Multi-Dimensional Data Farming: Extending Data Farming for Multi-Scale Decision Support by Integrating Novel AI Techniques

Traditional Data Farming (DF) consists of a toolbox of established analysis techniques that are available for an analyst-led study of a particular military operation in support of a single decision-maker. Multi-Dimensional Data Farming (MDDF) is a new and automated analytical process that provides accelerated support to military decision-making at multiple scales. At the strategic level, MDDF can inform decision-makers in planning long timescale campaigns, while at the tactical level, MDDF allows investigation of emerging technologies in shorter timescale operations. More importantly, MDDF explicitly addresses the interplay between a long timescale campaign and embedded short timescale operations, which is rarely tackled in the literature. MDDF extends DF by integrating novel AI techniques (Automated Machine Learning, eXplainable AI) and eXtended Reality visualization in an AI agent which automatically investigates the multi-dimensional parameter landscape and efficiently provides decision-makers with insight into the best, worst and most promising Courses of Action. We illustrate our new MDDF approach through a hybrid warfare scenario consisting of a Border Operation (interdiction of illegal migrants) embedded within a multi-faction (Blue, Red and Green forces) hybrid war campaign. Combining AI techniques exploring operations at multiple scales (domain, level, time) and boosting strategic and tactical understanding, MDDF innovates multi-scale decision-making.