Ants in the OCEAN: Modulating Agents with Personality for Planning with Humans

  • Sebastian Ahrndt
  • Armin Aria
  • Johannes Fähndrich
  • Sahin Albayrak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8953)


This work introduces a prototype that demonstrates the idea of using a psychological theory of personality types known as the Five-Factor Model (FFM) in planning for human-agent teamwork scenarios. FFM is integrated into the BDI model of agency leading to variations in the interpretation of inputs, the decision-making process and the generation of outputs. This is demonstrated in a multi-agent simulation. Furthermore, it is outlined how these variations can be used for the planning process in collaborative settings.


User/machine systems Human factors Software psychology 


  1. 1.
    Ahrndt, S.: Improving human-aware planning. In: Klusch, M., Thimm, M., Paprzycki, M. (eds.) MATES 2013. LNCS (LNAI), vol. 8076, pp. 400–403. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  2. 2.
    Ahrndt, S., Ebert, P., Fähndrich, J., Albayrak, S.: HPLAN: facilitating the implementation of joint human-agent activities. In: Demazeau, Y., Zambonelli, F., Corchado, J.M., Bajo, J. (eds.) PAAMS 2014. LNCS (LNAI), vol. 8473, pp. 1–12. Springer, Heidelberg (2014). CrossRefGoogle Scholar
  3. 3.
    Ahrndt, S., Fähndrich, J., Albayrak, S.: Human-aware planning: a survey related to joint human-agent activities. In: Bajo Perez, J., et al. (eds.) Trends in Practical Applications of Heterogeneous Multi-agent Systems. The PAAMS Collection. AISC, vol. 293, pp. 95–102. Springer, Heidelberg (2014). CrossRefGoogle Scholar
  4. 4.
    Allbeck, J., Badler, N.: Toward representing agent behavior 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). ACM Press, April 2002Google Scholar
  5. 5.
    Andre, E., Klesen, M., Gebhard, P., Allen, S., Rist, T.: Integrating models of personality and emotions into lifelike characters. In: Paiva, A.C.R. (ed.) IWAI 1999. LNCS (LNAI), vol. 1814, pp. 150–165. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  6. 6.
    Bradshaw, J.M., et al.: From tools to teammates: joint activity in human-agent-robot teams. In: Kurosu, M. (ed.) Human Centered Design, HCII 2009. LNCS, vol. 5619, pp. 935–944. Springer, Heidelberg (2009). CrossRefGoogle Scholar
  7. 7.
    Campos, A., Dignum, F., Dignum, V., Signoretti, A., Magaly, A., Fialho, S.: A process-oriented approach to model agent personality. In: Sierra, C., Castelfranchi, C., Decker, K.S., Sichman, J.S. (eds.) Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009), pp. 1141–1142. IFAAMAS, Budapest, Hungary, May 2009Google Scholar
  8. 8.
    Canuto, A.M.P., Campos, A.M.C., Santos, A.M., Moura, E.C.M., Santos, E.B., Soares, R.G., Dantas, K.A.A.: Simulating working environments through the use of personality-based agents. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds.) IBERAMIA 2006 and SBIA 2006. LNCS (LNAI), vol. 4140, pp. 108–117. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  9. 9.
    Castelfranchi, C., de Rosis, F., Falcone, R., Pizzutilo, S.: Personality traits and social attitudes in multi-agent cooperation. Appl. Artif. Intell. 12(7–8), 649–675 (1998). Special Issue on ‘Socially Intelligent Agents’CrossRefGoogle Scholar
  10. 10.
    Cirillo, M., Karlsson, L., Saffiotti, A.: Human-aware task planning: An application to mobile robots. ACM Trans. Intell. Syst. Technol. 1(2), 15:1–15:26 (2010). CrossRefGoogle Scholar
  11. 11.
    Dryer, C.: Getting personal with computers: how to design personalities for agents. Appl. Artif. Intell. 13(3), 273–295 (1999)CrossRefGoogle Scholar
  12. 12.
    Du, H., Huhns, M.N.: Determining the effect of personality types on human-agent interactions. In: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 2, pp. 239–244. IEEE, November 2013Google Scholar
  13. 13.
    Durupinar, F., Allbeck, J., Pelechano, N., Badler, N.: Creating crowd variation with the OCEAN personality model. In: Padgham, Parkes, Müller, Parsons (eds.) Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), pp. 1217–1220. IFAAMAS (2008)Google Scholar
  14. 14.
    Egges, A., Kshirsagar, S., Magnenat-Thalmann, N.: Generic personality and emotion simulation for conversational agents. Comput. Anim. Virtual Worlds 15, 1–13 (2004)CrossRefGoogle Scholar
  15. 15.
    Furnham, A.: The big five versus the big four: the relationship between the Myers-Briggs type indicator (MBTI) and NEO-PI five factor model of personality. Pers. Individ. Differ. 21(2), 303–307 (1996)CrossRefGoogle Scholar
  16. 16.
    John, O.P., Srivastava, S.: The big-five trait taxonomy: history, measurement, and theoretical perspectives. In: Pervin, L.A., John, O.P. (eds.) Handbook of Personality: Theory and Research, pp. 102–138. The Guilford Press, New York (1999) Google Scholar
