Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-25T14:58:05.867Z Has data issue: false hasContentIssue false
  • English
  • Français

COMMUNICATION IN AI-ASSISTED TEAMS DURING AN INTERDISCIPLINARY DRONE DESIGN PROBLEM

Published online by Cambridge University Press:  27 July 2021

Joshua T. Gyory ,
Binyang Song ,
Jonathan Cagan  and
Christopher McComb
Joshua T. Gyory
Affiliation:
Carnegie Mellon University;
Binyang Song
Affiliation:
Pennsylvania State University
Jonathan Cagan*
Affiliation:
Carnegie Mellon University;
Christopher McComb
Affiliation:
Pennsylvania State University
*
Cagan, Jonathan, Carnegie Mellon University, Mechanical Engineering, United States of America, [email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Human-artificial intelligent (AI) - assisted teaming is becoming a strategy for coalescing the complementary strengths of humans and computers to solve difficult tasks. Yet, there is still much to learn regarding how the integration of humans with AI agents into a team affects human behavior. Accordingly, this work begins to inform this research gap by focusing specifically on how the communication structure and interaction changes within AI-assisted human teams. The underlying discourse data for this work originates from a prior research study in which teams solve an interdisciplinary drone design and path-planning problem. Several metrics are employed in this work to study team discourse, including count, diversity, content richness, and semantic coherence. Results show significant differences in communication behavior in AI-assisted teams including more diversity and frequency in communication, more exchange of information regarding principal design parameters and problem-solving strategies, and more cohesion. Overall, this work takes meaningful steps towards understanding the effects of AI agents on human behavior in teams, critical for fully building effective human-AI hybrid teams in the future.

