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Human-Agent Collaborative Decision-Making Framework for Naval Systems

  • Maria Olinda Rodas
  • Jeff Waters
  • Cheryl Putnam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10910)

Abstract

This work provides an overview of a future human-agent collaborative decision-making framework to be developed for naval systems using an augmented reality platform. We present the basic concept behind the framework, key features of the application, and some details about a future proof of concept prototype that will demonstrate and evaluate the concept against a baseline design.

Keywords

Military Virtual reality Mixed reality Augmented reality Decision optimization Provenance Data visualization Design C2 Innovation 

References

  1. 1.
    Barnes, M.J., Evans, A.W.: Soldier-robot teams in future battlefields: an overview. In: Barnes, M., Jentsch, F. (eds.) Human-Robot Interactions in Future Military Operations, pp. 9–29. Ashgate, Farnham (2010)Google Scholar
  2. 2.
    Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and level of human interaction and automation. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 30(3), 286–297 (2000)CrossRefGoogle Scholar
  3. 3.
    Office of the Under Secretary of Defense for Acquisition, Technology and Logistics. Washington, DC. 20301-3140. Department of Defense: Defense Science Board. Task Force Report: The Role of Autonomy in DoD Systems, July 2012. https://fas.org/irp/agency/dod/dsb/autonomy.pdf. Accessed 12 Jan 2018
  4. 4.
    Parasuraman, R., Riley, V.: Humans and automation: use, misuse, disuse, abuse. Hum. Factors J. 39(2), 230–253 (1997)CrossRefGoogle Scholar
  5. 5.
    Hancock, P.A., Billing, D.R., Shaefer, K.E., Chen, Y.C., DeVisser, E.J., Parasuraman, R.: A meta-analysis of factors affecting trust in human-robot interaction. Hum. Factors J. 53(5), 517–527 (2016)CrossRefGoogle Scholar
  6. 6.
    Hoff, K.A., Bashir, M.: Trust in automation: integrating empirical evidence on factors that influence trust. Hum. Factors J. 57(3), 407–434 (2015)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Wagenhals, L.W., Levis, A.H.: Course of action development and evaluation. In: DTIC, Fairfax (2000)Google Scholar
  9. 9.
    Lee, J.D.: Trust, trustworthiness, and trustability. In: The Workshop on Human Machine Trust for Robust Autonomous Systems, Ocala, Florida (2012)Google Scholar
  10. 10.
    Chen, J.Y.C., Barnes, M.J.: Human–agent teaming for multirobot control: a review of human factors issues. IEEE Trans. Hum.-Mach. Syst. 44(1), 13–29 (2014)CrossRefGoogle Scholar
  11. 11.
    Mercado, J.E., Rupp, M.A., Barnes, M.J., Barber, D., Procci, K.: Intelligent agent transparency in human-agent teaming for multi-UxV management. Hum. Factors J. 58(3), 401–415 (2016)CrossRefGoogle Scholar
  12. 12.
    Wang, N., Pynadath, D.V., Hill, S.G., Merchant, C.: The dynamics of human-agent trust with POMDP-generated explanations. Intelligent Virtual Agents. LNCS (LNAI), vol. 10498, pp. 459–462. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67401-8_58CrossRefGoogle Scholar
  13. 13.
    Wang, N., Pynadath, D.V., Hill, S.G.: The impact of POMDP-generated explanations on trust and performance in human-robot teams. In: Proceeding of the 15th International Conference on Autonomous Agents and Multiagent Systems, Singapore, pp. 997–1005 (2016)Google Scholar
  14. 14.
    Lim, K., Suresh, P., Schulze, J.P.: Oculus rift with stereo camera for augmented reality medical intubation training. In: Proceedings of IS&T the Engineering Reality of Virtual Reality, San Francisco, CA, 1–2 February 2017Google Scholar
  15. 15.
    Wiley, B., Schulze, J.P.: archAR: an archaeological augmented reality experience. In: Proceedings of IS&T/SPIE Electronic Imaging, the Engineering Reality of Virtual Reality, San Francisco, CA, 9–10 February 2015Google Scholar
  16. 16.
    McCarthy, D., Schulze, J.P.: Distributed VR rendering using NVIDIA OptiX. In: Proceedings of IS&T the Engineering Reality of Virtual Reality, San Francisco, CA, 1–2 February 2017Google Scholar
  17. 17.
    Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors: J. Hum. Factors Ergon. Soc. 37(1), 32–64 (1995)CrossRefGoogle Scholar
  18. 18.
    Jeff, W., Bruce, P., Pilcher, J., Odland, A., Jones, D.: A dynamic agile process model for situational awareness: a machine-understandable, fractal-based, data-driven approach. Presented at the Cognitive Methods in Situational Awareness and Decision Support (CogSIMA) Conference, San Diego, California (2016)Google Scholar
  19. 19.
    Langloz, C.: Fundamental measures of diagnostic examination performance: usefulness for clinical decision making and research. Radiol. J. 228(1), 3–9 (2003)CrossRefGoogle Scholar
  20. 20.
    Putman, C., Waters, J., Rodas, O.: A standard decision format using provenance. In: Proceedings of IEEE International Symposium on Signal Processing and Information Technology, Bilbao, Spain (2017)Google Scholar

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  • Maria Olinda Rodas
    • 1
  • Jeff Waters
    • 1
  • Cheryl Putnam
    • 1
  1. 1.Space and Naval Warfare Systems Center PacificSan DiegoUSA

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