Multi-entity Bayesian networks for situation assessment

Reasoning about military situations requires a scientifically sound and computationally robust uncertainty calculus, a supporting inference engine that procedurally encodes the axioms of the calculus, the capability to fuse information at multiple levels of abstraction, and the ability to respond to dynamic situations. The inference engine also needs to be able to encapsulate expert knowledge, including deep human doctrinal and domain knowledge. At Information Extraction & Transport, Inc. (IET), we have developed techniques to encode domain and doctrinal expertise in reusable knowledge chunks, based on the technology of Bayesian network fragments, and the capability to automatically construct situation specific Bayesian Networks based on a combination of top down control and bottom up evidence-driven processes. These techniques have been used to prototype fusion systems capable of reasoning about uncertain numbers of uncertain hierarchically organized entities based on incomplete observations. These systems have demonstrated success in generating force level situation hypotheses from vehicle tracks and other evidence generated by level 1 fusion systems. This paper presents an overview of our technical approach with applications from recent projects.