Cyber-physical systems (CPS) combine computational and physical components. Online Collaborative AI System (OL-CAIS) is a type of CPS that learn online in collaboration with humans to achieve a common goal, which makes it vulnerable to disruptive events that degrade performance. Decision-makers must therefore restore performance while limiting energy impact, creating a trade-off between resilience and greenness. This research addresses how to balance these two properties in OL-CAIS. It aims to model resilience for automatic state detection, develop agent-based policies that optimize the greenness-resilience trade-off, and understand catastrophic forgetting to maintain performance consistency. We model OL-CAIS behavior through three operational states: steady, disruptive, and final. To support recovery during disruptions, we introduce the GResilience framework, which provides recovery strategies through multi-objective optimization (one-agent), game-theoretic decision-making (two-agent), and reinforcement learning (RL-agent). We also design a measurement framework to quantify resilience and greenness. Empirical evaluation uses real and simulated experiments with a collaborative robot learning object classification from human demonstrations. Results show that the resilience model captures performance transitions during disruptions, and that GResilience policies improve green recovery by shortening recovery time, stabilizing performance, and reducing human dependency. RL-agent policies achieve the strongest results, although with a marginal increase in CO2 emissions. We also observe catastrophic forgetting after repeated disruptions, while our policies help maintain steadiness. A comparison with containerized execution shows that containerization cuts CO2 emissions by half. Overall, this research provides models, metrics, and policies that ensure the green recovery of OL-CAIS.

翻译：信息物理系统（CPS）融合了计算与物理组件。在线协同人工智能系统（OL-CAIS）是一类通过与人类在线协作学习以实现共同目标的CPS，这使其易受导致性能下降的破坏性事件影响。因此，决策者必须在恢复性能的同时限制能源影响，从而在韧性与绿色性之间形成权衡。本研究探讨如何在OL-CAIS中平衡这两种特性。其目标包括：为自动状态检测建立韧性模型，开发基于智能体的策略以优化绿色-韧性权衡，以及理解灾难性遗忘以维持性能一致性。我们通过三种运行状态（稳态、破坏态和终态）对OL-CAIS行为进行建模。为支持破坏期间的恢复，我们提出GResilience框架，该框架通过多目标优化（单智能体）、博弈论决策（双智能体）和强化学习（RL智能体）提供恢复策略。我们还设计了量化韧性与绿色性的测量框架。实证评估采用真实与模拟实验，通过协作机器人从人类演示中学习物体分类。结果表明：韧性模型能捕捉破坏期间的性能转变；GResilience策略通过缩短恢复时间、稳定性能及降低人类依赖性来改善绿色恢复；RL智能体策略取得最佳效果，尽管伴随轻微的二氧化碳排放增加。我们还观察到重复破坏后的灾难性遗忘现象，而所提策略有助于维持稳态。与容器化执行的对比表明，容器化技术可减少一半的二氧化碳排放。总体而言，本研究提供了保障OL-CAIS绿色恢复的模型、度量标准与策略体系。