The Synthetic Teammate as a Team Player in Command-and-Control Teams. (September 2016) - Demir, Mustafa; McNeese, Nathan J.; Cooke, Nancy J.; Myers, Christopher
Project overview . The current project is part of a larger effort that focuses on Human-Automation Teaming (HAT) interaction in the context of the development, integration, and validation of a computational cognitive model that acts as a full-fledged synthetic teammate for a three-agent Unmanned Aircraft System (UAS) ground control crew. Our most recent effort looked at team process and team performance within the HAT. In order to be considered a team player, the synthetic teammate must be able to communicate and coordinate with its human teammates and do so in a subtle manner (Demir et al., 2016). In this task, there were three different and interdependent team members: 1) Air Vehicle Operator (AVO) – controls the UAS's heading, altitude, and airspeed; 2) Data Exploitation, Mission Planning, and Communications (DEMPC) – provides a dynamic flight plan as well as speed and altitude restrictions; and 3) Payload Operator (PLO) – monitors sensor equipment, negotiates with the AVO, and takes photographs of target waypoints. The communication within a three-agent UAS team occurred over a text-based communications system. In this research, there were three conditions which are differentiated by the AVO role: 1) the Synthetic - the synthetic teammate was assigned the AVO role; 2) the Control - the AVO was an inexperienced human participant; 3) the Experimenter - the AVO was one of the experimenters who was experienced with the task. The experimenter AVO asked questions of other team members to ensure timely and adaptive passing of information at target waypoints. In this current study, the coordination among the team members occurs at each target waypoint and requires a specific sequence of information passing for an optimum team performance (Cooke, Gorman, Duran, & Taylor, 2007): the information is provided by the DEMPC about the upcoming target waypoint to the AVO. After that, the PLO and the AVO negotiate regarding an appropriate altitude and airspeed for the target waypoints about required camera settings. Finally, the PLO sends feedback to other team members about the status of the target photo. Method . Activities during this period included conducting an experiment to: 1) evaluate the synthetic teammate's performance, and the HAT team performance in comparison to all human teams, 2) understand how team process differs between all human and human-synthetic teams and how this impacts performance, and 3) compare the human-synthetic teams and all human control teams to a team with a pilot that is experienced in pushing and pulling information across the team. For this experiment, participants were randomly assigned for the duration of the experiment. Within each of the five missions, teams were told to obtain as many "good" photos as possible while avoiding alarms and rule violations in less than 40 minutes. The overall focus of this paper is: team process that is comprised of eight verbal behaviors associated with team effectiveness; team performance that is a combination of mission variables, including the rate of successful target photographs, time spent in alarm and warning states (for each individual), and the critical waypoint acquisition rate; and target processing efficiency took into account the time spent inside a target waypoint to get a good photo. Results and discussion . In general, findings indicate that synthetic AVOs perform more poorly than control AVOs in terms of team performance. Synthetic teams perform as well at the mission level as control teams. However, in terms of target processing efficiency, synthetic teams perform poorer than control teams. In terms of team process, synthetic teams demonstrate interaction patterns corresponding to more pulling of information than pushing with little change over time. In summary, these results indicate that there is a strong potential for using synthetic team member as a teammate in real world tasks and for training.