Innovative approaches to Situation Modeling, Threat Modeling and Threat Prediction

The DF & NN team is composed of Christopher Bowman, Charles Morefield, Alan Steinberg, and Ed Waltz. We will develop methods to model and characterize the quality of data that has been"re-purposed"for fusion applications. We will develop algorithms useful to High-Level Information Fusion (HLF), primarily the areas of situation modeling, threat modeling, and threat prediction. Our algorithms will specifically address bias and uncertainty when data sources include non-numeric qualitative measurements. Our focus is on methods that automatically learn to characterize such re-used/re-purposed data, thereby avoiding expensive off-line manually constructed data and model transformations. The HLF design will apply the Dual Node Network Data Fusion & Resource Management (DF & RM) technical architecture. We propose to cost effectively construct models of bias and error which will over time provide estimates of these errors. Technical objectives are: 1. Scenarios that expose appropriate HLF design and data uncertainty issues to fusion system development. Ontologies will include red/blue force strategies/tactics representing military/political Courses of Action 2. Multi-model approach to L2/3 HLF providing situation modeling, threat modeling, and threat prediction. 3. Develop algorithmic approaches for modeling re-purposed data, especially estimating biases and errors. 4. Deliver a software architectural design for HLF, using the DNN DF & RM technical architecture.