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Introduction to “Systems Engineering and Artificial Intelligence” and the Chapters Read chapter Read first chapter

2021 | OriginalPaper | Chapter

7. Human-Autonomy Teaming for the Tactical Edge: The Importance of Humans in Artificial Intelligence Research and Development

Authors : Kristin E. Schaefer, Brandon Perelman, Joe Rexwinkle, Jonroy Canady, Catherine Neubauer, Nicholas Waytowich, Gabriella Larkin, Katherine Cox, Michael Geuss, Gregory Gremillion, Jason S. Metcalfe, Arwen DeCostanza, Amar Marathe

Published in: Systems Engineering and Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

The U.S. Army is currently working to integrate artificial intelligence, or AI-enabled systems, into military working teams in the form of both embodied (i.e., robotic) and embedded (i.e., computer or software) intelligent agents with the express purpose of improving performance during all phases of the mission. However, this is largely uncharted territory, making it unclear how to do this integration effectively for human-AI teams. This chapter provides an overview of the Combat Capabilities Development Command (DEVCOM) Army Research Laboratory’s effort to address the human as a critical gap with associated implications on effective teaming. This chapter articulates four major research thrusts critical to integrating AI-enabled systems into military operations, giving examples within these broader thrusts that are currently addressing specific research gaps. The four major research thrusts include: (1) Enabling Soldiers to predict AI; (2) Quantifying Soldier understanding for AI; (3) Soldier-guided AI adaptation; and (4) Characterizing Soldier-AI performance. These research thrusts are the organizing basis for explaining a path toward integration and effective human-autonomy teaming at the tactical edge.

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previous chapter Systems Engineering for Artificial Intelligence-based Systems: A Review in Time
next chapter Re-orienting Toward the Science of the Artificial: Engineering AI Systems
Footnotes
1
The original framing by Fitts (1951) was men-are-better-at, machines-are-better-at (MABA-MABA).
 
2
A full history of AI development is not within the scope of this paper; however, there are several detailed reviews that are worth exploring (see Haenlein & Kaplan, 2019).
 
3
An early example of an application of the expert systems approach was the MYCIN system, which predicted the types of bacteria that were most likely to be causing an infection by calculating a level of “belief” in the form of probabilities that were based on a selection of targeted questions that a physician would answer (Shortliffe and Buchanan, 1975). While the system performed reasonably effectively within its relatively well-defined domain, its core approach would be difficult to scale to broader diagnostics. That is, considering the number of questions a potential user would need to answer on the front end would grow exponentially, as well as the requisite complexity of the expert opinions stored on the back end, the system would need to provide accurate diagnostics across a broad spectrum of diseases.
 
