Advertisement

Adaptation and Validation of an Agent Model of Functional State and Performance for Individuals

  • Fiemke Both
  • Mark Hoogendoorn
  • S. Waqar Jaffry
  • Rianne van Lambalgen
  • Rogier Oorburg
  • Alexei Sharpanskykh
  • Jan Treur
  • Michael de Vos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5925)

Abstract

Human performance can seriously degrade under demanding tasks. To improve performance, agents can reason about the current state of the human, and give the most appropriate and effective support. To enable this, the agent needs a model of a specific person’s functional state and performance, which should be valid, as the agent might otherwise give inappropriate advice and even worsen performance. This paper concerns the adaptation of the parameters of the existing functional state model to the individual and validation of the resulting model. First, human experiments have been conducted, whereby measurements related to the model have been performed. Next, this data has been used to obtain appropriate parameter settings for the model, describing the specific subject. Finally, the model, with the tailored parameter settings, has been used to predict human behavior to investigate predictive capabilities of the model. The results have been analyzed using formal verification.

Keywords

Agent model functional state validation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bosse, T., Both, F., van Lambalgen, R., Treur, J.: An Agent Model for a Human’s Functional State and Performance. In: Jain, L., et al. (eds.) Proceedings of International Conference, IAT 2008, pp. 302–307. IEEE Computer Society Press, Los Alamitos (2008)Google Scholar
  2. 2.
    Bosse, T., Jonker, C.M., Meij, L., van der Sharpanskykh, A., Treur, J.: Specification and Verification of Dynamics in Agent Models. International Journal of Cooperative Information Systems 18(1), 167–193 (2008)CrossRefGoogle Scholar
  3. 3.
    Costa Jr., P.T., McCrae, R.R.: Revised NEO Personality Inventory (NEO-PI-R) and the NEO Five-Factor Inventory (NEO-FFI) professional manual (Psychological Assessment Resources). Odessa, FL (1992)Google Scholar
  4. 4.
    Hancock, P.A., Williams, G., Manning, C.P., Miyake, S.: Influence of task demand characteristics on workload and performance. The International Journal of Aviation Psychology 5(1), 63–86 (1995)CrossRefGoogle Scholar
  5. 5.
    Hill, D.W.: The critical power concept. Sports Medicine 16, 237–254 (1993)CrossRefGoogle Scholar
  6. 6.
    Hockey, G.R.J.: Compensatory control in the regulation of human performance under stress and high workload: a cognitive-energetical framework. Biological Psychology 45, 73–93 (1997)CrossRefGoogle Scholar
  7. 7.
    Kozierok, R., Maes, P.: A Learning Interface Agent for Scheduling Meetings. In: Proceedings of the 1st International Conference on Intelligent User Interfaces, pp. 81–88 (1993)Google Scholar
  8. 8.
    Maheswaran, R., Tambe, M., Varakantham, P., Myers, K.: Adjustable autonomy challenges in personal assistant agents: A position paper. In: Proceedings of the AAMAS 2003 Workshop on Agents and Comp. Autonomy, pp. 187–194 (2003)Google Scholar
  9. 9.
    Matthews, G., Deary, I.J.: Personality traits. Cambridge University Press, Cambridge (1998)Google Scholar
  10. 10.
    Mitchell, T., Caruana, R., Freitag, D., McDermott, J., Zabowski, D.: Experience with a Learning Personal Assistant. Communication of the ACM 37(7), 81–91 (1994)CrossRefGoogle Scholar
  11. 11.
    Parkhurst, D., Law, K., Niebur, E.: Modeling the role of salience in the allocation of overt visual attention. Vision Research 42(1), 107–123 (2002)CrossRefGoogle Scholar
  12. 12.
    Plomin, R., Spinath, F.M.: Genetics and general cognitive ability. Trends in Cognitive Science 6(4), 369–176Google Scholar
  13. 13.
    Rose, C.L., Murphy, L.B., Byard, L., Nikzad, K.: The role of the Big Five personality factors in vigilance performance and workload. European Journal of Personality 16, 185–200 (2002)CrossRefGoogle Scholar
  14. 14.
    Salgado, J.F.: The five factor model of personality and job performance in the European community. Journal of Applied Psychology 82(1), 30–43 (1997)CrossRefGoogle Scholar
  15. 15.
    Sorenson, H.W.: Parameter estimation: principles and problems. Marcel Dekker, Inc., New York (1980)zbMATHGoogle Scholar
  16. 16.
    Yilmaz, L.: Validation and verification of social processes within agent-based computational organization models. Computational and Mathematical Organization Theory 12, 283–312 (2006)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fiemke Both
    • 1
  • Mark Hoogendoorn
    • 1
  • S. Waqar Jaffry
    • 1
  • Rianne van Lambalgen
    • 1
  • Rogier Oorburg
    • 2
  • Alexei Sharpanskykh
    • 1
  • Jan Treur
    • 1
  • Michael de Vos
    • 2
  1. 1.Department of Artificial IntelligenceVrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Force Vision LabAmsterdamThe Netherlands

Personalised recommendations