The Civil Affairs Information Matrix: Designing Context-Aware Visual Analytics Enabling Mission Planning with Ensemble Learning

摘要

With the pivot towards grey zone operations, the United States Army must engage, influence, and partner with local populations in order to achieve the military objectives of their foreign policy. However, the knowledge that enables successful partnerships is split between the Civil Affairs and Intelligence staff elements. Here, we present the Civil Affairs Information Matrix, a context-aware visual analytics capability enabling automated gap analysis for grey zone operations using fused Civil Affairs and Intelligence data. The Civil Affairs Information Matrix uses semi-supervised and unsupervised learning techniques to fuse and annotate data (for example, surveys and news reports), generate potential actions from this data to address gaps and leverage strengths, and visualize the annotated data and rank-ordered potential actions to planners for consideration. Our ensemble approach enables more collaborative planning processes for grey zone operations that will ensure appropriate and dynamic responses to local population interests while achieving the planned outcomes.