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A Collaborative Artificial Intelligence System (CAIS) is a cyber-physicalsystem that learns actions in collaboration with humans in a shared environmentto achieve a common goal. In particular, a CAIS is equipped with an AI model tosupport the decision-making process of this collaboration. When an eventdegrades the performance of CAIS (i.e., a disruptive event), thisdecision-making process may be hampered or even stopped. Thus, it is ofparamount importance to monitor the learning of the AI model, and eventuallysupport its decision-making process in such circumstances. This paperintroduces a new methodology to automatically support the decision-makingprocess in CAIS when the system experiences performance degradation after adisruptive event. To this aim, we develop a framework that consists of threecomponents: one manages or simulates CAIS's environment and disruptive events,the second automates the decision-making process, and the third provides avisual analysis of CAIS behavior. Overall, our framework automatically monitorsthe decision-making process, intervenes whenever a performance degradationoccurs, and recommends the next action. We demonstrate our framework byimplementing an example with a real-world collaborative robot, where theframework recommends the next action that balances between minimizing therecovery time (i.e., resilience), and minimizing the energy adverse effects(i.e., greenness).

CAIS-DMA: A Decision-Making Assistant for Collaborative AI Systems