The Role of Simulation in Designing Human-Automation Systems

  • Christina F. RusnockEmail author
  • Jayson G. Boubin
  • Joseph J. Giametta
  • Tyler J. Goodman
  • Anthony J. Hillesheim
  • Sungbin Kim
  • David R. Meyer
  • Michael E. Watson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9744)


Human-machine teaming is becoming an ever present aspect of executing modern military missions. In this paper, we discuss an extensive line of research currently being conducted at the Air Force Institute of Technology focused specifically on using simulation in the design of automated systems in order to improve human-automation interactions. This research includes efforts to predict operator performance, mental workload, situation awareness, trust, and fatigue. This research explores using simulation to design interfaces, perform trade studies, create adaptive systems, and make task allocation decisions.


Human-machine teaming Human-performance modeling Simulation System design 


  1. 1.
    Crane, J., Hamilton, B.A., Brownlow, L., Hamilton, B.A.: Optimization of multi-satellite systems using integrated model based system engineering (MBSE) techniques (2015)Google Scholar
  2. 2.
    Do, Q., Cook, S., Lay, M.: An investigation of MBSE practices across the contractual boundary. Procedia Comput. Sci. 28(Cser), 692–701 (2014)CrossRefGoogle Scholar
  3. 3.
    Russell, M.: Using MBSE to enhance system design decision making. Procedia Comput. Sci. 8, 188–193 (2012)CrossRefGoogle Scholar
  4. 4.
    Allender, L.: Modeling human performance: impacting system design, performance, and cost. In: Proceedings Military, Government and Aerospace Simulation Symposium, 2000 Advanced Simulation Technologies Conference, vol. 32, no. 3, pp. 139–144 (2000)Google Scholar
  5. 5.
    Mitchell, D.K., Samms, C., Wojcik, T.M.: System-of-systems Modeling: The Evolution of an Approach for True Human System IntegrationGoogle Scholar
  6. 6.
    Mitchell, D.K., Samms, C.L., Henthorn, T., Wojciechowski, J.Q.: Trade Study: A Two- Versus Three-Soldier Crew for the Mounted Combat System (MCS) and Other Future Combat System Platforms (2003)Google Scholar
  7. 7.
    Colombi, J.M., Miller, M.E., Schneider, M., McGrogan, J., Long, D.S., Plaga, J.: Predictive mental workload modeling for semiautonomous system design: implications for systems of systems. Syst. Eng. 14(3), 305–326 (2011)CrossRefGoogle Scholar
  8. 8.
    Watson, M.E.: Improving System Design through the Integration of Human Systems and Systems Engineering Models. Air Force Institute of Technology (2016)Google Scholar
  9. 9.
    Parasuraman, R., Manzey, D.H.: Complacency and bias in human use of automation: an attentional integration. Hum. Factors 52, 381–410 (2010)CrossRefGoogle Scholar
  10. 10.
    Rouse, W.: Human-computer interaction in multitask situations. IEEE Trans. Syst. Man Cybern. 7(5), 384–392 (1977)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Goodman, T., Miller, M., Rusnock, C.: Incorporating automation: using modeling and simulation to enable task re-allocation. In: Winter Simulation Conference (2015)Google Scholar
  12. 12.
    Delligatti, L.: SysML Distilled: A Brief Guide to the Systems Modeling Language. Addison-Wesley, Boston (2013)Google Scholar
  13. 13.
    Improved Performance Research Integration (IMPRINT) Tool: Army Research Laboratory (2010)Google Scholar
  14. 14.
    Johnson, A.W., Oman, C.M., Sheridan, T.B., Duda, K.R.: Dynamic task allocation in operational systems: Issues, gaps, and recommendations. In: 2014 IEEE Aerospace Conference, pp. 1–15 (2014)Google Scholar
  15. 15.
    Rouse, W.B.: Adaptive aiding for human/computer control. Hum. Factors 30, 431–438 (1988)Google Scholar
  16. 16.
    Parasuraman, R., Bahri, T., Deaton, J.E., Morrison, J.G., Barnes, M.: Theory and Design of Adaptive Automation in Aviation Systems. Naval Air Warfare Center, Aircraft Division, Warminster (1992)Google Scholar
  17. 17.
    Giametta, J., Borghetti, B.J.: EEG-based secondary task detection in a multiple objective operational environment. In: Proceedings of the 14th International Conference on Machine Learning and Applications (ICMLA) (2015)Google Scholar
