A Collaborative Artificial Intelligence System (CAIS) works with humans in a shared environment to achieve a common goal. To recover from a disruptive event that degrades its performance and ensures its resilience, a CAIS may then need to perform a set of actions either by the system, by the humans, or collaboratively together. As for any other system, recovery actions may cause energy adverse effects due to the additional required energy. Therefore, it is of paramount importance to understand which of the above actions can better trade-off between resilience and greenness. In this in-progress work, we propose an approach to automatically evaluate CAIS recovery actions for their ability to trade-off between the resilience and greenness of the system. We have also designed an experiment protocol and its application to a real CAIS demonstrator. Our approach aims to attack the problem from two perspectives: as a one-agent decision problem through optimization, which takes the decision based on the score of resilience and greenness, and as a two-agent decision problem through game theory, which takes the decision based on the payoff computed for resilience and greenness as two players of a cooperative game.

翻译：协作人工智能系统（CAIS）在共享环境中与人类协作以实现共同目标。为从导致性能下降的干扰事件中恢复并确保其韧性，CAIS可能需要由系统、人类或双方协作执行一系列恢复动作。与其他系统相同，恢复动作可能因额外能耗而产生能源负面影响。因此，理解上述动作如何在韧性与绿色性之间实现更优权衡至关重要。在本项进行中的工作中，我们提出一种方法，用于自动评估CAIS恢复动作在系统韧性与绿色性之间的权衡能力。我们还设计了一套实验协议，并将其应用于真实CAIS演示平台。我们的方法从两个视角解决该问题：作为单智能体决策问题，基于韧性与绿色性评分通过优化进行决策；以及作为双智能体决策问题，通过博弈论基于韧性与绿色性作为合作博弈中两个参与者的收益计算结果进行决策。