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 class room. 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 research will show that the use of static models and companion correlation modules during scenario creation will reduce the complexity of scenario development and reduce the domain knowledge required. Static models can be devised to encapsulate domain knowledge for a particular data source, and correlation can be used to fuse the output from each static model to produce a cohesive scenario. To enable autonomic generation and regeneration of multi-source scenarios, the proposed research will also address service composibility and data heterogeneity among the participating static models and correlation modules. Finally, the proposed research will investigate human-computer interfaces for guiding the scenario developer through the autonomic process.