Literature
Bao, M., & Engel, S. A. (2019). Augmented reality as a tool for studying visual plasticity: 2009–2018. Current Directions in Psychological Science, 28, 574–580.CrossRef
Berman, D. (2018). Representations of spatial frequency, depth, and higher-level image content in human visual cortex (Doctoral dissertation, The Ohio State University).
Brady, T. F., Shafer-Skelton, A., & Alvarez, G. A. (2017). Global ensemble texture representations are critical to rapid scene perception. Journal of Experimental Psychology: Human Perception and Performance, 43(6), 1160.
Butchibabu, A., Sparano-Huiban, C., Sonenberg, L., & Shah, J. (2016). Implicit coordination strategies for effective team communication. Human Factors, 58(4), 595–610.CrossRef
Chen, J. Y. C., Procci, K., Boyce, M., Wright, J. L., Garcia, A., & Barnes, M. (2014). Situation awareness-based agent transparency (No. ARL-TR-6905). Army Research Laboratory, Aberdeen Proving Ground, MD.
Chhan, D., Scharine, A., & Perelman, B. S. (2020). Human-autonomy teaming essential research program project 2: Transparent multimodal crew interface designs. technical note 3: Multimodal cueing for transparency in mobility operations. Technical note ARL-TN-1019. Aberdeen Proving Ground, MD: CCDC Army Research Laboratory.
Cooke, N. J., Gorman, J. C., Myers, C. W., & Duran, J. L. (2013). Interactive team cognition. Cognitive Science, 37, 255–285.CrossRef
DeCostanza, A. H., Marathe, A. R., Bohannon, A., Evans, A. W., Palazzolo, E. T., Metcalfe, J. S., & McDowell, K. (2018). Enhancing human-agent teaming with individualized, adaptive technologies: A discussion of critical scientific questions. Technical report ARL-TR-8359. Aberdeen Proving Ground, MD: US Army Research Laboratory.
Dekker, S. W., & Woods, D. D. (2002). MABA-MABA or Abracadabra? Progress on human-automation co-ordination. Cognition, Technology & Work, 4, 240–244.CrossRef
DiNocera, F., Cmilli, M., & Terenzi, M. (2007). A random glance at the flight deck: Pilots’ scanning strategies and the real-time assessment of mental workload. Journal of Cognitive Engineering and Decision Making, 1(3), 271–285.CrossRef
Findlay, J. M., & Gilchrist, I. D. (1998). Chapter 13—Eye guidance and visual search. In G. Underwood (Ed) Eye guidance in reading and scene perception (295–312), Elsevier Science.
Fitts, P. M. (1951). Human engineering for an effective air navigation and traffic control system. National Research Council.
Garcia, J. O., Brooks, J., Kerick, S., Johnson, T., Mullen, T. R., & Vettel, J. M. (2017). Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving. NeuroImage, 150, 239–249.CrossRef
Geisler, W. S. (2008). Visual perception and the statistical properties of natural scenes. Annual Review of Psychology, 59, 167–192.CrossRef
Geuss, M. N., Cooper, L., Bakdash, J., Moore, S., & Holder, E. (2020). Visualizing dynamic and uncertain battlefield information: Lessons from cognitive science. In Virtual, augmented, and mixed reality technology for multi-domain operations, international SPIE: Defense + commercial sensing.
Geuss, M. N., Larkin, G., Swoboda, J., Yu, A., Bakdash, J., White, T., & Lance, B. (2019). Intelligent squad weapon: Challenges to displaying and interacting with artificial intelligence in small arms weapon systems. In Artificial intelligence and machine learning for multi-domain operations applications (Vol. 11006, p. 110060V). International Society for Optics and Photonics.
Gandhi, S., Oates, T., Mohsenin, T., & Waytowich, N. (2019). Learning from observations using a single video demonstration and human feedback. arXiv:​1909.​13392.
Goecks, V. G., Gremillion, G. M., Lawhern, V. J., Valasek, J., & Waytowich, N. R. (2019). Efficiently combining human demonstrations and interventions for safe training of autonomous systems in real-time. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, pp. 2462–2470).https://​doi.​org/​10.​1609/​aaai.​v33i01.​33012462.
Goecks, V. G., Gremillion, G. M., Lawhern, V. J., Valasek, J., & Waytowich, N. R. (2020). Integrating behavior cloning and reinforcement learning for improved performance in dense and sparse reward environments. In Proceedings of the AAMAS 2020 conference on artificial intelligence https://​doi.​org/​10.​13140/​RG.​2.​2.​35626.​16322.
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14.CrossRef
Habtegiorgis, S. W., Jarvers, C., Rifai, K., Neumann, H., & Wahl, S. (2019). The role of bottom-up and top-down cortical interactions in adaptation to natural scene statistics. Frontiers in Neural Circuits, 13.
Hoffing, R. A., & Thurman, S. M. (2020). The state of the pupil: Moving toward enabling real-world use of pupillometry-based estimation of human states. Army research lab aberdeen proving ground md aberdeen proving ground United States.
