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.

一个协作人工智能系统（CAIS）与人类在共享环境中合作达成共同目标。为了从降低性能并确保其弹性的干扰事件中恢复过来，CAIS 可能需要由系统、人类或者协作一起执行一组操作。本文提出了一种自动评估 CAIS 恢复行动在系统的弹性和绿色性之间权衡能力的方法，并设计了实验协议及其应用于真实 CAIS 演示装置，旨在从优化和博弈论两个角度攻击该问题。