Efficient reasoning in qualitative probabilistic networks

Authors:
Marek J. Druzdzel;Max Henrion
Affiliations:
Carnegie Mellon University, Department of Engineering, Pittsburgh, PA;Rockwell International Science Center, Palo Alto Laboratory, Palo Alto, CA
Venue:
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Year:
1993

Citing 9
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Abstract

Qualitative Probabilistic Networks (QPNs) are. an abstraction of Bayesian belief networks replacmg 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 connected 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.