Learning boundaries on military operational plans from simulation data

In this paper we learn indicators from simulated data that serve as boundaries on military operational plans of an expeditionary operation. These are boundaries that an operation must not move beyond without risk of drastic failure. We receive simulated and evaluated partial patterns of plan instances from a simulation-based decision support system that are patterns of integer strings. These partial patterns are clustered by an unsupervised neural Potts spin clustering method into clusters where the instances in each cluster have similar characteristics and outcomes. This gives all partial patterns a classification. We use a Dempster-Shafer theory based factor screening method on each pair of clusters, where all activities of the plan are evaluated as to their differentiating capacity between the two sets of partial plan instances. All plan instances are projected from their full integer string representation to a subset of factors with high differentiating capacity. We apply supervised learning by Support Vector Machine using the previous classification to learn support vectors for each pair of clusters given the projected plan instances of these clusters. From these support vectors we derive a lower dimension hyper plane that will serve as one of the indicators. One indicator from each pair of clusters will make up a full set of indicators for this operational plan. This set of indicators can be provided to the intelligence service and used during execution of the plan for assessment of its progress, and serve as a warning bell if the plan approaches an indicator which it should not proceed beyond.