Uncertainty in Artificial Intelligence
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An Explanation Mechanism for Bayesian Inferencing Systems
Steven Norton
Abstract:
Explanation facilities are a particularly important feature of expert system frameworks. It is an area in which traditional rule-based expert system frameworks have had mixed results. While explanations about control are well handled, facilities are needed for generating better explanations concerning knowledge base content. This paper approaches the explanation problem by examining the effect an event has on a variable of interest within a symmetric Bayesian inferencing system. We argue that any effect measure operating in this context must satisfy certain properties. Such a measure is proposed. It forms the basis for an explanation facility which allows the user of the Generalized Bayesian Inferencing System to question the meaning of the knowledge base. That facility is described in detail.
Keywords: Bayesian Inference Systems, Explanation facilities
Pages: 165-173
PS Link:
PDF Link: /papers/86/p165-norton.pdf
BibTex:
@INPROCEEDINGS{Norton86,
AUTHOR = "Steven Norton ",
TITLE = "An Explanation Mechanism for Bayesian Inferencing Systems",
BOOKTITLE = "Uncertainty in Artificial Intelligence 2 Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1986",
PAGES = "165--173"
}


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