Session Overview: Adaptation Strategies and Adaptation Management

  • Sven FuchsEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10915)


Researchers in the field of Augmented Cognition (AugCog) have often focused on detecting and classifying cognitive problem states (e.g. via physiological sensors). Yet, knowing what cognitive state to address through adaptation is merely a first step to building an adaptive system. The next steps – to determine what to adapt and how to do it – are just as interesting and challenging. There is great untapped potential in the adaptation component, yet it has been underrepresented and underappreciated in the AugCog community discourse. The goal of this contribution and the associated conference session is to get our community thinking about how to put their cognitive state diagnoses to use in an innovative manner, how to develop innovative adaptation strategies, and how to address adaptation management issues. This session overview lists a number of challenges faced by AugCog researchers with respect to the development of adaptation strategies and adaptation management frameworks. The papers featured in this session encompass a diverse set of approaches and ideas to address these challenges.


Augmented cognition Adaptive systems Adaptive training Human-systems integration Adaptive human-computer interaction Cooperative systems Adaptation strategies Adaptation management Adaptation frameworks 


  1. 1.
    Wickens, C.D.: Attentional Tunneling and Task Management. Technical report AHFD-05-01/ NASA-05-10. NASA Ames Research Center, Moffett Field, CA (2005)Google Scholar
  2. 2.
    Parasuraman, R., Molloy, R., Singh, I.L.: Performance consequences of automation-induced complacency. Int. J. Aviat. Psychol. 3(1), 1–23 (1993)CrossRefGoogle Scholar
  3. 3.
    Kessel, C.J., Wickens, C.D.: The transfer of failure-detection skills between monitoring and controlling dynamic systems. Hum. Factors 24, 49–60 (1982)CrossRefGoogle Scholar
  4. 4.
    Endsley, M.R., Kiris, E.O.: The out-of-the-loop performance problem and level of control in automation. Hum. Factors 37(2), 381–394 (1995)CrossRefGoogle Scholar
  5. 5.
    Dorneich, M., Whitlow, S., Ververs, P.M., Mathan, S., Raj, A., Muth, E., Hoover, A., DuRousseau, D., Parra, L., Sajda, P.: DARPA Improving Warfighter Information Intake under Stress - Augmented Cognition Concept Validation Experiment (CVE) Analysis Report for the Honeywell Team. DARPA/IPTO Technical report (2004)Google Scholar
  6. 6.
    Hale, K.S., Fuchs, S., Berka, C., Levendowski, D., Axelsson, P., Baskin, A., Juhnke, J.: Information Delivery and Display for Shared Awareness in the Net-Centric Battlespace. SBIR Phase I Final Technical report under Contract W31P4Q-06-C-0041. Design Interactive, Inc., Oviedo, FL (2006)Google Scholar
  7. 7.
    Fuchs, S., Hale, K.S., Stanney, K.M., Juhnke, J., Schmorrow, D.D.: Enhancing mitigation in augmented cognition. J. Cogn. Eng. Decis. Making 3, 309–326 (2007)CrossRefGoogle Scholar
  8. 8.
    Breton, R., Bossé, É.: The cognitive costs and benefits of automation. In: The Role of Humans in Intelligent and Automated Systems. Proceedings of the RTO Human Factors and Medicine Panel (HFM) Symposium (RTO-MP-088). NATO RTO, Neuilly-sur-Seine, France (2003)Google Scholar
  9. 9.
    Endsley, M.R.: Automation and situation awareness. In: Parasuraman, R., Mouloua, M. (eds.) Automation and Human Performance: Theory and Applications, pp. 163–181. Lawrence Erlbaum, Mahwah (1996)Google Scholar
  10. 10.
    Stanney, K., Reeves, L.: Mitigation strategies and performance effects. White paper outbrief from a working session at Improving Warfighter Information Intake Under Stress, AugCog PI Meeting, Chantilly, VA (2005)Google Scholar
  11. 11.
    Schwarz, J., Fuchs, S.: Multidimensional real-time assessment of user state and performance to trigger dynamic system adaptation. In: Schmorrow, Dylan D., Fidopiastis, Cali M. (eds.) AC 2017. LNCS (LNAI), vol. 10284, pp. 383–398. Springer, Cham (2017). Scholar
  12. 12.
    Fuchs, S., Schwarz, J.: Towards a dynamic selection and configuration of adaptation strategies in augmented cognition. In: Schmorrow, Dylan D., Fidopiastis, Cali M. (eds.) AC 2017. LNCS (LNAI), vol. 10285, pp. 101–115. Springer, Cham (2017). Scholar
  13. 13.
    Baltzer, C.A., Lassen, C., López, D., Flemisch, F.: Behaviour adaptation using interaction patterns with augmented reality elements. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCI 2018. LNCS (LNAI), vol. 10915, pp. 9–21. Springer, Cham (2018)Google Scholar
  14. 14.
    Stephens, C., Dehais, F., Roy, R., Harrivel, A., Last, M. C., Kennedy, K., Pope, A.: Biocybernetic adaptation strategies: machine awareness of human state for improved operational performance. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCI 2018. LNCS (LNAI), vol. 10915, pp. 89–98. Springer, Cham (2018)Google Scholar
  15. 15.
    Fortin-Côte, A., Lafond, D., Kopf, M., Gagnon, J.-F., Tremlay, S.: Adaptive training based on biobehavioral monitoring. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCI 2018. LNCS (LNAI), vol. 10915, pp. 34–45. Springer, Cham (2018)Google Scholar
  16. 16.
    Tollar, J.T.: Statistical process control as a triggering mechanism for augmented cognition mitigations. In: Schmorrow, D.D. (ed.) Foundations of Augmented Cognition, pp. 414–420. Lawrence Erlbaum Associates, Mahwah (2005)Google Scholar
  17. 17.
    Fuchs, S., Hale, K.S., Stanney, K.M., Berka, C., Levendowski, D., Juhnke, J.: Physiological sensors cannot effectively drive system mitigation alone. In: Schmorrow, D.D., Stanney, K.M., Reeves, L.M. (eds.) Foundations of Augmented Cognition, 2nd edn, pp. 193–200. Strategic Analysis Inc., Arlington (2006)Google Scholar
  18. 18.
    Wiener, N.: The Human Use of Human Beings. Houghton Mifflin, Boston (1950)Google Scholar
  19. 19.
    Veltman, J.A., Jansen, C.: The adaptive operator. In: Vincenzi, D.A., Mouloua, M., Hancock, P. (eds.) Human Performance, Situation Awareness, and Automation: Current Research and Trends, vol. 2, pp. 7–10. Lawrence Erlbaum Associates, Mahwah (2004)Google Scholar
  20. 20.
    Hancock, P.A., Chignell, M.H.: Input information requirements for an adaptive human-machine system. In: Proceedings of the Tenth Department of Defense Conference on Psychology, pp. 493–498. Defense Technical Information Center, Colorado Springs (1986)Google Scholar
  21. 21.
    Sottilare, R. A.: Community Models to Enhance Adaptive Instruction. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCI 2018. LNCS (LNAI), vol. 10915, pp. 78–88. Springer, Cham (2018)Google Scholar
  22. 22.
    Schwarz, J., Fuchs, S., Flemisch, F.: Towards a more holistic view on user state assessment in adaptive human-computer interaction. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 1247–1253. IEEE, San Diego (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIEWachtbergGermany

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