PROGNOS: applying probabilistic ontologies to distributed predictive situation assessment in naval operations
Keh‐Chin Chang, Kathryn Blackmond Laskey, Paulo Costa · 2009
Achieving agile Command and Control in Maritime Operations requires composeability the ability to construct responses “on the fly” to queries about a given situation, by discovering and drawing upon the appropriate resources from among the vast collection of resources existing on a distributed network. Although net-centric architectures such as FORCEnet provide the necessary connectivity and computational power needed to achieve fast and adaptive decision cycles, the sheer volume of data creates informational and cognitive bottlenecks that hinder agility. To address these limitations, new approaches bridging the gap from data interchange to knowledge interchange are needed, enabling C2 systems to produce a dynamic, comprehensive, and accurate battlespace picture. This is the main focus of PROGNOS, a system for Predictive Naval Situation Awareness currently being developed at George Mason University’s C4I Center. PROGNOS will integrate four state-of-the-art enabling technologies into a distributed system architecture that represents domain knowledge as a modular collection of probabilistic ontologies, combine these “knowledge nuggets” dynamically into complex situation models, and apply theoretically sound, computationally efficient hypothesis management and inference to combine evidence and background knowledge to reason about the current situation. PROGNOS will also interoperate with other FORCEnet systems by interacting via semantically enabled services.