Abstract
Robots are increasingly embedded in human societies where they encounter human collaborators, potential adversaries, and even uninvolved by-standers. Such robots must plan to accomplish joint goals with teammates while avoiding interference from competitors, possibly utilizing bystanders to advance the robot’s goals. We propose a planning framework for robot task and action planners that can cope with collaborative, competitive, and non-involved human agents at the same time by using mental models of human agents. By querying these models, the robot can plan for the effects of future human actions and can plan robot actions to influence what the human will do, even when influencing them through explicit communication is not possible. We implement the framework in a planner that does not assume that human agents share goals with, or will cooperate with, the robot. Instead, it can handle the diverse relations that can emerge from interactions between the robot’s goals and capacities, the task environment, and the human behavior predicted by the planner’s models. We report results from an evaluation where a teleoperated robot executes a planner-generated policy to influence the behavior of human participants. Since the robot is not capable of performing some of the actions necessary to achieve its goal, the robot instead tries to cause the human to perform those actions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson, J.R.: Rules of the Mind. Psychology Press, Hove (2014)
Ben Larbi, R., Konieczny, S., Marquis, P.: Extending classical planning to the multi-agent case: a game-theoretic approach. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 731–742. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75256-1_64
Bolander, T.: A gentle introduction to epistemic planning: the DEL approach. Electron. Proc. Theoret. Comput. Sci. 243, 1–22 (2017). https://doi.org/10.4204/eptcs.243.1
Bowling, M., Jensen, R., Veloso, M.: Multi-agent planning in the presence of multiple goals, Chap. 10, pp. 301–325. Wiley (2006). https://doi.org/10.1002/0471781266.ch10. https://onlinelibrary.wiley.com/doi/abs/10.1002/0471781266.ch10
Brafman, R.I., Domshlak, C.: From one to many: planning for loosely coupled multi-agent systems. In: ICAPS, pp. 28–35 (2008)
Brafman, R.I., Domshlak, C., Engel, Y., Tennenholtz, M.: Planning games. In: IJCAI, pp. 73–78 (2009)
Buzing, P., ter Mors, A., Valk, J., Witteveen, C.: Coordinating self-interested planning agents. Auton. Agents Multi-Agent Syst. 12(2), 199–218 (2006). https://doi.org/10.1007/s10458-005-6104-4
Chakraborti, T., Kambhampati, S.: Algorithms for the greater good! On mental modeling and acceptable symbiosis in human-AI collaboration. CoRR abs/1801.09854 (2018). http://arxiv.org/abs/1801.09854
Chakraborti, T., Kambhampati, S., Scheutz, M., Zhang, Y.: AI challenges in human-robot cognitive teaming. arXiv preprint arXiv:1707.04775 (2017)
Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S.: Planning with trust for human-robot collaboration. In: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2018, pp. 307–315. ACM, New York (2018). https://doi.org/10.1145/3171221.3171264. https://doi.acm/10.1145/3171221.3171264
Chidambaram, V., Chiang, Y., Mutlu, B.: Designing persuasive robots: how robots might persuade people using vocal and nonverbal cues. In: 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 293–300 (2012). https://doi.org/10.1145/2157689.2157798
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). https://doi.org/10.1145/1869397.1869404. https://doi.acm/10.1145/1869397.1869404
Dragan, A.D.: Robot planning with mathematical models of human state and action (2017)
Ghallab, M., Nau, D., Traverso, P.: Automated Planning and Acting. Cambridge University Press, Cambridge (2016). https://doi.org/10.1017/CBO9781139583923
Görür, O.C., Rosman, B., Sivrikaya, F., Albayrak, S.: Social cobots: anticipatory decision-making for collaborative robots incorporating unexpected human behaviors. In: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2018, pp. 398–406. ACM, New York (2018). https://doi.org/10.1145/3171221.3171256. https://doi.acm/10.1145/3171221.3171256
Gray, J., Breazeal, C.: Manipulating mental states through physical action. Int. J. Soc. Robot. 6(3), 315–327 (2014). https://doi.org/10.1007/s12369-014-0234-2
Kulkarni, A., Srivastava, S., Kambhampati, S.: Implicit robot-human communication in adversarial and collaborative environments. CoRR abs/1802.06137 (2018). http://arxiv.org/abs/1802.06137
Laird, J.E.: The Soar Cognitive Architecture. MIT Press, Cambridge (2012)
Milliez, G., Lallement, R., Fiore, M., Alami, R.: Using human knowledge awareness to adapt collaborative plan generation, explanation and monitoring. In: 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 43–50 (2016). https://doi.org/10.1109/HRI.2016.7451732
Muise, C., et al.: Planning over multi-agent epistemic states: a classical planning approach. In: Proceedings of AAAI 2012, The Twenty-Sixth AAAI Conference on Artificial Intelligence (2015). https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9974
Muise, C., Felli, P., Miller, T., Pearce, A.R., Sonenberg, L.: Leveraging FOND planning technology to solve multi-agent planning problems. In: Workshop on Distributed and Multi-Agent Planning (DMAP 2015) (2015). http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf
Nikolaidis, S., Hsu, D., Srinivasa, S.: Human-robot mutual adaptation in collaborative tasks: models and experiments. Int. J. Robot. Res. 36(5–7), 618–634 (2017). https://doi.org/10.1177/0278364917690593
Nikolaidis, S., Kwon, M., Forlizzi, J., Srinivasa, S.: Planning with verbal communication for human-robot collaboration. ACM Trans. Hum.-Robot Interact. 7(3), 22:1–22:21 (2018). https://doi.org/10.1145/3203305
Nissim, R., Brafman, R.I., Domshlak, C.: A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Volume 1, AAMAS 2010, vol. 1, pp. 1323–1330. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2010). http://dl.acm.org/citation.cfm?id=1838206.1838379
Prunera, J.J.M.: Non-cooperative games for self-interested planning agents. Ph.D. thesis, Universitat Politècnica de València (2017). https://doi.org/10.4995/Thesis/10251/90417
Scheutz, M., DeLoach, S., Adams, J.: A framework for developing and using shared mental models in human-agent teams. J. Cogn. Eng. Decis. Mak. 11(3), 203–224 (2017)
Talamadupula, K., Briggs, G., Chakraborti, T., Scheutz, M., Kambhampati, S.: Coordination in human-robot teams using mental modeling and plan recognition. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 2957–2962. IEEE (2014)
Torreño, A., Onaindia, E., Komenda, A., Stolba, M.: Cooperative multi-agent planning: a survey. CoRR abs/1711.09057 (2017). http://arxiv.org/abs/1711.09057
de Weerdt, M., Clement, B.: Introduction to planning in multiagent systems. Multiagent Grid Syst. 5, 345–355 (2009)
Acknowledgements
This work was in part funded by AFOSR grant number FA9550-18-1-0465 and NASA grant number C17-2D00-TU.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Buckingham, D., Chita-Tegmark, M., Scheutz, M. (2020). Robot Planning with Mental Models of Co-present Humans. In: Wagner, A.R., et al. Social Robotics. ICSR 2020. Lecture Notes in Computer Science(), vol 12483. Springer, Cham. https://doi.org/10.1007/978-3-030-62056-1_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-62056-1_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62055-4
Online ISBN: 978-3-030-62056-1
eBook Packages: Computer ScienceComputer Science (R0)