  Artificial intelligence applications such as industrial robotics, military
surveillance, and hazardous environment clean-up, require situation
understanding based on partial, uncertain, and ambiguous or erroneous evidence.
It is necessary to evaluate the relative likelihood of multiple possible
hypotheses of the (current) situation faced by the decision making program.
Often, the evidence and hypotheses are hierarchical in nature. In image
understanding tasks, for example, evidence begins with raw imagery, from which
ambiguous features are extracted which have multiple possible aggregations
providing evidential support for the presence of multiple hypothesis of objects
and terrain, which in turn aggregate in multiple ways to provide partial
evidence for different interpretations of the ambient scene. Information fusion
for military situation understanding has a similar evidence/hypothesis
hierarchy from multiple sensor through message level interpretations, and also
provides evidence at multiple levels of the doctrinal hierarchy of military
forces.
