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

TL;DR

This paper presents CAIS-DMA, a framework that monitors and supports decision-making in collaborative AI systems during performance degradation caused by disruptive events, enhancing resilience and sustainability.

Contribution

It introduces a novel framework that automatically detects performance issues in CAIS and provides decision support through simulation, automation, and visual analysis tools.

Findings

Framework successfully detects performance degradation.

Automated decision support improves system resilience.

Balancing recovery time and energy efficiency demonstrated.

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).