GResilience: Trading Off Between the Greenness and the Resilience of Collaborative AI Systems
This work addresses the trade-off between system resilience and energy consumption for CAIS developers and operators, but it is incremental as it builds on existing concepts of resilience and greenness in AI systems.
The paper tackles the problem of balancing resilience and energy efficiency (greenness) in Collaborative AI Systems (CAIS) by proposing an approach to automatically evaluate recovery actions, using optimization and game theory methods, with an experiment protocol applied to a real demonstrator.
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.