Human-Agent Teaming: A System-Theoretic Overview
A systems-theoretic and interdisciplinary account of human-agent teaming, written to bridge the gap between the AI and human-machine interaction communities and to establish a common language between them.
The transformative impact of artificial intelligence (AI) over the last three decades has led to the emergence of human-agent teaming (HAT) as a rapidly growing field. HAT aims to improve the performance of human-agent systems by combining the strengths of humans and machines to tackle complex problems and achieve innovative outcomes. Understanding HAT is crucial for effective collaboration, communication, and decision-making between humans and AI and for addressing socio-technological concerns. This paper aims to provide a holistic, systems-theoretic, and interdisciplinary perspective of HAT that will bridge gaps between the AI and human-machine interaction communities, create a common language to enable effective collaboration and lead to new insights and innovations.
A preprint, not peer reviewed. Joint work with Matthew L. Bolton and Peter A. Beling.