Refining Reasoning in Qualitative Probabilistic Networks
  In recent years there has been a spate of papers describing systems for
probabilisitic reasoning which do not use numerical probabilities. In some
cases the simple set of values used by these systems make it impossible to
predict how a probability will change or which hypothesis is most likely given
certain evidence. This paper concentrates on such situations, and suggests a
number of ways in which they may be resolved by refining the representation.
