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.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
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)
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)
Hoffman, G., Breazeal, C.: Collaboration in human-robot teams. In: Proc. of the AIAA 1st Intelligent Systems Technical Conference, Chicago, IL, USA (2004)
Salas, E., Fiore, S., Letsky, M.: Theories of team cognition: Cross-disciplinary perspectives. Routledge Academic (2011)
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)
Fiore, S.: Personal communication
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)
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)
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)
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)
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)
Burion, S.: Human detection for robotic urban search and rescue. Diploma Work (2004)
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)
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)
Thrun, S., Burgard, W., Fox, D., et al.: Probabilistic robotics, vol. 1. MIT Press, Cambridge (2005)
Cover, T., Thomas, J.: Elements of information theory. Wiley Interscience (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Goodrich, M.A., Yi, D. (2013). Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach. In: Shumaker, R. (eds) Virtual Augmented and Mixed Reality. Designing and Developing Augmented and Virtual Environments. VAMR 2013. Lecture Notes in Computer Science, vol 8021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39405-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-39405-8_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39404-1
Online ISBN: 978-3-642-39405-8
eBook Packages: Computer ScienceComputer Science (R0)