、恢复、维持与转型能力。
- 方法可推广至多智能体系统，适合关注AI鲁棒性的研究者使用。
韧性指系统抵御、适应并从扰动中恢复的能力。尽管该概念在多个领域备受关注，但在协作人工智能领域仍缺乏明确定义。本文提出‘协作韧性’的清晰定义，并构建其定量测量方法。该方法在基于强化学习与大模型增强的自主代理环境中验证，面对环境变化及不可持续行为代理的引入，通过参数化生成多种扰动场景进行评估。结果表明，韧性指标对分析系统在扰动前的准备、抵抗、恢复、维持整体福祉及实现系统性转变具有关键作用。研究为协作韧性提供了定义、测量与初步分析的基础，对人工智能领域具有深远影响。所提出的框架和度量可广泛应用于各类AI系统，提升其在动态不确定环境中的可靠性与有效性。
原文摘要 · Abstract (English)
Resilience refers to the ability of systems to withstand, adapt to, and recover from disruptive events. While studies on resilience have attracted significant attention across various research domains, the precise definition of this concept within the field of cooperative artificial intelligence remains unclear. This paper addresses this gap by proposing a clear definition of `cooperative resilience' and outlining a methodology for its quantitative measurement. The methodology is validated in an environment with RL-based and LLM-augmented autonomous agents, subjected to environmental changes and the introduction of agents with unsustainable behaviors. These events are parameterized to create various scenarios for measuring cooperative resilience. The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions. These findings provide foundational insights into the definition, measurement, and preliminary analysis of cooperative resilience, offering significant implications for the broader field of AI. Moreover, the methodology and metrics developed here can be adapted to a wide range of AI applications, enhancing the reliability and effectiveness of AI in dynamic and unpredictable environments.
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