  A Collaborative Artificial Intelligence System (CAIS) is a cyber-physical
system that learns actions in collaboration with humans in a shared environment
to achieve a common goal. In particular, a CAIS is equipped with an AI model to
support the decision-making process of this collaboration. When an event
degrades the performance of CAIS (i.e., a disruptive event), this
decision-making process may be hampered or even stopped. Thus, it is of
paramount importance to monitor the learning of the AI model, and eventually
support its decision-making process in such circumstances. This paper
introduces a new methodology to automatically support the decision-making
process in CAIS when the system experiences performance degradation after a
disruptive event. To this aim, we develop a framework that consists of three
components: one manages or simulates CAIS's environment and disruptive events,
the second automates the decision-making process, and the third provides a
visual analysis of CAIS behavior. Overall, our framework automatically monitors
the decision-making process, intervenes whenever a performance degradation
occurs, and recommends the next action. We demonstrate our framework by
implementing an example with a real-world collaborative robot, where the
framework recommends the next action that balances between minimizing the
recovery time (i.e., resilience), and minimizing the energy adverse effects
(i.e., greenness).