  17. 17.
    Kirsch, A., Kruse, T., Sisbot, E.A., Alami, R., Lawitzky, M., Brscic, D., Hirche, S., Basili, P., Glasauer, S.: Plan-based control of joint human-robot activities. KI - Künstliche Intelligenz 24(3), 223–231 (2010)CrossRefGoogle Scholar
  18. 18.
    Klein, G., Woods, D.D., Bradshaw, J.M., Hoffmann, R.R., Feltovich, P.J.: Ten challenges for making automation a ‘team player’ in joint human-agent activity. Hum. Cent. Comput. 19(6), 91–95 (2004)Google Scholar
  19. 19.
    Kurtz, J.E., Parrish, C.L.: Semantic response consistency and protocol validity in structured personality assessment: the case of the NEO-PI-R. J. Pers. Assess. 76(2), 315–332 (2001)CrossRefGoogle Scholar
  20. 20.
    Lützenberger, M., Albayrak, S.: Current frontiers in reproducing human driver behavior. In: Proceedings of the 46th Summer Computer Simulation Conference 2014, pp. 514–521 (2014)Google Scholar
  21. 21.
    McCrea, R.R., Costa, P.: Reinterpreting the Myers-Briggs type indicators from the perspective of the five-factor model of personality. J. Pers. 57(1), 17–40 (1989)CrossRefGoogle Scholar
  22. 22.
    McCrea, R.R., John, O.P.: An introduction to the five-factor model and its applications. J. Pers. 60(2), 175–215 (1992)CrossRefGoogle Scholar
  23. 23.
    Myers, I.B., Byers, P.B.: Gifts Differing: Understanding Personality Type, 2nd edn. Nicholas Brealey Publishing, Boston (1995)Google Scholar
  24. 24.
    O’Connor, B.P.: A quantitative review of the comprehensiveness of the five-factor model in relation to popular personality inventories. Assessment 9, 188–203 (2002)CrossRefGoogle Scholar
  25. 25.
    Paunonen, S.V., Jackson, D.N.: What is beyond the big five? plenty!. J. Pers. 68(5), 821–835 (2000)CrossRefGoogle Scholar
  26. 26.
    Pittenger, D.J.: Cautionary comments regarding the Myers-Briggs type indicator. Consult. Psychol. J. Pract. Res. 57(3), 210–221 (2005)CrossRefGoogle Scholar
  27. 27.
    Prada, R., Paiva, A.: Human-agent interaction: challenges for bringing humans and agents together. In: Proceedings of the 3rd International Workshop on Human-Agent Interaction Design and Models (HAIDM 2014) at the 13th International Conference on Agent and Multi-Agent Systems (AAMAS 2014), pp. 1–10. IFAAMAS (2014)Google Scholar
  28. 28.
    Rao, A.S., Georgeff, M.P.: BDI agents: from theory to practice. In: Lesser, V., Gasser, L. (eds.) Proceedings of the First International Conference on Multiagent Systems (ICMAS 1995), pp. 312–319. AAAI, The MIT Press, April 1995Google Scholar
  29. 29.
    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, vol. 6532, pp. 28–43. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  30. 30.
    Salvit, J., Sklar, E.: Modulating agent behavior using human personality type. In: Proceedings of the Workshop on Human-Agent Interaction Design and Models (HAIDM) at Autonomous Agents and MultiAgent Systems (AAMAS), pp. 145–160 (2012)Google Scholar
  31. 31.
    de Silva, L., Padgham, L.: A comparison of BDI based real-time reasoning and HTN based planning. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 1167–1173. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  32. 32.
    Sisbot, E.A., Marin-Urias, L.F., Alami, R., Simeon, T.: A human aware mobile robot motion planner. IEEE Trans. Robot. 23(5), 874–883 (2007)CrossRefGoogle Scholar
  33. 33.
    Talman, S., Hadad, M., Gal, Y., Kraus, S.: Adapting to agents’ personalities in negotiation. In: Pechoucek, M., Steiner, D., Thompson, S. (eds.) Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 383–389. ACM, New York (2005)Google Scholar
  34. 34.
    Tapus, A., Matarić, M.J., Scassellati, B.: The grand challenges in socially assistive robotics. IEEE Robot. Autom. Mag. 14(1), 35–42 (2007)CrossRefGoogle Scholar
  35. 35.
    Terracciano, A., Costa Jr, P.T., McCrae, R.R.: Personality plasticity after age 30. Pers. Soc. Psychol. B 32(8), 999–1009 (2006)CrossRefGoogle Scholar
  36. 36.
    Wilks, L.: The stability of personality over time as a function of personality trait dominance. Griffith Univ. Undergrad. Stud. Psychol. J. 1, 1–9 (2009)Google Scholar
  37. 37.
    Wooldridge, M.: Reasoning About Rational Agents. Intelligent robotics and autonomous agents. The MIT Press, Cambridge (2000) zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sebastian Ahrndt
    • 1
  • Armin Aria
    • 1
  • Johannes Fähndrich
    • 1
  • Sahin Albayrak
    • 1
  1. 1.DAI-Laboratory of the Technische Universität BerlinFaculty of Electrical Engineering and Computer ScienceBerlinGermany

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