Advertisement

Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach

  • Michael A. Goodrich
  • Daqing Yi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8021)

Abstract

We consider a set of team-based information tasks, meaning that the team’s goals are to choose behaviors that provide or enhance information available to the team. These information tasks occur across a region of space and must be performed for a period of time. We present a Bayesian model for (a) how information flows in the world and (b) how information is altered in the world by the location and perceptions of both humans and robots. Building from this model, we specify the requirements for a robot’s computational mental model of the task and the human teammate, including the need to understand where and how the human processes information in the world. The robot can use this mental model to select its behaviors to support the team objective, subject to a set of mission constraints.

Keywords

Mental Model Search Task Search Region World Trade Center Shared Belief 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Parasuraman, R., Sheridan, T., Wickens, C.: A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 30(3), 286–297 (2000)CrossRefGoogle Scholar
  2. 2.
    Breazeal, C., Gray, J., Hoffman, G., Berlin, M.: Social robots: beyond tools to partners. In: 13th IEEE International Workshop on Robot and Human Interactive Communication, ROMAN 2004, pp. 551–556 (September 2004)Google Scholar
  3. 3.
    Hoffman, G., Breazeal, C.: Collaboration in human-robot teams. In: Proc. of the AIAA 1st Intelligent Systems Technical Conference, Chicago, IL, USA (2004)Google Scholar
  4. 4.
    Salas, E., Fiore, S., Letsky, M.: Theories of team cognition: Cross-disciplinary perspectives. Routledge Academic (2011)Google Scholar
  5. 5.
    Mathieu, J.E., Heffner, T.S., Goodwin, G.F., Salas, E., Cannon-Bowers, J.A.: The influence of shared mental models on team process and performance. Journal of Applied Psychology 85(2), 273 (2000)CrossRefGoogle Scholar
  6. 6.
    Fiore, S.: Personal communicationGoogle Scholar
  7. 7.
    Neerincx, M., de Greef, T., Smets, N., Sam, M.: Shared mental models of distributed human-robot teams for coordinated disaster responses. In: 2011 AAAI Fall Symposium Series (2011)Google Scholar
  8. 8.
    Roscheck, M., Goodrich, M.: Detection likelihood maps for wilderness search and rescue. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 327–332. IEEE (2012)Google Scholar
  9. 9.
    Casper, J., Murphy, R.: Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 33(3), 367–385 (2003)CrossRefGoogle Scholar
  10. 10.
    Doroodgar, B., Ficocelli, M., Mobedi, B., Nejat, G.: The search for survivors: Cooperative human-robot interaction in search and rescue environments using semi-autonomous robots. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 2858–2863 (May 2010)Google Scholar
  11. 11.
    Ruangpayoongsak, N., Roth, H., Chudoba, J.: Mobile robots for search and rescue. In: 2005 IEEE International Safety, Security and Rescue Robotics, Workshop, pp. 212–217. IEEE (2005)Google Scholar
  12. 12.
    Burion, S.: Human detection for robotic urban search and rescue. Diploma Work (2004)Google Scholar
  13. 13.
    Nourbakhsh, I.R., Sycara, K., Koes, M., Yong, M., Lewis, M., Burion, S.: Human-robot teaming for search and rescue. IEEE Pervasive Computing 4(1), 72–79 (2005)CrossRefGoogle Scholar
  14. 14.
    Burke, J., Murphy, R.: Human-robot interaction in usar technical search: two heads are better than one. In: 13th IEEE International Workshop on Robot and Human Interactive Communication, ROMAN 2004, pp. 307–312 (September 2004)Google Scholar
  15. 15.
    Thrun, S., Burgard, W., Fox, D., et al.: Probabilistic robotics, vol. 1. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  16. 16.
    Cover, T., Thomas, J.: Elements of information theory. Wiley Interscience (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael A. Goodrich
    • 1
  • Daqing Yi
    • 1
  1. 1.Brigham Young UniversityProvoUSA

Personalised recommendations