Abstract
With recent progress in connectivity and the transition into knowledge economies, mixed human and agent teams will become increasingly commonplace in both our personal and professional spheres. Hence, further examination of factors that affect human satisfaction in these types of teams can inform the design and use of effective human-agent teams. In particular, we are interested in virtual and ad-hoc team scenarios where a human is paired with an agent, and both need to assess and adapt to the capabilities of the partner to maximize team performance. We designed, implemented, and experimented with an environment in which virtual human-agent teams repeatedly collaborate to complete heterogeneous task sets. We investigate the role humans and autonomous agents play, as task allocators, on human satisfaction of virtual and adhoc human-agent repeated teamwork. We evaluated the satisfaction of human participants, recruited from the Amazon Mechanical Turk platform, using survey questions focusing on different aspects of team collaboration, including the process, allocation strategy, team performance, and agent partner. We find that participants are more satisfied (a) with the process when they allocate and (b) with the team outcome when the agent is allocating. We analyze and identify the factors that contributed to this result. This work contributes to our understanding of how to allocate tasks in virtual and ad hoc human-agent to improve team effectiveness.
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Notes
- 1.
Agent expertise is simulated by flip** a coin with success probability of \(P_t\), the confidence level.
References
Anderson, A., Kleinberg, J., Mullainathan, S.: Assessing human error against a benchmark of perfection. ACM Trans. Knowl. Discov. Data (TKDD) 11(4), 1–25 (2017)
Brinkman, W.P.: Design of a questionnaire instrument. In: Handbook of Mobile Technology Research Methods, pp. 31–57. Nova Publishers (2009)
Genter, K., Agmon, N., Stone, P.: Role-based ad hoc teamwork. In: Proceedings of the Plan, Activity, and Intent Recognition Workshop at the Twenty-Fifth Conference on Artificial Intelligence (PAIR-11) (2011)
Gervits, F., Thurston, D., Thielstrom, R., Fong, T., Pham, Q., Scheutz, M.: Toward genuine robot teammates: Improving human-robot team performance using robot shared mental models. In: AAMAS, pp. 429–437 (2020)
Gladstein, D.L.: Groups in context: A model of task group effectiveness. Administrative Science Quarterly, pp. 499–517 (1984)
Green, S.G., Taber, T.D.: The effects of three social decision schemes on decision group process. Organ. Behav. Hum. Perform. 25(1) (1980)
Hauser, D., Paolacci, G., Chandler, J.: Common concerns with mturk as a participant pool: Evidence and solutions (2019)
Hayes-Roth, B.: A blackboard architecture for control. Artif. Intell. 26(3), 251–321 (1985)
Hertel, G., Geister, S., Konradt, U.: Managing virtual teams: A review of current empirical research. Hum. Resour. Manag. Rev. 15(1), 69–95 (2005)
Kahneman, D.: Thinking, Fast and Slow. Macmillan (2011)
Lai, V., Tan, C.: On human predictions with explanations and predictions of machine learning models: A case study on deception detection. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 29–38 (2019)
Larson, L., DeChurch, L.A.: Leading teams in the digital age: Four perspectives on technology and what they mean for leading teams. Leadersh. Quart. 31(1), 101377 (2020)
Mathieu, J.E., Hollenbeck, J.R., van Knippenberg, D., Ilgen, D.R.: A century of work teams in the journal of applied psychology. J. Appl. Psychol. 102(3), 452 (2017)
Mosteo, A.R., Montano, L.: A survey of multi-robot task allocation. Instituto de Investigacin en Ingenierła de Aragn (I3A), Tech. Rep. (2010)
Prajod, P., Al Owayyed, M., Rietveld, T., van der Steeg, J.J., Broekens, J.: The effect of virtual agent warmth on human-agent negotiation. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pp. 71–76 (2019)
Puranam, P., Alexy, O., Reitzig, M.: What’s “new” about new forms of organizing? Acad. Manag. Rev. 39(2), 162–180 (2014)
Reinig, B.A.: Toward an understanding of satisfaction with the process and outcomes of teamwork. J. Manag. Inf. Syst. 19(4), 65–83 (2003)
Rosenfeld, A., Agmon, N., Maksimov, O., Kraus, S.: Intelligent agent supporting human-multi-robot team collaboration. Artif. Intell. 252, 211–231 (2017)
Shoham, Y., Leyton-Brown, K.: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press (2008)
Stark, E.M., Bierly, P.E., III.: An analysis of predictors of team satisfaction in product development teams with differing levels of virtualness. R &d Management 39(5), 461–472 (2009)
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Abuhaimed, S., Sen, S. (2023). Human Satisfaction in Ad Hoc Human-Agent Teams. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14051. Springer, Cham. https://doi.org/10.1007/978-3-031-35894-4_15
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