  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.
