Comparisons of Adaptive Automation Conditions for Single-Operator Multiple-Agent Control Systems

Abstract

Autonomous systems provide tangible benefits in the field of human-computer interaction (HCI) by reallocating work from human operators to suitable machine substitutes. However, improper implementations of autonomy in HCI systems have led to dire consequences. As such, the expansion of autonomy in research and industry must be matched by solutions that properly balance the interaction between human and machine. Early human-computer teams relied on multiple human operators working with one or just a few complex machines. With the growth of technology and improved autonomy, however, this trend has gradually reversed in that multiple complex machines are now supervised and operated by individual human controllers. Past research suggests that simply increasing autonomy fails to address the imbalance between human and machine within a cooperative mission scenario. Instead, adaptive automation has been demonstrated to be a viable solution in balancing the degree of autonomy between human and machine. Previous studies have verified the existence of relationships between levels of autonomy, human cognition, and system performance. In the context of single-operator multiple-agent scenarios, adaptive systems allow the levels of automation to dynamically adapt to the needs of both the human and the machine. This research explores various methods of invoking adaptive automation that aims to balance the level of automation between the human and machine within a simulated multitasking scenario. As such, a military-inspired simulation was designed and implemented to compare the effects of different adaptation mechanisms on objective task performance and operator cognitive workload. Comparisons of four adaptation conditions support the use of adaptive automation as opposed to random or adaptable automation mechanisms for maintaining overall mission performance requirements. Results indicate increased operator utilization and situation awareness over time with decreased subjective workload scores in the adaptive conditions compared to the adaptable and random automation conditions.