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.

翻译：韧性指系统承受、适应并从破坏性事件中恢复的能力。尽管韧性研究已在多个领域引起广泛关注，但这一概念在合作人工智能领域的确切定义仍不明确。本文通过提出“协同韧性”的明确定义并构建其量化测量方法，填补了这一空白。该方法在基于强化学习和大型语言模型增强的自主智能体环境中进行了验证，测试环境包含环境变化及引入具有不可持续行为智能体等干扰因素。这些事件通过参数化处理构建了多种测量协同韧性的场景。结果表明，韧性指标在分析集体系统如何为干扰做准备、抵抗干扰、从中恢复、维持良好状态以及实现转型方面具有关键作用。这些发现为协同韧性的定义、测量及初步分析提供了基础性见解，对更广泛的人工智能领域具有重要意义。此外，本文开发的方法与指标可适用于各类人工智能应用场景，从而提升AI在动态不可预测环境中的可靠性与有效性。