Situation Awareness-Based Agent Transparency For Human-Autonomy Teaming Effectiveness
Keywords
autonomy; human-machine teaming; human-robot interaction; situation awareness; transparency
Abstract
We developed the Situation awareness-based Agent Transparency (SAT) model to support human operators' situation awareness of the mission environment through teaming with intelligent agents. The model includes the agent's current actions and plans (Level 1), its reasoning process (Level 2), and its projection of future outcomes (Level 3). Human-inthe-loop simulation experiments have been conducted (Autonomous Squad Member and IMPACT) to illustrate the utility of the model for human-autonomy team interface designs. Across studies, the results consistently showed that human operators' task performance improved as the agents became more transparent. They also perceived transparent agents as more trustworthy.
Publication Date
1-1-2017
Publication Title
Proceedings of SPIE - The International Society for Optical Engineering
Volume
10194
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1117/12.2263194
Copyright Status
Unknown
Socpus ID
85024403809 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85024403809
STARS Citation
Chen, Jessie Y.C.; Barnes, Michael J.; Wright, Julia L.; Stowers, Kimberly; and Lakhmani, Shan G., "Situation Awareness-Based Agent Transparency For Human-Autonomy Teaming Effectiveness" (2017). Scopus Export 2015-2019. 6810.
https://stars.library.ucf.edu/scopus2015/6810