Evaluating the Benefits and Potential Costs of Automation Delegation for Supervisory Control of Multiple UAVs

Abstract

Previous studies have begun exploring the possibility that “adaptable” automation, in which tasks are delegated to intelligent automation by the user, can preserve the benefits of automation while minimizing its costs. One approach to adaptable automation is the Playbook®interface, which has been used in previous research and has shown performance enhancements as compared to other automation approaches. However, additional investigations are warranted to evaluate both benefits and potential costs of adaptable automation. The present study incorporated a delegation interface into a new display and simulation system, the multiple unmanned aerial vehicle simulator (MUSIM), to allow for flexible control over three unmanned aerial vehicles (UAVs) at three levels of delegation abstraction. Task load was manipulated by increasing the frequency of primary and secondary task events. Additionally, participants experienced an unanticipated event that was not a good fit for the higher levels of delegation abstraction. Treatment of this poor “automation fit” event, termed a “Non-Optimal Play Environment” event (NOPE event), required the use of manual control. Results showed advantages when access to the highest levels of delegation abstraction was provided and as long as operators also had the flexibility to revert to manual control. Performance was better across the two task load conditions and reaction time to respond to the NOPE event was fastest in this condition. The results extend previous findings showing benefits of flexible delegation of tasks to automation using the Playbook interface and suggest that Playbook remains robust even in the face of poor “automation-fit” events.

Team Structure and Team Building Improve Human–Machine Teaming With Autonomous Agents

“…Traditionally, human-automation interaction has been studied under a supervisory control framework, akin to a master-slave relationship (de Visser, Shaw, Mohamed-Ameen, & Parasuraman, 2010;Dzindolet, Pierce, Beck, Dawe, & Anderson, 2001;Muir, 1994;Muir & Moray, 1996;Satterfield, Ramirez, Shaw, & Parasuraman, 2012). However, research has shown that these supervisory control structures can potentially amplify cognitive demands on human operators as they struggle to integrate multiple information sources (Cosenzo, Parasuraman, & de Visser, 2010;Cummings & Mitchell, 2008;Shaw et al, 2010;Woods, Patterson, & Roth, 2002). Technological advances have allowed us to overcome the limitations associated with "automation-as-a-tool" control strategies and shift toward the use of autonomous, self-governing systems.…”

Human-Automation Challenges for the Control of Unmanned Aerial Systems

“…Playbook is an example of flexible delegation interactions with automation within the context of a task hierarchy-permitting human operators to select alternate levels and types of authority to hand off to automation and retain for themselves in different circumstances. Previously (Parasuraman, et al, 2005), and again in recent work (Fern & Shively, 2009;Shaw, et al, 2010), play-like, flexible delegation interactions with automation have been shown to have significant benefits in terms of overall performance and workload reduction across a variety of task contexts. In recent research, we have been exploring human performance with plays in multiple unmanned vehicle control scenarios under conditions where the plays do not "fit" well-that is, Non-Optimal Play Environment (NOPE) conditions.…”

Initiators and Triggering Conditions for Adaptive Automation in Advanced Small Modular Reactors

“…Several studies that have investigated adaptable automation based on task-delegation interfaces have found that this type of operator-initiated adaptive automation enhances performance [10][11][12]. Studies have also found that performance is highest when the delegation interface includes the highest level of abstraction (analogous to level of automation in this scheme), even when it is not appropriate for the current context.…”

“…While traditional approaches to operator-initiated adaptive automation typically define adaptability as shifting the level of automation (i.e., how much of the task is automated), several researchers have advocated for designing adaptive automation based on a hierarchical task delegation method [9][10][11][12]. In this adaptive automation scheme, the operator still has final decision authority over shifts in automation; however the functions are broken into predetermined tasks that the operator can choose to delegate to automation.…”

“…Studies have also found that performance is highest when the delegation interface includes the highest level of abstraction (analogous to level of automation in this scheme), even when it is not appropriate for the current context. In two separate studies, participants executed scenarios in which the operators had to revert to fully manual performance (the lowest level of abstraction in the task delegation interface), however their performance was still enhanced by having the higher level of abstraction available [11][12]. The researchers argued that this adaptive automation scheme allows operators to use high levels of automation (thus, reducing workload) without becoming complacent [11].…”