Tracking Operator Intent in Tactical Operations

Effective teams coordinate their actions to achieve shared goals. In Human-Agent teams, the Artificial Intelligent Agents (AIAs) struggle to coordinate effectively due to a lack of understanding of their human teammate's intent. This places a burden on the human teammate to extensively communicate explicitly what goals they are pursuing and how they are pursuing them. To improve the AIAs ability to coordinate, we have proposed Operationalized Intent as a means to explicitly model how an operator qualitatively desires a task to be performed. In this paper, we report the results of a study to track operator intent through a tactical scenario. The focus of this paper is on the dynamics of intent and it's cohesiveness across operators. The study employed an immersive, advanced research, remotely piloted aircraft (RPA) simulator to study intent in a synthetic task environment. Using operational pilots and sensor operators in realistic scenarios we were able to elicit their intent under naturalistic conditions in the midst of challenging tactical situations to study the real-time dynamics. Analysis indicates that the method models intent which is dynamically responsive to changes in the situation and the data are suitably cohesive across operators to generalize to an operator role. When the intent data is coupled to situated data from the simulator it provides a labeled data source for future AIAs to estimate intent in real-time.