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

• URL: http://arxiv.org/abs/2311.04562v1
• Date: Wed, 8 Nov 2023 09:49:46 GMT
• Title: CAIS-DMA: A Decision-Making Assistant for Collaborative AI Systems
• Authors: Diaeddin Rimawi, Antonio Lotta, Marco Todescato, Barbara Russo
• Abstract summary: Collaborative Artificial Intelligence System (CAIS) is a cyber-physical system that learns actions in collaboration with humans to achieve a common goal. When an event degrades the performance of CAIS (i.e., a disruptive event), this decision-making process may be hampered or even stopped. 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.
• Score: 1.4325175162807644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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).

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