MP Wellman

Workshop on Uncertainty in Artificial Intelligence, pages 311–318, 1986.


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.

Revised version published in J. F. Lemmer and L. N. Kanal (eds.), Uncertainty in Artificial Intelligence 2, North Holland, 1988.  Another revised version included in Readings in Uncertain Reasoning (G. Shafer and J. Pearl, eds.), Morgan Kaufmann, 1990.