Intelligent Agent Transparency in Human–Agent Teaming for Multi-UxV Management

Abstract

Objective: We investigated the effects of level of agent transparency on operator performance, trust, and workload in a context of human–agent teaming for multirobot management. Background: Participants played the role of a heterogeneous unmanned vehicle (UxV) operator and were instructed to complete various missions by giving orders to UxVs through a computer interface. An intelligent agent (IA) assisted the participant by recommending two plans—a top recommendation and a secondary recommendation—for every mission. Method: A within-subjects design with three levels of agent transparency was employed in the present experiment. There were eight missions in each of three experimental blocks, grouped by level of transparency. During each experimental block, the IA was incorrect three out of eight times due to external information (e.g., commander’s intent and intelligence). Operator performance, trust, workload, and usability data were collected. Results: Results indicate that operator performance, trust, and perceived usability increased as a function of transparency level. Subjective and objective workload data indicate that participants’ workload did not increase as a function of transparency. Furthermore, response time did not increase as a function of transparency. Conclusion: Unlike previous research, which showed that increased transparency resulted in increased performance and trust calibration at the cost of greater workload and longer response time, our results support the benefits of transparency for performance effectiveness without additional costs. Application: The current results will facilitate the implementation of IAs in military settings and will provide useful data to the design of heterogeneous UxV teams.