  18. 18.
    Smith, A.M., Borghetti, B.J., Rusnock, C.F.: Improving model cross-applicability for operator workload estimation. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 59(1), 681–685 (2015)CrossRefGoogle Scholar
  19. 19.
    Rusnock, C., Borghetti, B., McQuaid, I.: Objective-analytical measures of workload – the third pillar of workload triangulation? In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) AC 2015. LNCS, vol. 9183, pp. 124–135. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  20. 20.
    Giametta, J.: Cross-subject continuous analytic workload profiling using stochastic discrete event simulation. Air Force Institute of Technology (2015)Google Scholar
  21. 21.
    Rusnock, C.F., Geiger, C.D.: Using discrete-event simulation for cognitive workload modeling and system evaluation. In: Proceedings of the 2013 Industrial and Systems Engineering Research Conference (2013)Google Scholar
  22. 22.
    Goodman, T., Miller, M.E., Christina, F., Bindewald, J.: Timing within human-agent interaction and its effects on team performance and human behavior. In: Cogsima 2016 (2016)Google Scholar
  23. 23.
    Kim, S.: Unmanned Aerial Vehicle (UAV) Operators’ Workload Reduction: The Effect of 3D Audio on Operators’ Workload and Performance during Multi-Aircraft Control. Air Force Institute of Technology (2016)Google Scholar
  24. 24.
    Endsley, M.R.: Situation awareness global assessment technique (SAGAT). In: Aerospace and Electronics Conference 1988, NAECON 1988, Proceedings of IEEE 1988 National, pp. 789–795 (1988)Google Scholar
  25. 25.
    Endsley, M.R.: Situation awareness in aviation systems. In: Garland, D.J., Wise, J.A., Hopkin, V.D. (eds.) Handbook of Aviation Human Factors, pp. 257–276. Lawrence Erlbaum Associates, Mahwah (1999)Google Scholar
  26. 26.
    Meyer, D.R.: Effects of automation on aircrew workload and situation awareness in tactical airlift missions. Air Force Institute of Technology (2015)Google Scholar
  27. 27.
    Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors J. Hum. Factors Ergon. Soc. 37(1), 32–64 (1995)CrossRefGoogle Scholar
  28. 28.
    Endsley, M.R.: Situation awareness and workload: flip sides of the same coin. In: Proceedings of the 7th International Symposium on Aviation Psychology, pp. 906–911 (1993)Google Scholar
  29. 29.
    Ross, J.M., Szalma, J.L., Hancock, P.A., Barnett, J.S., Taylor, G.: The effect of automation reliability on user automation trust and reliance in a search-and-rescue scenario. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 52(19), 1340–1344 (2008)CrossRefGoogle Scholar
  30. 30.
    Lee, J.D., See, K.A.: Trust in automation: designing for appropriate reliance. Hum. Factors J. Hum. Factors Ergon. Soc. 46(1), 50–80 (2004)CrossRefGoogle Scholar
  31. 31.
    Boubin, J.G., Rusnock, C.F., Bindewald, J.M., Miller, M.E.: Measuring human compliance and reliance with automated systems. In: Proceedings of the 2016 Industrial and Systems Engineering Research Conference (2016)Google Scholar
  32. 32.
    Dixon, S.R., Wickens, C.D.: Automation reliability in unmanned aerial vehicle control: a reliance-compliance model of automation dependence in high workload. Hum. Factors 48(3), 474–486 (2006)CrossRefGoogle Scholar
  33. 33.
    Gunzelmann, G., Gluck, K.A.: An integrative approach to understanding and predicting the consequences of fatigue on cognitive performance. Cogn. Technol. 14(1), 14–25 (2009)Google Scholar
  34. 34.
    Giambra, L.M., Quilter, R.E.: A two-term exponential functional description of the time course of sustained attention. Hum. Factors 29(6), 635–643 (1987)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christina F. Rusnock
    • 1
    Email author
  • Jayson G. Boubin
    • 1
  • Joseph J. Giametta
    • 1
  • Tyler J. Goodman
    • 1
  • Anthony J. Hillesheim
    • 1
  • Sungbin Kim
    • 1
  • David R. Meyer
    • 1
  • Michael E. Watson
    • 1
  1. 1.Air Force Institute of Technology, Wright-Patterson AFBDaytonUSA

Personalised recommendations