Assessing Quality Goal Rankings as a Method for Communicating Operator Intent

Abstract

Effective teammates coordinate their actions to achieve shared goals. In current human-Artificial Intelligent Agent (AIA) Teams, humans explicitly communicate task-oriented goals and how the goals are to be achieved to the AIAs as the AIAs do not support implicit communication. This research develops a construct for applying quality goals to improve coordination among human-AIA teams. This construct assumes that trained operators will exhibit similar priorities in similar situations and provides a shorthand communication mechanism to convey intentions. A study was designed and performed to assess situated operator priorities to provide insight into “how” operators desire a task to be performed. This assessment was performed episodically by trained and experienced Remotely Piloted Aircraft operators as they controlled an aircraft in a synthetic task environment through three challenging tactical scenarios. The results indicate that operator priorities change dynamically with situation changes. Further, the results are suitably cohesive across most trained operators to apply the data collected from the proposed method as training data to bootstrap development of an intent estimation agent. However, the data differed sufficiently among individual operators to justify the development of operator specific models, necessary for robust estimation of operator priorities to indicate “how” task-oriented goals should be pursued.