Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming
Qi Gao, Wei Xu, Mowei Shen, Zaifeng Gao

TL;DR
This paper introduces the Agent Teaming Situation Awareness (ATSA) framework, which models dynamic, bidirectional SA interactions in human-AI teams to improve understanding and performance of mixed teams.
Contribution
The paper presents a novel SA framework for HAT that unifies human and AI behaviors, emphasizing bidirectional interaction and cognitive mechanisms.
Findings
ATSA unifies human and AI SA models.
Framework supports dynamic, bidirectional interactions.
Proposes future research directions for HAT SA.
Abstract
The rapid advancements in artificial intelligence (AI) have led to a growing trend of human-AI teaming (HAT) in various fields. As machines continue to evolve from mere automation to a state of autonomy, they are increasingly exhibiting unexpected behaviors and human-like cognitive/intelligent capabilities, including situation awareness (SA). This shift has the potential to enhance the performance of mixed human-AI teams over all-human teams, underscoring the need for a better understanding of the dynamic SA interactions between humans and machines. To this end, we provide a review of leading SA theoretical models and a new framework for SA in the HAT context based on the key features and processes of HAT. The Agent Teaming Situation Awareness (ATSA) framework unifies human and AI behavior, and involves bidirectional, and dynamic interaction. The framework is based on the individual and…
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Taxonomy
TopicsHuman-Automation Interaction and Safety · Team Dynamics and Performance · Personal Information Management and User Behavior
