Toward a Myers-Briggs Type Indicator Model of Agent Behavior in Multiagent Teams

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6532)


This paper explores the use of the Myers-Briggs Type Indicator (MBTI) as the basis for defining the personality of an agent. The MBTI is a well-known psychological theory of human personality. In the MBTI model, four axes are defined to explain how humans perceive their environment, how they interact with others and how they make decisions based on these traits. The work described here presents a preliminary model of agent behavior in which two of the axes are implemented, combining to reflect four distinct agent personality types. Experiments were conducted under three environmental conditions: single agent setting, homogeneous multiagent team, and heterogeneous multiagent team. Results are presented for each condition and are analyzed in comparison with the other conditions, as well as within the context of the expected MBTI behaviors given each environment and the simulated task. It is demonstrated that agents of each personality type produce very different results, distinct for and characteristic of each MBTI personality type.


Personality Trait Multiagent System Autonomous Agent Agent Behavior Personality Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Myers, I.B., Myers, P.B.: Gifts Differing. Consulting Psychologists Press (1980)Google Scholar
  2. 2.
    Jung, C.: Psychological types. In: The collected works of C. G. Jung, vol. 6. Princeton University Press, Princeton (1971) (originally 1921)Google Scholar
  3. 3.
    Bratman, M.E., Israel, D.J., Pollack, M.E.: Plans and resource-bounded practical reasoning. Computational Intelligence 4(4), 349–355 (1988)CrossRefGoogle Scholar
  4. 4.
    Kinny, D., Georgeff, M.: Modelling and design of multi-agent systems. In: Jennings, N.R., Wooldridge, M.J., Müller, J.P. (eds.) ECAI-WS 1996 and ATAL 1996. LNCS, vol. 1193, pp. 1–20. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  5. 5.
    Resnick, M.: Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds. MIT Press, Cambridge (1994)Google Scholar
  6. 6.
    Nilsson, N.J.: Technical note no. 323. Technical report, SRI International, Menlo Park, CA (1984); This is a collection of papers and technical notes, some previously unpublished, from the late 1960s and early 1970sGoogle Scholar
  7. 7.
    Brooks, R.A.: New approaches to robotics. Science 253(5025), 1227–1232 (1991)CrossRefGoogle Scholar
  8. 8.
    Wilensky, U.: NetLogo (1999),
  9. 9.
    Salvit, J., Sklar, E.: Toward a Myers-Briggs Type Indicator Model of Agent Behavior in Multiagent Teams. In: Bosse, T., Geller, A., Jonker, C.M. (eds.) MABS 2010. LNCS (LNAI), vol. 6532, pp. 28–43. Springer, Heidelberg (2011)Google Scholar
  10. 10.
    Dryer, D.C.: Getting personal with computers: how to design personalities for agents. Applied Artificial Intelligence 13, 273–295 (1999)CrossRefGoogle Scholar
  11. 11.
    Lin, C.-H., McLeod, D.: Temperament-based information filtering: A human factors approach to information recommendation. In: Proceedings of the IEEE International Conference on Multimedia & Exposition, New York (2000)Google Scholar
  12. 12.
    Allbeck, J., Badler, N.: Toward representing agent behaviors modified by personality and emotion. In: Proceedings of the Workshop on Embodied Conversational Agents at the 1st International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Bologna (2002)Google Scholar
  13. 13.
    Lisetti, C.L.: Personality, Affect and Emotion Taxonomy for Socially Intelligent Agents. In: Proceedings of the 15th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2002). AAAI Press, Menlo Park (2002)Google Scholar
  14. 14.
    Castelfranchi, C., de Rosis, F., Falcone, R., Pizzutilo, S.: A Testbed for investigating personality-based multiagent cooperation. In: Proceedings of the Symposium on Logical Approaches to Agent Modeling and Design (1997)Google Scholar
  15. 15.
    Talman, S., Gal, Y., Hadad, M., Kraus, S.: Adapting to Agents’ Personalities in Negotiation. In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS). ACM, New York (2005)Google Scholar
  16. 16.
    Durupinar, F., Allbeck, J., Pelechano, N., Badler, N.: Creating Crowd Variation with OCEAN Personality Model. In: Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1217–1220 (2008)Google Scholar
  17. 17.
    Parunak, H.V.D., Bisson, R., Brueckner, S., Matthews, R., Sauter, J.: A Model of Emotions for Situated Agents. In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS). ACM, New York (2006)Google Scholar
  18. 18.
    Campos, A., Dignum, F., Dignum, V., Signoretti, A., Mag’aly, A., Fialho, S.: A process-oriented approach to model agent personality. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Budapest, pp. 1141–1142 (2009)Google Scholar
  19. 19.
    Kitano, H., Tadokoro, S.: RoboCup Rescue: A Grand Challenge for Multiagent and Intelligent Systems. AI Magazine 22(1) (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Brooklyn CollegeCity University of New YorkBrooklynUSA
  2. 2.The Graduate CenterCity University of New YorkNew YorkUSA

Personalised recommendations