Optimization of Task Allocation through Human Cognitive Simulation: Levels of Automation and Human Behaviors

  • Hiroshi Furukawa
  • Toshiyuki Inagaki
  • Yuji Niwa
Conference paper


Proper task allocation between humans and machines is one of the design requirements to optimize the advantages of automation, such as extraordinary precision, higher processing capabilities, and extension of the operator’s perceptual and cognitive capabilities [1]. A well-known technical basis for rational task allocation is Fitts’ list of what“men are better at” and“machines are better at” [2]. The criteria embodied in the list are based on a qualitative way of assessing the abilities of humans and machines. A strategy of task allocation using these criteria is that a particular task is allocated either to humans or machines according to their advantages in performing the task.


Task Allocation Task Completion Time State Comprehension Cognitive Workload Automation Level 
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  1. 1.
    Sheridan TB. Function allocation: algorithm, alchemy or apostasy? Int. J. Human-Computer Studies 2000; 52: 203–216CrossRefGoogle Scholar
  2. 2.
    Fitts PM. Human engineering for an effective air navigation and traffic control system. Ohio State University Foundation Report, Columbus, 1951Google Scholar
  3. 3.
    Sheridan TB, Verplank WL. Human and computer control of undersea teleoperators. Man-machine systems laboratory report, Cambridge, 1978Google Scholar
  4. 4.
    Furukawa H, Niwa Y, Inagaki T. Levels of automation in emergency operating procedures for a large-complex system. In: Proc. HCI International 2001. New Orleans, 2001, pp 1513-1517Google Scholar
  5. 5.
    Sharit J. Allocation of functions. In: Salvendy G. (ed) Handbook of Human Factors and Ergonomics. John Willy & Sons, New York, 1997, pp 301–339Google Scholar
  6. 6.
    Laughery KR. Modeling human performance during system design. In: Salas E (ed) Human/technology interaction in complex systems. JAI Press, Stamford, 1999, pp 147–174Google Scholar
  7. 7.
    Wickens CD, Yeh YY. A multiple resource model of workload prediction and assessment. In: Proc. the IEEE Conference on SMC, 1986, pp 1044-1048Google Scholar
  8. 8.
    User’s manual of WinCrew: Windows-based workload and task analysis tool, Micro Analysis and Design, 1997Google Scholar
  9. 9.
    Swain AD, Guttmann HE. Handbook of human reliability analysis with emphasis on nuclear power plant applications. NUREG/CR-1278, 1983Google Scholar
  10. 10.
    Eide, SA, Calley NB. Generic component failure database. In: Proceedings of PSA′93, 1993, pp 1175-1182Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Hiroshi Furukawa
    • 1
  • Toshiyuki Inagaki
    • 3
  • Yuji Niwa
    • 2
  1. 1.Institute of Information SciencesUniversity of TsukubaTsukubaJapan
  2. 2.Institute of Nuclear TechnologyInstitute of Nuclear Safety System, Inc.Mihama-choJapan
  3. 3.The Kansai electric Power Co.OsakaJapan

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