Qualitative Probabilistic Networks for Planning Under Uncertainty
  Bayesian networks provide a probabilistic semantics for qualitative
assertions about likelihood. A qualitative reasoner based on an algebra over
these assertions can derive further conclusions about the influence of actions.
While the conclusions are much weaker than those computed from complete
probability distributions, they are still valuable for suggesting potential
actions, eliminating obviously inferior plans, identifying important tradeoffs,
and explaining probabilistic models.
