Adaptive Fleet Synthetic Scenario Research

Synthetic scenario-based training of Navy personnel in the use of Navy SIGINT/IO systems has helped to reduce training costs, and it has enabled the personnel to be trained in an environment that sufficiently approximates real-world situations that could not otherwise be accomplished within the classroom. However, scenario development is highly complex and involves a great deal of human effort and domain knowledge, discouraging the modification of existing scenarios to keep them current in an ever-changing threat environment. This problem is exacerbated when the scenario represents a combination of multiple data sources. The proposed Phase II effort will leverage the positive results from the Phase I research to develop a fieldable Scenario Generator able to output Stallion-ready training scenarios. The Scenario Generator will make use of data-driven static models developed during Phase I, which significantly reduced scenario creation time and reduced the domain knowledge required. The Phase I research showed that domain knowledge, encapsulated within selected data source, could be used to drive static models, and that those static models could be orchestrated such that their output produces a cohesive, multiple-Intelligence (Multi-INT) scenario.