In 2018, MIT PhD stude Yuening Zhang faced a big challenge on a research trip to Hawaii; Maiaining Order and Coordination in Teams in Stressful Environmes During underwater terrain mapping, Zhang realized that a lack of precise alignme among team members could lead to confusion and failure to achieve common goals. This experience made him think of creating an artificial ielligence assista that could help improve the performance of teams.
Artificial ielligence comes to the rescue
Six years later, as a research assista at MIT’s Computer Science and Artificial Ielligence Laboratory (CSAIL), Zhang succeeded in developing a system to act as the missing piece in team collaboration, an AI assista that oversees human and robotic teams. Aligns roles and iervenes as needed to increase teamwork effectiveness in areas such as search and rescue missions, medical procedures, and video games.
The system, preseed in a paper at the Iernational Conference on Robotics and Automation (ICRA) and published in IEEE Xplore, uses a theory-of-mind model for artificial ielligence ages. This model shows how humans think and understand the possible action plans of teammates when collaborating on a task. By observing the actions of the ages, the AI team coordinator can infer their plans and their understanding of each other from a set of previous beliefs, and in case of inconsistency, iervene by aligning these beliefs and providing the necessary instructions.
For example, in a relief team, it is very importa to make quick and accurate decisions based on the roles and progress of colleagues. This type of complex scheduling is enhanced by CSAIL software, which can send messages about each age’s tasks and areas to be covered to avoid duplicate attempts. In these cases, the AI assista can ensure improved team performance by informing ages about the activities of colleagues.
“Our work is based on the idea that you believe what someone else believes,” explains Zhang, now a research scieist at Mobi Systems. In a team, you might ask yourself: What exactly does that person do? what will i do Does he know what I’m doing? “We tried to model this meal process and allow the AI assista to improve team members’ understanding of their tasks.”
Even with complex programs, both human and robotic ages can become confused. This situation is especially critical in search and rescue missions where there is limited time and a large area to search. The new AI assista can help teams move more efficiely through their tasks with consta notifications.
This type of coordination can also be effective in high-risk scenarios such as surgeries. In this situation, the medical team must perform their duties carefully to avoid mistakes. Artificial ielligence assista can preve confusions and improve team collaboration by monitoring the differe stages of surgery and providing necessary guidance.
Even in video games like Valora where players need to be closely coordinated, the AI assista can act as a guide and guide players on their tasks to optimize team performance.
Before Zhang led the developme of the model, the EPike system was developed, acting as a member of the team. This system was implemeed in a 3D simulation environme and helped coordinate team tasks by corolling a robotic age. Although these algorithms were very iellige, they still faced problems such as misunderstandings between ages in some cases. The new AI coordinator was able to avoid these problems by correcting ages’ beliefs and ensuring that tasks are performed correctly.
This project was carried out by the MIT team led by Professor Brian C. Williams of CSAIL was conducted in collaboration with leading researchers including Paul Robertson, Tianmin Shu, and Sangkeun Hong, and was supported in part by the US Defense Advanced Research Projects Agency (DARPA). They plan to improve this system using machine learning techniques and optimize it for use in real-life tasks.
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