Development of an Autonomous Manager for Dyadic Human-Machine Teams in an Applied Multitasking Surveillance Environment

  • Mary E. FrameEmail author
  • Alan S. Boydstun
  • Anna M. Maresca
  • Jennifer S. Lopez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)


Automation is crucial in increasingly many workplaces. Though automation is often associated with job replacement, humans and machines have divergent proficiencies. Thus, human-machine teaming is generally favored over replacement. Within applied surveillance environments, automation is leveraged for cognitively intensive tasks. To maintain optimal performance within a dyadic human-machine team, we developed an Autonomous Manager (AM) that dynamically redistributes tasks between human and machine. Participants performed four simultaneous image identification tasks while paired with a simulated autonomous partner. Our AM was responsible for monitoring team performance and redistributing tasks when performance fell sub-threshold. We manipulated the refresh rate of the images, affording us the opportunity to measure improvement under multiple conditions.


Task Delegation Systems Human-Machine Teaming Adaptive Automation 


  1. 1.
    Kaber, D.B., Endsley, M.R.: The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Theoretical Issues in Ergonomics Science. 5, 113–153 (2004)CrossRefGoogle Scholar
  2. 2.
    Miller, C.A., Parasuraman, R.: Designing for flexible interaction between humans and automation: Delegation interfaces for supervisory control. Human Factors. 49, 57–75 (2007)CrossRefGoogle Scholar
  3. 3.
    Parasuraman, R., Mouloua, M., Molloy, R., Hilburn, B.: Adaptive function allocation reduces performance cost of static automation. In: 7th International Symposium on Aviation Psychology, pp.37–42. (1993)Google Scholar
  4. 4.
    Parasuraman, R., Wickens, C.D.: Humans: Still vital after all these years of automation. Human Factors. 50, 511–520 (2008)CrossRefGoogle Scholar
  5. 5.
    Prinzel, L.J., Freeman, F.G., Scerbo, M.W., Mikulka, P.J., Pope, A.T.: Effects of a psychophysiological system for adaptive automation on performance, workload, and the event-related potential P300 component. Human Factors. 45, 601–614 (2003)CrossRefGoogle Scholar
  6. 6.
    Young, M.S., Stanton, N.A.: Automotive automation: Investigating the impact on drivers’ mental workload. International journal on cognitive ergonomics. 1, 325–336 (1997)Google Scholar

Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Mary E. Frame
    • 1
    Email author
  • Alan S. Boydstun
    • 1
  • Anna M. Maresca
    • 1
  • Jennifer S. Lopez
    • 2
  1. 1.Wright State Research InstituteWright State UniversityBeavercreekUSA
  2. 2.Air Force Research LaboratoryDaytonUSA

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