Efficient Reasoning in Qualitative Probabilistic Networks

Marek J. Druzdzel, Max Henrion

Qualitative Probabilistic Networks (QPNs) are an abstraction of Bayesian belief networks replacing numerical relations by qualitative influences and synergies [Wellman, 1990b]. To reason in a QPN is to find the effect of new evidence on each node in terms of the sign of the change in belief (increase or decrease). We introduce a polynomial time algorithm for reasoning in QPNs, based on local sign propagation. It extends our previous scheme from singly connected to general multiply con- nected networks. Unlike existing graph-reduction algorithms, it preserves the network structure and determines the effect of evidence on all nodes in the network. This aids meta-level reasoning about the model and automatic generation of intuitive explanations of probabilistic reasoning.

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