A lack of guidance for designing complex, dynamic networked systems presents challenges to the development of such systems to maximize overall human-machine system performance. An intelligent adaptive interface (IAI) concept and associated technologies have been developed to address this problem. In order to support effective decision making, a typical IAI is driven by software agents that can change the display and/or control characteristics to react to the changes of mission and operator states in real time. This work investigated the efficacy of IAIs in a multi-uninhabited aerial vehicle (UAV) scenario. The IAI was modeled as part of the UAV tactical workstations found in a maritime patrol aircraft. A performance model was developed to compare the difference in mission activities with and without IAI agents. A prototype IAI experimental environment was implemented for a human-in-the-loop empirical investigation. Both simulation and experiment results revealed that the control of multiple UAVs is a cognitively complex task with high workload. IAIs facilitated a significant reduction in workload and an improvement in situation awareness, thus allowing operators to continue working under high time pressure. This research revealed IAI triggering conditions under different cognitive workload situations.
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