Small teams—no matter the project they are working on—are constantly sharing information with each other about goals, obstacles and next steps. By talking through options, the group builds a shared understanding of the .
Small teams—no matter the project they are working on—are constantly sharing information with each other about goals, obstacles and next steps. By talking through options, the group builds a shared understanding of the work. It’s a complicated and decidedly human process that can get messy quickly if one member is not paying attention or simply misunderstands a key point and fails to clarify it while the group pushes forward.
Now, researchers at Colorado State University have developed a model that could enable an artificially intelligent agent to monitor and even potentially referee those interactions to encourage better collaboration. The work is part of an ongoing effort in the field to better integrate an understanding of nonverbal behavior, speech and direct action into human-AI and human-robot collaboration scenarios. The findings are detailed in a new paper recently published in the Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation.
The paper explores the concept of “common ground tracking”, which consists of identifying and monitoring the shared beliefs and open questions a group has when talking about a task. This is a difficult challenge for an AI system that requires careful tracking of both the verbal and nonverbal interactions by the group, which can interweave in subtle ways. It also requires developing an understanding of the team’s thought process over time and how best to intervene if it strays off task.
Assistant Professor Nikhil Krishnaswamy led the work at CSU through the Department of Computer Science.
