Human‐Autonomy Collaboration and Coordination Toward Multi‐<scp>RPA</scp>Missions

doi:10.1002/9781118965900.ch5

Cited by 8 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In simulation studies, increases in the number of unmanned vehicles controlled impair performance (Cummings & Mitchell, 2006; De Visser & Parasuraman, 2007; Dixon et al, 2005; Lewis, 2013). Multi-UAS operations will impose diverse demands, including information retrieval, flight control, navigation, mission and payload management, and communication (Calhoun & Draper, 2015; Calhoun, Goodrich, Dougherty, & Adams, 2016; Goodrich & Cummings, 2015). The allocation of sufficient attentional resources may thus be taxed by multiple cognitive demands associated with perceptual, cognitive, and response load (Hart & Wickens, 2010).…”

Section: Resource Theory: Foundational For Human Performance?mentioning

confidence: 99%

Overload and automation-dependence in a multi-UAS simulation: Task demand and individual difference factors.

Lin

Matthews

Wohleber

et al. 2020

Journal of Experimental Psychology: Applied

View full text Add to dashboard Cite

Future unmanned aerial systems (UAS) operations will require control of multiple vehicles. Operators are vulnerable to cognitive overload, despite support from system automation. This study tested whether attentional resource theory predicts impacts of cognitive demands on performance measures, including automation-dependence and stress. It also investigated individual differences in response to demands. One-hundred and 1 university student participants performed a multi-UAS simulation mission incorporating 2 surveillance tasks. Cognitive demands and level of automation (LOA) of key tasks were manipulated between-subjects. Results were partially consistent with predictions. Higher task demands impaired performance and elevated distress and workload, as expected. Higher LOA produced greater dependence on automation, but failed to mitigate workload. It was expected that, as the automation was quite reliable, participants would attempt to conserve resources by depending more on automation under high demand. In fact, the opposite tendency was observed. Individuals high in conscientiousness were especially likely to override the automation under high demand, apparently taking charge personally. Neuroticism and distress were also associated with performance, but results did not fit a resource theory interpretation. Thus, understanding impacts of overload in the multi-UAS context requires understanding operator strategy as well as resource insufficiency. Findings have implications for system design, and operator selection and training.

show abstract

Section: Resource Theory: Foundational For Human Performance?mentioning

confidence: 99%

Overload and automation-dependence in a multi-UAS simulation: Task demand and individual difference factors.

Lin

Matthews

Wohleber

et al. 2020

Journal of Experimental Psychology: Applied

View full text Add to dashboard Cite

show abstract

“…The separation of "what" from "how" can be illustrated by examining implementations of the play calling concept (Calhoun et al, 2016(Calhoun et al, , 2018Miller, 2017). In one version of this concept described by Zacharias (Zacharias, 2019), operators select a play and a location.…”

Section: Introductionmentioning

confidence: 99%

Assessing Quality Goal Rankings as a Method for Communicating Operator Intent

Schneider

Miller

McGuirl

2022

Journal of Cognitive Engineering and Decision Making

View full text Add to dashboard Cite

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.

show abstract