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A Collaborative Artificial Intelligence System (CAIS) works with humans in ashared environment to achieve a common goal. To recover from a disruptive eventthat degrades its performance and ensures its resilience, a CAIS may then needto perform a set of actions either by the system, by the humans, orcollaboratively together. As for any other system, recovery actions may causeenergy adverse effects due to the additional required energy. Therefore, it isof paramount importance to understand which of the above actions can bettertrade-off between resilience and greenness. In this in-progress work, wepropose an approach to automatically evaluate CAIS recovery actions for theirability to trade-off between the resilience and greenness of the system. Wehave also designed an experiment protocol and its application to a real CAISdemonstrator. Our approach aims to attack the problem from two perspectives: asa one-agent decision problem through optimization, which takes the decisionbased on the score of resilience and greenness, and as a two-agent decisionproblem through game theory, which takes the decision based on the payoffcomputed for resilience and greenness as two players of a cooperative game.

GResilience: Trading Off Between the Greenness and the Resilience of Collaborative AI Systems