Howe, C. Q., & Purves, D. (2002). Range image statistics can explain the anomalous perception of length. Proceedings of the National Academy of Sciences, 99(20), 13184–13188.CrossRef
Howe, C. Q., & Purves, D. (2005). The Müller-Lyer illusion explained by the statistics of image–source relationships. Proceedings of the National Academy of Sciences, 102(4), 1234–1239.CrossRef
Huey, B. M., & Wickens, C. D. (1993). Workload transition: Implications for individual and team performance. National Academy Press.
Jain, S., & Argall, B. (2019). Probabilistic human intent recognition for shared autonomy in assistive robotics. ACM Transactions on Human-Robot Interaction (THRI), 9(1), 1–23.
Kalia, A. K., Buchler, N., DeCostanza, A., & Singh, M. P. (2017). Computing team process measures from the structure and content of broadcast collaborative communications. IEEE Transactions on Computational Social Systems, 4(2), 26–39.CrossRef
Karp, R. M. (1972). Reducibility among combinatorial problems. In Complexity of computer computations (pp. 85–103). Springer, Boston, MA.
Klein, G. A. (1993). A recognition-primed decision (RPD) model of rapid decision making. In Decision making in action: Models and methods (pp. 138–147). Westport, CT: Ablex Publishing.
Kowler, E., Anderson, E., Dosher, B., & Blaser, E. (1995). The role of attention in the programming of saccades. Vision Research, 35(13), 1897–1916.CrossRef
Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society, 7(1), 48–50.MathSciNetCrossRef
Kulic, D., & Croft, E. A. (2007). Affective state estimation for human–robot interaction. IEEE Transactions on Robotics, 23(5), 991–1000.CrossRef
Lance, B. J., Larkin, G. B., Touryan, J. O., Rexwinkle, J. T., Gutstein, S. M., Gordon, S. M. Toulson, O., Choi, J., Mahdi, A., Hung, C. P., & Lawhern, V. J. (2020). Minimizing data requirements for soldier-interactive AI/ML applications through opportunistic sensing. In Proceedings of the SPIE 11413, artificial intelligence and machine learning for multi-domain operations applications. https://​doi.​org/​10.​1117/​12.​2564514.
Larkin, G. B., Geuss, M., Yu, A., Rexwinkle, J. T., Callahan-Flintoft, C., Bakdash, J. Z., Swoboda, J., Lieberman, G., Hung, C. P., Moore, S., & Lance, B. J. (2020). Augmented target recognition display recommendations. Defense Systems Information Analysis Center Journal, 7(1), 28–34.
Lyons, J. B., Wynne, K. T., Mahoney, S., & Roebke, M. A. (2019). Trust and human-machine teaming: A qualitative study. In W. Lawless, R. Mittu, D. Sofge, I. S., Moskowitz, & S. Russel (Eds.), Artificial intelligence for the internet of everything (pp. 101–116). Academic Press.
Marathe, A. R.; Schaefer, K. E., Evans, A. W., & Metcalfe J. S. (2018). Bidirectional communication for effective human-agent teaming. In J. Y. C. J. Chen, & G. Fragomeni (Eds.), Virtual, augmented and mixed reality: Interaction, navigation, visualization, embodiment, and simulation. VAMR 2018. Lecture notes in computer science (pp. 338–350). Cham: Springer.
Mares, I., Smith, M. L., Johnson, M. H., & Senju, A. (2018). Revealing the neural time-course of direct gaze processing via spatial frequency manipulation of faces. Biological Psychology, 135, 76–83.CrossRef
Marquart, G., Cabrall, C., & de Winter, J. (2015). Review of eye-realted measures of drivers’ mental workload. Procedia Manufacturing, 3, 2854–2861.CrossRef
Mathieu, J. E., Heffner, T. S., Goodwin, G. F., Salas, E., & Cannon-Bowers, J. A. (2000). The influence of shared mental models on team process and performance. Journal of Applied Psychology, 85(2), 273.CrossRef
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1956). A proposal for the dartmouth summer research project on artificial intelligence. Dartmouth.
McKenzie, G., Hegarty, M., Barrett, T., & Goodchild, M. (2016). Assessing the effectiveness of different visualizations for judgments of positional uncertainty. International Journal of Geographical Information Science, 30(2), 221–239.CrossRef
Metcalfe, J. S., Marathe, A. R., Haynes, B., Paul, V. J., Gremillion, G. M., Drnec, K., Atwater, C., Estepp, J. R., Lukos, J. R., Carter, E. C., & Nothwang, W. D. (2017). Building a framework to manage trust in automation. In Proceedings of the micro-and nanotechnology sensors, systems, and applications IX (Vol. 10194, p. 101941U). International Society for Optics and Photonics. https://​doi.​org/​10.​1117/​12/​2264245.
Motter, B. C., & Belky, E. J. (1998). The guidance of eye movements during active visual search. Vision Research, 38(12), 1805–1815.CrossRef
Padilla, L., Kay, M., & Hullman, J. (2020). Uncertainty visualization. In R. Levine (Ed.), Handbook of Computational Statistics & Data Science: Springer Science
Perelman, B. S., Evans, A. W., & Schaefer, K. E. (2020a). Where do you think you’re going? Characterizing spatial mental models from planned routes. ACM Transactions in Human Robot Interaction, 9(4) https://​doi.​org/​10.​1145/​3385008.
