Situation awareness-based agent transparency for human-autonomy teaming effectiveness

Jessie Y. C. Chen; Michael J. Barnes; Julia L. Wright; Kimberly Stowers; Shan G. Lakhmani

doi:10.1117/12.2263194

18 May 2017 Situation awareness-based agent transparency for human-autonomy teaming effectiveness

Jessie Y. C. Chen, Michael J. Barnes, Julia L. Wright, Kimberly Stowers, Shan G. Lakhmani

Proceedings Volume 10194, Micro- and Nanotechnology Sensors, Systems, and Applications IX; 101941V (2017) https://doi.org/10.1117/12.2263194
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Conference Presentation

Citation Download Citation

Jessie Y. C. Chen, Michael J. Barnes, Julia L. Wright, Kimberly Stowers, and Shan G. Lakhmani "Situation awareness-based agent transparency for human-autonomy teaming effectiveness", Proc. SPIE 10194, Micro- and Nanotechnology Sensors, Systems, and Applications IX, 101941V (18 May 2017); https://doi.org/10.1117/12.2263194

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