Transdisciplinary Team Science: Transcending Disciplines to Understand Artificial Social Intelligence in Human-Agent Teaming-Reference-Cited by-同舟云学术

Transdisciplinary Team Science: Transcending Disciplines to Understand Artificial Social Intelligence in Human-Agent Teaming

Published:2023-09 Issue:1 Volume:67 Page:419-424
ISSN:2169-5067
Container-title:Proceedings of the Human Factors and Ergonomics Society Annual Meeting
language:en
Short-container-title:Proceedings of the Human Factors and Ergonomics Society Annual Meeting

Author:

Fiore Stephen M.¹^ORCID,Johnson Matthew²,Robertson Paul³,Diego-Rosell Pablo⁴,Fouse Adam⁵

Affiliation:

1. University of Central Florida, Orlando, FL, USA

2. Institute for Human and Machine Cognition, Pensacola, FL, USA

3. DOLL Inc., Lexington, MA, USA

4. Gallup, Inc., Washington, DC, USA

5. Aptima, Inc., Woburn, MA, USA

Abstract

We provide a transdisciplinary viewpoint on creating artificial social intelligence for human-agent teaming. We discuss theoretical, methodological, and technological insights, drawn from different disciplines, to more fully illuminate how cross-disciplinary research can inform research design and development. We unite ideas spanning human factors, cognitive and computer science, and organizational behavior. Grounding our ideas in real world challenges for human-AI teaming, and via a series of questions designed to facilitate synthesis across disciplines, we illustrate how transdisciplinary team science more effectively asks and answers complex questions on human-agent teaming. Our objective is to contribute to research and development in the field of human-AI and human-robot teaming by emphasizing a more human-centered perspective on AI.

Funder

Defense Advanced Research Projects Agency

Publisher

SAGE Publications

Subject

General Medicine,General Chemistry

Link

http://journals.sagepub.com/doi/pdf/10.1177/21695067231192245

Reference28 articles.

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5. Innovation in Team Interaction: New Methods for Assessing Collaboration Between Brains and Bodies Using a Multi-level Framework