Perelman, B. S., Wright, J. L., Lieberman, G. A., & Lakhmani, S. (2020b). Human-autonomy teaming essential research program project 2: transparent multimodal crew interface designs. Technical note 2: Transparency in mobility planning. technical note ARL-TN-1004. Aberdeen Proving Ground, MD: CCDC Army Research Laboratory.
Perelman, B. S., Lakhmani, S., Wright, J. L., Chhan, D., Scharine, A., Evans, A. W. III., & Marathe, A. R. (2020c). Human-autonomy teaming essential research program project 2: Transparent multimodal crew interface designs. project summary technical report. Technical Report ARL-TR-9002. Aberdeen Proving Ground, MD: CCDC Army Research Laboratory.
Perelman, B. S, Metcalfe, J. S., Boothe, D. L., & McDowell, K. Oversimplifications limit potential for human-AI partnerships. IEEE Access. Manuscript under review.
Polivanova, N. I. (1974). Functional and structural aspects of the visual components of intuition in problem solving. Voprosy Psychologii, 4, 41–51.
Pomplun, M., & Sunkara, S. (2003). Pupil dilation as an indicator of cognitive workload in human-computer interaction. In Proceedings of the international conference on HCI, 2003.
Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006). An empirical study of machine learning techniques for affect recognition in human–robot interaction. Pattern Analysis and Applications, 9(1), 58–69.CrossRef
Ruginski, I. T., Boone, A. P., Padilla, L. M., Liu, L., Heydari, N., Kramer, H. S., & Creem-Regehr, S. H. (2016). Non-expert interpretations of hurricane forecast uncertainty visualizations. Spatial Cognition & Computation, 16(2), 154–172.CrossRef
Salas, E., Stout, R., & Cannon-Bowers, J. (1994). The role of shared mental models in developing shared situational awareness. In: Situational awareness in complex systems pp. 297–304.
Schaefer, K. E., Baker, A. L., Brewer, R. W., Patton, D., Canady, J., & Metcalfe, J. S. (2019). Assessing multi-agent human-autonomy teams: US army robotic wingman gunnery operations. In Proceedings of the SPIE 10982, micro- and nanotechnology sensors, systems, and applications XI, 109822B. https://​doi.​org/​10.​1117/​12.​2519302.
Schulz, C. M., Schneider, E., Frtiz, L., Vockeroth, J., Hapfelmeier, A., Wasmaier, M., Kochs, E. F., & Schneider, G. (2011). Eye tracking for assessment of workload: A pilot study in an anaesthesia simulator environment. British Journal of Anaesthesia, 106(1), 44–50.CrossRef
Sheridan, T. B. (2000). Function Allocation: Algorithm, Alchemy or Apostasy? The International Journal of Human-Computer Studies, 52, 203–216.CrossRef
Shortliffe, E. H., & Buchanan, B. G. (1975). A model of inexact reasoning in medicine. Mathematical Biosciences, 23(3–4), 351–379.MathSciNetCrossRef
Stowers, K., Kasdaglis, N., Newton, O., Lakhmani, S., Wohleber, R., & Chen, J. (2016). Intelligent agent transparency: The design and evaluation of an interface to facilitate human and intelligent agent collaboration. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60(1), 1706–1710. https://​doi.​org/​10.​1177/​1541931213601392​
Takahshi, K., & Watanabe, K. (2015). Effects of image blur on visual perception and affective response. IEEE 978-1-4799-6049.
Telesford, Q. K., Ashourvan, A., Wymbs, N. F., Grafton, S. T., Vettel, J. M., & Bassett, D. S. (2017). Cohesive network reconfiguration accompanies extended training. Human Brain Mapping, 38(9), 4744–4759.CrossRef
Turban, E. (1988). Review of expert systems technology. IEEE Transactions on Engineering Management, 35(2), 71–81.CrossRef
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.CrossRef
van Dijk, H., van de Merwe, K., & Zon, R. (2011). A Coherent impression of the pilots’ situation awareness: studying relevant human factors tools The International Journal of Aviation Psychology, 21(4), 343–356
Van Orden, K. F., Limbert, W., Makeig, S., & Jung, T.-P. (2001). Eye activity correlates of workload during a visuospatial memory task. Human Factors, 43(1), 111–121.CrossRef
Waytowich, Nicholas & G. Goecks, Vinicius & Lawhern, Vernon. (2018). Workshop on Human Robot Interaction.
Zbrodoff, N. J., Logan, G. D. (2005). What everyone finds: The problem-size effect. In J. D. Campbell (Ed.), Handbook of mathematical cognition (pp. 331–346). New York, NY: Psychology Press.
Metadata
Title
Human-Autonomy Teaming for the Tactical Edge: The Importance of Humans in Artificial Intelligence Research and Development
Authors
Kristin E. Schaefer
Brandon Perelman
Joe Rexwinkle
Jonroy Canady
Catherine Neubauer
Nicholas Waytowich
Gabriella Larkin
Katherine Cox
Michael Geuss
Gregory Gremillion
Jason S. Metcalfe
Arwen DeCostanza
Amar Marathe
Copyright Year
2021
Book
Systems Engineering and Artificial Intelligence

Print ISBN: 978-3-030-77282-6

Electronic ISBN: 978-3-030-77283-3

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77283-3_7

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