Uncertainty in Artificial Intelligence
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Hierarchical Evidence and Belief Functions
Paul Black, Kathryn Laskey
Abstract:
Dempster/Shafer (D/S) theory has been advocated as a way of representing incompleteness of evidence in a system's knowledge base. Methods now exist for propagating beliefs through chains of inference. This paper discusses how rules with attached beliefs, a common representation for knowledge in automated reasoning systems, can be transformed into the joint belief functions required by propagation algorithms. A rule is taken as defining a conditional belief function on the consequent given the antecedents. It is demonstrated by example that different joint belief functions may be consistent with a given set of rules. Moreover, different representations of the same rules may yield different beliefs on the consequent hypotheses.
Keywords: null
Pages: 207-215
PS Link:
PDF Link: /papers/88/p207-black.pdf
BibTex:
@INPROCEEDINGS{Black88,
AUTHOR = "Paul Black and Kathryn Laskey",
TITLE = "Hierarchical Evidence and Belief Functions",
BOOKTITLE = "Uncertainty in Artificial Intelligence 4 Annual Conference on Uncertainty in Artificial Intelligence (UAI-88)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1988",
PAGES = "207--215"
}


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