Soft information, dirty graphs and uncertainty representation/processing for situation understanding

In conventional warfare as well as counter-insurgency (COIN) operations, the understanding of the situation is extremely vital to assure a sense of security. Intelligence in COIN is about people, and deployed units in the field are the best sources of intelligence. Past and present intelligence data is analyzed to look for changes in the insurgents' approach or tactics. To do this, graphical methods have proven to be effective. In recent work, have developed an inexact subgraph matching algorithm as a variation of the subgraph isomorphism approach for situation assessment. This paper enhances this procedure to represent inaccurate observations or data estimates, and inaccurate structural representations of a state of interest, thus accounting for the uncertainties. Various probabilistic and possibilistic uncertainty representations, transformations between representations and methods for establishing similarities between representations have been reviewed. This comprehensible approach will give pragmatic estimates providing rigor and sound understanding during situation assessment.