Uncertainty in Artificial Intelligence
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Generating Explanations for Evidential Reasoning
Hong Xu, Philippe Smets
In this paper, we present two methods to provide explanations for reasoning with belief functions in the valuation-based systems. One approach, inspired by Strat's method, is based on sensitivity analysis, but its computation is simpler thus easier to implement than Strat's. The other one is to examine the impact of evidence on the conclusion based on the measure of the information content in the evidence. We show the property of additivity for the pieces of evidence that are conditional independent within the context of the valuation-based systems. We will give an example to show how these approaches are applied in an evidential network.
Keywords: Evidential reasoning, theory of belief functions, sensitivity analysis, the measure
Pages: 574-581
PS Link: ftp://iridia.ulb.ac.be/pub/hongxu/uai95-xu.ps.Z
PDF Link: /papers/95/p574-xu.pdf
AUTHOR = "Hong Xu and Philippe Smets",
TITLE = "Generating Explanations for Evidential Reasoning",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "574--581"

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