Representations of probablistic situations

Determining a decision from data is an important DoD research area with far-reaching applications. In particular, the long-elusive goal of autonomous machines discovering the relations between entities within a situation has proved to be extremely dicult. Many current sensing systems are devoted to fusing information from a variety of heterogeneous sensors in order to characterize the entities and relationships in the data. This leads to the need for representations of relationships and situations which can model the uncertainty that is present in any system. We develop mathematics for representing a situation where the relations are uncertain and use the work of Meng to show how to compare probabilistic relations and situations.