Uncertainty in Artificial Intelligence
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Qualitative Probabilistic Networks for Planning Under Uncertainty
Michael Wellman
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.
Keywords: Bayesian Networks, Qualitative Probabilistic Networks
Pages: 197-208
PS Link:
PDF Link: /papers/86/p197-wellman.pdf
BibTex:
@INPROCEEDINGS{Wellman86,
AUTHOR = "Michael Wellman ",
TITLE = "Qualitative Probabilistic Networks for Planning Under Uncertainty",
BOOKTITLE = "Uncertainty in Artificial Intelligence 2 Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1986",
PAGES = "197--208"
}


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