Position paper at the invited panel on Issues and Challenges of Knowledge Representation and Reasoning Methods in Situation Assessment (Level 2 Fusion), Proceedings of the 8th International Conference on Information Fusion. Philadelphia, PA (July 2005)
The two largest hurdles for situation assessment (SA, aka. Level 2 fusion) in contemporary urban combat environments are the environmental clutter and the enemy’s lack of conformity to established tactical doctrine. The clutter factor arises from the fact that the environment can be populated with a large number (and hence large intelligence message traffic) and arbitrary mixture of adversarial and neutral entities (e.g. armored vehicles within city traffic). Adversarial entities in the environment must be identified and tracked, individually or as groups, to recognize higher-level situations (e.g. attack, ambush, interdiction, insurgency) and determine effective military responses or preemptive actions. Furthermore, because contemporary enemy behavior is often innovative and unpredictable, traditional tactical models cannot be applied to recognize significant developments in contemporary situations. As a result, an effective automated means for extracting useful situation information from the thousands of multi-source events generated every minute in the theatre of operations remains an open problem. Human analysts currently perform the bulk of this difficult situation and threat assessment work, but are only able to process a small fraction of the available data. In this talk, I argue that neither traditional knowledge discovery nor model-based approaches afford a complete solution for SA requirements. I propose a hybrid approach allowing us to leverage the wealth of data available to provide information about “what is strange” about a given situation, without having to know what exactly it is we are looking for, thus triggering models for follow-up SA.
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