Abstract
Training in the US Army starts with the individual. Soldiers work on acquiring skills, knowledge, and attributes in order to perform tasks to support operational requirements. Feedback is provided through After-Action Reviews (AARs) to support training and improve future operations. A main difficulty for developing effective training for manned-unmanned teams (MUM-T) is that AARs with human-agent teams are yet to be developed. While AAR processes for human teams are well trained in the Army, the current methods for delivering an AAR do not account for unmanned systems that are integrated in collective tasks. The US Army’s Robotic Wingman program provides a use case for discussing potential technology solutions that can support critical human factors for MUM-T during a gunnery collective task. Understanding the capabilities and limitations of an unmanned platform will help develop effective training plans and performance measures for the unmanned asset which is now part of the team.
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Notes
- 1.
The Training Circular (TC) 25-20, A Leaders Guide to After-Action Reviews is the guide that the Army developed to help leaders plan, prepare, and conduct an effective AAR [2].
- 2.
- 3.
ARES handles the decision making for the weapon system. Identification of a target in the field of view adds a red box to highlight the target, currently depicted on the RVG WMI.
References
Morrison, J.E., Meliza, L.L.: Foundations of the After Action Review Process. Institute for Defense Analyses, Alexandria (1999)
Command, U.A.T.a.D.: A leader’s guide to after-action reviews. In: (US), D.o.t.A. (ed.) Training Circular No.: TC 25-20, Washington, DC (1993 Sep 30)
DeGrosky, M.T.: Improving After Action Review (AAR) Practice. The Association (2005)
Allen, G., Smith, R.: After action review in military training simulations. In: Proceedings of Winter Simulation Conference, pp. 845–849. IEEE (1994)
Gratch, J., Mao, W.: Automating after action review: attributing blame or credit in team training. University of Southern California Marina Del Rey Information Sciences Institute (2003)
Johnson, C., Gonzalez, A.J.: Automated after action review: state-of-the-art review and trends. J. Defense Model. Simul. 5, 108–121 (2008)
Smith-Jentsch, K.A., Johnston, J.H., Payne, S.C.: Measuring team-related expertise in complex environments. Mak. Decisions Stress: Implications Individ. Team Training 1, 61–87 (1998)
De Dreu, C.K., Weingart, L.R.: Task versus relationship conflict, team performance, and team member satisfaction: a meta-analysis. J. Appl. Psychol. 88, 741 (2003)
Marks, M.A., Mathieu, J.E., Zaccaro, S.J.: A temporally based framework and taxonomy of team processes. Acad. Manag. Rev. 26, 356–376 (2001)
Blickensderfer, E., Cannon-Bowers, J.A., Salas, E.: Theoretical bases for team self-correction: fostering shared mental models. Adv. Interdisc. Stud. Work Teams 4, 249–279 (1997)
Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors 37, 32–64 (1995)
Salas, E., Sims, D.E., Burke, C.S.: Is there a “big five” in teamwork? Small Group Res. 36, 555–599 (2005)
Marks, M.A., Panzer, F.J.: The influence of team monitoring on team processes and performance. Hum. Performance 17, 25–41 (2004)
Schaefer, K.E., Aksaray, D., Wright, J.L., Roy, N.: Challenges with addressing context with AI and human-agent teaming. In: Lawless, W.F., Mittu, R., Sofge, D. (eds.) Computational Context: The Value, Theory and Application of Context with AI. CRC Press, Boca Raton (2019)
Schaefer, K.E., Oh, J., Aksaray, D., Barber, D.: Integrating context into artificial intelligence: research from the robotics collaborative technology alliance. AI Magazine (in press)
Sasou, K., Reason, J.: Team errors: definition and taxonomy. Reliab. Eng. Syst. Saf. 65, 1–9 (1999)
Mckinney Jr., E.H., Barker, J.R., Davis, K.J., Smith, D.: How swift starting action teams get off the ground: what United flight 232 and airline flight crews can tell us about team communication. Manage. Commun. Quart. 19, 198–237 (2005)
Cooke, N.J., Gorman, J.C., Myers, C.W., Duran, J.L.: Interactive team cognition. Cogn. Sci. 37, 255–285 (2013)
McNeese, N.J., Demir, M., Cooke, N.J., Myers, C.: Teaming with a synthetic teammate: insights into human-autonomy teaming. Hum. Factors 60, 262–273 (2018)
Bisk, Y., Yuret, D., Marcu, D.: Natural language communication with robots. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 751–761 (2016)
Schaefer, K.E., Straub, E.R., Chen, J.Y., Putney, J., Evans III, A.W.: Communicating intent to develop shared situation awareness and engender trust in human-agent teams. Cogn. Syst. Res. 46, 26–39 (2017)
Hoff, K.A., Bashir, M.: Trust in automation: Integrating empirical evidence on factors that influence trust. Hum. Factors 57, 407–434 (2015)
Meliza, L.L., Goldberg, S.L., Lampton, D.R.: After action review in simulation-based training. Army Research IInst for the Behavioral and Social Sciences (2007)
Kaber, D.B., Endsley, M.R.: Team situation awareness for process control safety and performance. Process Saf. Prog. 17, 43–48 (1998)
de Jong, B.A., Dirks, K.T., Gillespie, N.: Trust and team performance: a meta-analysis of main effects, contingencies, and qualifiers. In: Academy of Management Proceedings, p. 14561. Academy of Management Briarcliff Manor, NY 10510 (2015)
Schaefer, K.E., Chen, J.Y., Szalma, J.L., Hancock, P.A.: A meta-analysis of factors influencing the development of trust in automation: implications for understanding autonomy in future systems. Hum. Factors 58, 377–400 (2016)
Costa, A.C.: Work team trust and effectiveness. Pers. Rev. 32, 605–622 (2003)
Lee, J.D., See, K.A.: Trust in automation: designing for appropriate reliance. Hum. Factors 46, 50–80 (2004)
Gremillion, G.M., Donavanik, D., Neubauer, C.E., Brody, J.D., Schaefer, K.E.: Estimating human state from simulated assisted driving with stochastic filtering techniques. In: International Conference on Applied Human Factors and Ergonomics, pp. 113–125. Springer (2018)
Salas, E., Rosen, M.A., Burke, C.S., Goodwin, G.F.: The wisdom of collectives in organizations: an update of the teamwork competencies. In: Team Effectiveness in Complex Organizations: Cross-Disciplinary Perspectives and Approaches, pp. 39–79. Routledge, New York (2009)
Command, U.A.T.a.D.: Training and qualification, crew. In: (US), D.o.t.A. (ed.) Training Circular No.: TC 3-20.31, Washington, DC (2015 Mar 17)
A leader’s guide to after-action reviews. U.S. Army Training Circular 25-20 (1993)
Smith-Jentsch, K.A., Cannon-Bowers, J.A., Tannenbaum, S.I., Salas, E.: Guided team self-correction: impacts on team mental models, processes, and effectiveness. Small Group Res. 39, 303–327 (2008)
Dyer, J.L., Wampler, R.L., Blankenbeckler, P.N.: After action reviews with the ground Soldier system. Army Research IInst for the Behavioral and Social Sciences (2005)
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Brewer, R.W., Walker, A.J., Pursel, E.R., Cerame, E.J., Baker, A.L., Schaefer, K.E. (2020). Assessment of Manned-Unmanned Team Performance: Comprehensive After-Action Review Technology Development. In: Chen, J. (eds) Advances in Human Factors in Robots and Unmanned Systems. AHFE 2019. Advances in Intelligent Systems and Computing, vol 962. Springer, Cham. https://doi.org/10.1007/978-3-030-20467-9_11
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