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Name: Shiwen Zhou
Master’s Thesis Defense Meeting
Date: Friday, July 15, 2022
Time: 12:30 PM
Location: https://gatech.zoom.us/j/95135294606
Advisor:
Jamie Gorman, Ph.D. (Georgia Tech)
Thesis Committee Members:
Jamie Gorman, Ph.D. (Georgia Tech)
Richard Catrambone, Ph.D. (Georgia Tech)
James Roberts, Ph.D. (Georgia Tech)
Nancy Cooke, Ph.D. (Arizona State)
Title: Communication Pattern Analysis in Human-Autonomy Teaming
Abstract:
Communication is critical to team coordination and interaction because it provides information flows allowing a team to build team cognition, which contributes to overall team performance. In recent years, autonomous (AI) team members are beginning to be considered as effective substitutes for human teammates. However, research has shown that AI team members may lack the communication skills that are required for effective team performance (McNeese et al., 2018). To better understand which aspects of communication an AI team member performs differently compared to a human team member, and how they impact team performance, the current study analyzes communication features of three-person teams that include all human teams and human-AI teams operating in a remotely piloted aircraft system (RPAS). The current study analyzed communication pattern predictability (communication determinism) and transition probabilities to measure communication flow and Latent Semantic Analysis (LSA) to measure communication content. The current study found that both communication flow and content distinguished communication in all-human teams from communication in human-AI teams and found that these communication flow and content features predicted team performance in all-human versus human-AI teams. In this way, the current study hopes these communication differences can provide feedback and suggestions to future adoption of AI as a teammate in team training and team operations.
