Belief Propagation in Qualitative Probabilistic Networks

Qualitative probabilistic networks (QPNs) [13] are an abstraction of in uence diagrams and Bayesian belief networks replacing numerical relations by qualitative in uences and synergies. To reason in a QPN is to nd the e ect of decision or new evidence on a variable of interest in terms of the sign of the change in belief (increase or decrease). We review our work on qualitative belief propagation, a computationally e cient reasoning scheme based on local sign propagation in QPNs. Qualitative belief propagation, unlike the existing graph-reduction algorithm, preserves the network structure and determines the e ect of evidence on all nodes in the network. We show how this supports meta-level reasoning about the model and automatic generation of intuitive explanations of probabilistic reasoning.