Human Robot Team Development: An Operational and Technical Perspective

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 595)


Turning a robot into an effective team-player requires continuous adaptation during its lifecycle to human team-members, tasks, and the technological environment. This paper proposes a concept for human-robot team development over longer periods of time and discusses technological and operational implications. From an operational perspective, we discuss the types of adaptations to team behavior that are required in a military house search scenario. From a technological perspective, we explain how teamwork adaptations can be implemented using a teamwork module based on ontologies and policies. The approach is demonstrated in a virtual environment, in which humans and robots collaborate to find objects in a house search.


Human robot teaming Policies Defense 


  1. 1.
    Bradshaw, J.M., Hoffman, R.R., Woods, D.D., Johnson, M.: The Seven deadly myths of “autonomous systems”. IEEE Intell. Syst. 28(3), 54–61 (2013)CrossRefGoogle Scholar
  2. 2.
    Klein, G., Woods, D.D., Bradshaw, J.M., Hoffman, R.R., Feltovich, P.J.: Ten challenges for making automation a “team player” in joint human-agent activity. IEEE Intell. Syst. 19(6), 91–95 (2004)CrossRefGoogle Scholar
  3. 3.
    Van Diggelen, J., Bradshaw, J.M., Johnson, M., Uszok, A., Feltovich, P.J.: Implementing collective obligations in human-agent teams using KAoS policies. In: Padget, J., Artikis, A., Vasconcelos, W., Stathis, K., da Silva, V.T., Matson, E., Polleres, A. (eds.) Coordination, Organizations, Institutions and Norms in Agent Systems V, pp. 36–52. Springer, Berlin (2010) CrossRefGoogle Scholar
  4. 4.
    Neerincx, M.A., van Diggelen, J., van Breda, L.: Interaction design patterns for adaptive human-agent-robot teamwork in high-risk domains. In: Harris, D. (ed.) International Conference on Engineering Psychology and Cognitive Ergonomics, pp. 211–220. Springer, Cham (2016)Google Scholar
  5. 5.
  6. 6.
    Asif, K.I., Bethel, C.L., Carruth, D.W.: Iterative interface design for robot integration with tactical teams. In: Savage-Knepshield, P., Chen, J. (eds.) Advances in Human Factors in Robots and Unmanned Systems, pp. 3–16. Springer, Cham (2017)CrossRefGoogle Scholar
  7. 7.
    Kruijff, G.J.M., Janíček, M., Keshavdas, S., Larochelle, B., Zender, H., Smets, N.J., Mioch, T., Neerincx, M.A., et al.: Experience in system design for human-robot teaming in urban search and rescue. In: Yoshida, K., Tadokoro, S. (eds.) Field and Service Robotics, pp. 111–125. Springer, Berlin (2014) CrossRefGoogle Scholar
  8. 8.
    Murphy, R.: Introduction to AI Robotics. MIT Press, Cambridge (2000)Google Scholar
  9. 9.
    Carpenter, J.: Culture and Human-Robot Interaction in Militarized Spaces: A War Story. Routledge, Abingdon (2016)Google Scholar
  10. 10.
    Sauppé, A., Mutlu, B.: The social impact of a robot co-worker in industrial settings. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3613–3622. ACM (2015)Google Scholar
  11. 11.
    Strenzke, R., Uhrmann, J., Benzler, A., Maiwald, F., Rauschert, A., Schulte, A.: Managing cockpit crew excess task load in military manned-unmanned teaming missions by dual-mode cognitive automation approaches. In: AIAA Guidance, Navigation, and Control Conference, p. 6237 (2011)Google Scholar
  12. 12.
    Cohen, P.R., Levesque, H.J.: Intention is choice with commitment. Artif. Intell. 42(2–3), 213–261 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Finin, T., Fritzson, R., McKay, D., McEntire, R.: KQML as an agent communication language. In: Proceedings of the Third International Conference on Information and Knowledge Management, pp. 456–463. ACM (1994)Google Scholar
  14. 14.
    Tambe, M.: Towards flexible teamwork. J. Artif. Intell. Res. 7, 83–124 (1997)Google Scholar
  15. 15.
    Bradshaw, J. M., Feltovich, P. J., Johnson, M. J., Bunch, L., Breedy, M. R., Eskridge, T., Jung, H., Lott, J., et al.: Coordination in human-agent-robot teamwork. In: International Symposium on Collaborative Technologies and Systems, 2008. CTS 2008, pp. 467–476. IEEE (2008)Google Scholar
  16. 16.
    Bradshaw, J.M., Montanari, R., Uszok, A.: Policy-based governance of complex distributed systems: what past trends can teach us about future requirements. In: Adaptive, Dynamic, and Resilient Systems, pp. 259–284. Auerbach Publications (2014)Google Scholar
  17. 17.
    Uszok, A., Bradshaw, J.M., Johnson, M., Jeffers, R., Tate, A., Dalton, J., Aitken, S.: KAoS policy management for semantic web services. IEEE Intell. Syst. 19(4), 32–41 (2004)CrossRefGoogle Scholar
  18. 18.
    Guarino, N.: Formal ontology, conceptual analysis and knowledge representation. Int. J. Hum. Comput. Stud. 43(5–6), 625–640 (1995)CrossRefGoogle Scholar
  19. 19.
    Marzinotto, A., Colledanchise, M., Smith, C., Ögren, P.: Towards a unified behavior trees framework for robot control. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5420–5427. IEEE (2014)Google Scholar
  20. 20.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.TNOSoesterbergThe Netherlands

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