Keywords

Artificial intelligenceCommunicationHuman behaviour in designHybrid teaming
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 651 - 660
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Bird, S., Klein, E. and Loper, E., 2009. “Natural language processing with Python: analyzing text with the natural language toolkit.” O'Reilly Media, Inc.Google Scholar
Blei, D.M., Ng, A.Y. and Jordan, M.I., 2003. “Latent dirichlet allocation.” Journal of Machine Learning Research, 3(Jan), pp.993–1022.Google Scholar
Bowers, C.A., Jentsch, F., Salas, E. and Braun, C.C., 1998. “Analyzing communication sequences for team training needs assessment.” Human factors, 40(4), pp.672–679.CrossRefGoogle Scholar
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K. and Harshman, R., 1990. “Indexing by latent semantic analysis.” Journal of the American society for information science, 41(6), pp.391–407.3.0.CO;2-9>CrossRefGoogle Scholar
Dellermann, D., Ebel, P., Söllner, M. and Leimeister, J.M., 2019. “Hybrid intelligence.” Bus Inf Syst Eng.CrossRefGoogle Scholar
Dong, A., Hill, A.W. and Agogino, A.M., 2004. “A document analysis method for characterizing design team performance.” Journal of Mechanical Design, 126(3), pp.378–385.CrossRefGoogle Scholar
Ezer, N., Bruni, S., Cai, Y., Hepenstal, S.J., Miller, C.A. and Schmorrow, D.D., 2019, November. Trust Engineering for Human-AI Teams. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 63, No. 1, pp. 322–326). Sage CA: Los Angeles, CA: SAGE Publications.Google Scholar
Gorman, J.C., Foltz, P.W., Kiekel, P.A., Martin, M.J. and Cooke, N.J., 2003, October. “Evaluation of Latent Semantic Analysis-based measures of team communications content.” In Proceedings of the Human Factors and Ergonomics Society annual meeting (Vol. 47, No. 3, pp. 424–428). Sage CA: Los Angeles, CA: SAGE Publications.Google Scholar
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S. and Yang, G.Z., 2019. XAI—Explainable artificial intelligence. Science Robotics, 4(37).CrossRefGoogle ScholarPubMed
Gyory, J.T., Kotovsky, K. and Cagan, J., 2021. “The Influence of Process Management: Uncovering the Impact of Real-Time Managerial Interventions via a Topic Modelling Approach” Journal of Mechanical Design.CrossRefGoogle Scholar
Hendry, D.G., 2004, August. “Communication functions and the adaptation of design representations in interdisciplinary teams.” In Proceedings of the 5th conference on Designing interactive systems: processes, practices, methods, and techniques (pp. 123–132).CrossRefGoogle Scholar
Higgins, P.G., 1999. “Job shop scheduling: Hybrid intelligent human-computer paradigm.” University of Melbourne, Department of Mechanical and Manufacturing Engineering.Google Scholar
Hu, Y. and Taylor, M.E., A Computer-Aided Design Intelligent Tutoring System Teaching Strategic Flexibility. In: 2016 ASEE Annual Conference & Exposition Proceedings. ASEE Conferences. Epub ahead of print 2016. https://dx.doi.org/10.18260/p.27208.CrossRefGoogle Scholar
Inrak, P. and Sinthupinyo, S., 2010, April. Applying latent semantic analysis to classify emotions in Thai text. In 2010 2nd International Conference on Computer Engineering and Technology (Vol. 6, pp. V6-450). IEEE.Google Scholar
Kane, L., Clayton, M.F., Baucom, B.R., Ellington, L. and Reblin, M., 2020. Measuring Communication Similarity Between Hospice Nurses and Cancer Caregivers Using Latent Semantic Analysis. Cancer nursing, 43(6), pp.506–513.CrossRefGoogle ScholarPubMed
Kratzer, J., Leenders, R.T.A. and Van Engelen, J.M., 2010. “The social network among engineering design teams and their creativity: A case study among teams in two product development programs.” International Journal of Project Management, 28(5), pp.428–436.CrossRefGoogle Scholar
Lake, B.M., Ullman, T.D., Tenenbaum, J.B. and Gershman, S.J., 2017. “Building machines that learn and think like people.” Behavioral and brain sciences, 40.CrossRefGoogle ScholarPubMed
Landauer, T.K., Foltz, P.W. and Laham, D., 1998. “An introduction to latent semantic analysis.” Discourse processes, 25(2-3), pp.259–284.CrossRefGoogle Scholar
Liew, C., 2018. “The future of radiology augmented with artificial intelligence: a strategy for success.” European journal of radiology, 102, pp.152–156.CrossRefGoogle Scholar
Roll, I., Wiese, E.S., Long, Y., Aleven, V. and Koedinger, K.R., 2014. “Tutoring self-and co-regulation with intelligent tutoring systems to help students acquire better learning skills.” Design recommendations for intelligent tutoring systems, 2, pp.169–182.Google Scholar
Sexton, J.B. and Helmreich, R.L., 2000. “Analyzing cockpit communications: the links between language, performance, error, and workload.” Human Performance in Extreme Environments, 5(1), pp.63–68.Google ScholarPubMed
Song, B., Gyory, J.T., Zhang, G., Soria Zurita, N.F., Stump, G., Martin, J., Miller, S., Balon, B., Yukish, M., McComb, C., Cagan, J. “Decoding the agility of human-artificial intelligence hybrid teams in complex problem solving.” In submission.Google Scholar
Song, B., Zurita, N.S., Zhang, G., Stump, G., Balon, C., Miller, S.W., Yukish, M., Cagan, J. and McComb, C., 2020, May. “Toward Hybrid Teams: A Platform to Understand Human-Computer Collaboration During the Design of Complex Engineered Systems.” In Proceedings of the Design Society: DESIGN Conference (Vol. 1, pp. 1551–1560). Cambridge University Press.Google Scholar
Sonnenwald, D.H., 1996. “Communication roles that support collaboration during the design process.” Design studies, 17(3), pp.277–301.CrossRefGoogle Scholar
Stolcke, A., Ries, K., Coccaro, N., Shriberg, E., Bates, R., Jurafsky, D., Taylor, P., Martin, R., Ess-Dykema, C.V. and Meteer, M., 2000. “Dialogue act modeling for automatic tagging and recognition of conversational speech.” Computational linguistics, 26(3), pp.339–373.CrossRefGoogle Scholar
Stump, G.M., Miller, S.W., Yukish, M.A., Simpson, T.W. and Tucker, C., 2019. “Spatial Grammar-Based Recurrent Neural Network for Design Form and Behavior Optimization.” Journal of Mechanical Design, 141(12).CrossRefGoogle Scholar
Toft, Y., Howard, P. and Jorgensen, D., 2003. “Human-centered engineers—a model for holistic interdisciplinary communication and professional practice.” International journal of industrial ergonomics, 31(3), pp.195–202.CrossRefGoogle Scholar
Vrana, S.R., Vrana, D.T., Penner, L.A., Eggly, S., Slatcher, R.B. and Hagiwara, N., 2018. Latent Semantic Analysis: A new measure of patient-physician communication. Social Science & Medicine, 198, pp.22–26.CrossRefGoogle ScholarPubMed
Wu, Q. and Cormican, K., 2016. “Shared leadership and team creativity: A social network analysis in engineering design teams.” Journal of technology management & innovation, 11(2), pp.2–12.CrossRefGoogle Scholar
Yang, Q, Steinfeld, A, Rosé, C, et al. , 2020. Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design. https://dx.doi.org/10.1145/3313831.3376301.CrossRefGoogle Scholar
Zhang, G., Raina, A., Cagan, J., McComb, C., 2021. “A Cautionary Tale about the Impact of AI on Human Design Teams.” Design Studies, 72(2021), 100990.CrossRefGoogle Scholar
Zhang, G., Soria Zurita, N.F., Song, B., McComb, C., Cagan, J., 2020. “Data on the design and operation of drones by both individuals and teams.” Data in Brief. (Accepted)CrossRefGoogle Scholar