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

arXiv: https://arxiv.org/abs/2311.04562 · HTML: https://mlsanity.com/paper/2311.04562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 8 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning](https://mlsanity.com/paper/1801.04099/) (2018)
- [Human-centered collaborative robots with deep reinforcement learning](https://mlsanity.com/paper/2007.01009/) (2020)

## Abstract (arXiv)

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).