Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
  Qualitative probabilistic reasoning in a Bayesian network often reveals
tradeoffs: relationships that are ambiguous due to competing qualitative
influences. We present two techniques that combine qualitative and numeric
probabilistic reasoning to resolve such tradeoffs, inferring the qualitative
relationship between nodes in a Bayesian network. The first approach
incrementally marginalizes nodes that contribute to the ambiguous qualitative
relationships. The second approach evaluates approximate Bayesian networks for
bounds of probability distributions, and uses these bounds to determinate
qualitative relationships in question. This approach is also incremental in
that the algorithm refines the state spaces of random variables for tighter
bounds until the qualitative relationships are resolved. Both approaches
provide systematic methods for tradeoff resolution at potentially lower
computational cost than application of purely numeric methods.
