Abstract
Current Army operations rely on decision making processes that are driven by data and the knowledge and expertise of the commanders, analysts, and other decision makers. These decision makers are increasingly dealing with information that is incomplete or of uncertain reliability. The large volume of data along with the wide diversity of information sources can contribute to this uncertainty. Defining computational models of uncertainty of information (UoI) will enable the creation of automated tools that assist the decision maker by ranking relevant information, identifying emergent decision points, and recommend potential alternate courses of action. DEVCOM ARL researchers conducted an experimental user study to explore how soldiers might prioritize these models in the taxonomy given two scenarios. This paper analyzes these results and highlights patterns that can further aid how intelligent systems can support decision making ensuring that the decision maker maintains an understanding of uncertainties associated with the information.
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Acknowledgments
Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-19–2-0062. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
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Raglin, A., Emlet, A., Caylor, J., Richardson, J., Mittrick, M., Metu, S. (2022). Uncertainty of Information (UoI) Taxonomy Assessment Based on Experimental User Study Results. In: Kurosu, M. (eds) Human-Computer Interaction. Theoretical Approaches and Design Methods. HCII 2022. Lecture Notes in Computer Science, vol 13302. Springer, Cham. https://doi.org/10.1007/978-3-031-05311-5_20
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DOI: https://doi.org/10.1007/978-3-031-05311-5_20
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