Abstract
Autonomous tools that can evaluate a course of action (COA) are being developed to assist military leaders. System designers must determine the most effective method of presenting these COAs to operators. To address this challenge, an experimental testbed was developed in which participants were required to achieve the highest score possible in a specific time window by completing mission tasks. For each task, eight possible COAs were presented. Each COA had four parameters—points, time, fuel, and detection. Four experimental visualizations were evaluated, varying in COA number and type: (1) a single COA (most points), (2) four COAs (four highest point values), (3) four COAs (the most points, the least time, the least fuel, and the least chance of detection), and (4) all eight COAs. Both objective and subjective data indicated that the single COA visualization was significantly less effective than the other visualizations. Suggestions are made for follow-on research.
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This work was funded by the Air Force Research Laboratory.
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Behymer, K., Ruff, H., Calhoun, G., Bartik, J., Frost, E. (2019). Presentation of Autonomy-Generated Plans: Determining Ideal Number and Extent Differ. In: Chen, J. (eds) Advances in Human Factors in Robots and Unmanned Systems. AHFE 2018. Advances in Intelligent Systems and Computing, vol 784. Springer, Cham. https://doi.org/10.1007/978-3-319-94346-6_9
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DOI: https://doi.org/10.1007/978-3-319-94346-6_9
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