An "interpretation system" finds the likely mappings from portions of an image
to real-world objects. An "interpretation policy" specifies when
to apply which imaging operator, to which portion of the image,
during every stage of interpretation.
Earlier results compared a number of policies,and demonstrated that policies
that select operators which maximize the information gain per cost,
worked most effectively. However, those policies are "myopic" --- 
they rank the operators based only on their "immediate" rewards.
This can lead to inferior overall results: it may be better
to use a relatively expensive operator first,
if that operator provides information that 
will significantly reduce the cost of the subsequent operators.