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27 April 2018 Reexamining computational support for intelligence analysis: a functional design for a future capability
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This basic research project focused on the specification of a top-level functional design of a new mixed-initiative approach providing effective and innovative computational support strategies that efficiently exploit human cognition while minimizing cognitive workload for achieving new intelligence analysis and decision-support capabilities. Toward improving analysis capabilities, we build in part on the similarly-oriented works of researchers that have argumentation methods at the core of their strategies for providing computationally-based support for analysis. However, in our approach, a central theme combines the story- and argumentation-based methods following suggestions in the literature into a hybrid scheme. The argumentation-based foundation provides the advantages of: 1) a basis on simple principles of reasoning, 2) explication of the generalizations and the evidence in the arguments, and 3) allowing the reasoning from the evidence to a conclusion to be easy to follow. In framing our overall functional analysis and decision-support architecture, we also leverage our own research in topic modeling for computational support to narrative development, and in methods for hard and soft data association, fusion, and inferencing. Our approach also takes an Open-World approach and as well addresses the issue of uncertainty in a mathematically rigorous way using a technique called the Transferable Belief Model (TBM). This paper focuses on the highlights of this overall approach; extended details are provided in our citations.
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James Llinas, Galina Rogova, Kevin Barry, and James Scrofani "Reexamining computational support for intelligence analysis: a functional design for a future capability", Proc. SPIE 10653, Next-Generation Analyst VI, 106530T (27 April 2018);


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