Uncertainty in Artificial Intelligence
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Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
Chao-Lin Liu, Michael Wellman
Qualitative probabilistic reasoning in a Bayesian network often reveals tradeoffs: relationships that are ambiguous due to competing qualitative influences. We present two techniques that combine qualitative and numeric probabilistic reasoning to resolve such tradeoffs, inferring the qualitative relationship between nodes in a Bayesian network. The first approach incrementally marginalizes nodes that contribute to the ambiguous qualitative relationships. The second approach evaluates approximate Bayesian networks for bounds of probability distributions, and uses these bounds to determinate qualitative relationships in question. This approach is also incremental in that the algorithm refines the state spaces of random variables for tighter bounds until the qualitative relationships are resolved. Both approaches provide systematic methods for tradeoff resolution at potentially lower computational cost than application of purely numeric methods.
Keywords: Qualitative probabilistic networks, approximation.
Pages: 338-345
PS Link: ftp://ftp.eecs.umich.edu/people/wellman/uai98liu-itor.ps
PDF Link: /papers/98/p338-liu.pdf
AUTHOR = "Chao-Lin Liu and Michael Wellman",
TITLE = "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "338--345"

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