Qualitative Probabilistic Networks for Planning Under Uncertainty
arXiv:1304.3115
Abstract
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.
Appears in Proceedings of the Second Conference on Uncertainty in Artificial Intelligence (UAI1986)