Abstract
Artificially intelligent agents increasingly collaborate with humans in human-agent teams. Timely proactive sharing of relevant information within the team contributes to the overall team performance. This paper presents a machine learning approach to proactive communication in AI-agents using contextual factors. Proactive communication was learned in two consecutive experimental steps: (a) multi-agent team simulations to learn effective communicative behaviors, and (b) human-agent team experiments to refine communication suitable for a human team member. Results consist of proactive communication policies for communicating both beliefs and goals within human-agent teams. Agents learned to use minimal communication to improve team performance in simulation, while they learned more specific socially desirable behaviors in the human-agent team experiment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bernsen, N.O., Dybkjær, L.: Building usable spoken dialogue systems: some approaches. Sprache und Datenverarbeitung 28(2), 111–131 (2004)
Bradshaw, J.M., et al.: Adjustable autonomy and human-agent teamwork in practice: an interim report on space applications. In: Weiss, G., Hexmoor, H., Castelfranchi, C., Falcone, R. (eds.) Agent Autonomy, vol. 7, pp. 243–280. Kluwer Academic Press, Boston (2003). https://doi.org/10.1007/978-1-4419-9198-0_11. http://link.springer.com/10.1007/978-1-4419-9198-0 11
Butchibabu, A.: Anticipatory Communication Strategies for Human Robot Team Coordination (2016)
Chu-Carroll, J.: MIMIC: an adaptive mixed initiative spoken dialogue system for information queries. In: Proceedings of the Sixth Conference on Applied Natural Language Processing, pp. 97–104. Association for Computational Linguistics, Seattle (2000). https://doi.org/10.3115/974147.974161, http://portal.acm.org/citation.cfm?doid=974147.974161
Costa, A.C., Anderson, N.: Measuring trust in teams: development and validation of a multifaceted measure of formative and reflective indicators of team trust. Eur. J. Work Organ. Psychol. 20(1), 119–154 (2011). https://doi.org/10.1080/13594320903272083, http://www.tandfonline.com/doi/abs/10.1080/13594320903272083
Foerster, J., Assael, I.A., de Freitas, N., Whiteson, S.: Learning to communicate with deep multi-agent reinforcement learning. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 2137–2145. Curran Associates, Inc. (2016). http://papers.nips.cc/paper/6042-learning-to-communicate-with-deep-multi-agent-reinforcement-learning.pdf
Goldman, C.V., Zilberstein, S.: Optimizing information exchange in cooperative multi-agent systems. In: Proceedings of the Second International Joint Conference on Autonomous Agents and Multi Agent Systems, pp. 137–144. ACM Press, Melbourne (2003)
Jarvenpaa, S.L., Leidner, D.E.: Communication and trust in global virtual teams. Organ. Sci. 10(6), 791–815 (1999). https://doi.org/10.1287/orsc.10.6.791, http://pubsonline.informs.org/doi/abs/10.1287/orsc.10.6.791
Johnson, M., Bradshaw, J.M., Feltovich, P.J., Jonker, C.M., van Riemsdijk, B., Sierhuis, M.: The fundamental principle of coactive design: interdependence must shape autonomy. In: De Vos, M., Fornara, N., Pitt, J.V., Vouros, G. (eds.) COIN -2010. LNCS (LNAI), vol. 6541, pp. 172–191. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21268-0_10. http://link.springer.com/10.1007/978-3-642-21268-0 10
Johnson, M., Jonker, C., van Riemsdijk, B., Feltovich, P.J., Bradshaw, J.M.: Joint activity testbed: blocks world for teams (BW4T). In: Aldewereld, H., Dignum, V., Picard, G. (eds.) ESAW 2009. LNCS (LNAI), vol. 5881, pp. 254–256. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10203-5_26. http://link.springer.com/10.1007/978-3-642-10203-5 26
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). https://doi.org/10.1109/MIS.2004.74
Kruijff, G.J.M., Janıcek, M., Keshavdas, S., Larochelle, B., Zender, H.: Experience in system design for human-robot teaming in urban search & rescue. In: 8th International Conference on Field and Service Robotics, Matsushima, Japan, pp. 1–14 (2012)
Lazaridou, A., Peysakhovich, A., Baroni, M.: Multi-agent cooperation and the emergence of (natural) language. arXiv:1612.07182 [cs], December 2016
Lewis, J.R.: IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. Int. J. Hum.-Comput. Interact. 7, 57–78 (1995)
Singh, D., Hindriks, K.V.: Learning to improve agent behaviours in GOAL. In: Dastani, M., Hübner, J.F., Logan, B. (eds.) ProMAS 2012. LNCS (LNAI), vol. 7837, pp. 158–173. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38700-5_10. http://link.springer.com/10.1007/978-3-642-38700-5 10
Sordoni, A., et al.: A neural network approach to context-sensitive generation of conversational responses. arXiv:1506.06714 [cs], June 2015
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
van Zoelen, E.M., Cremers, A., Dignum, F.P.M., van Diggelen, J., Peeters, M.M. (2020). Learning to Communicate Proactively in Human-Agent Teaming. In: De La Prieta, F., et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection. PAAMS 2020. Communications in Computer and Information Science, vol 1233. Springer, Cham. https://doi.org/10.1007/978-3-030-51999-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-51999-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51998-8
Online ISBN: 978-3-030-51999-5
eBook Packages: Computer ScienceComputer Science (R0)