Uncertainty in Artificial Intelligence
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Approximations for Decision Making in the Dempster-Shafer Theory of Evidence
Mathias Bauer
Abstract:
The computational complexity of reasoning within the Dempster-Shafer theory of evidence is one of the main points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief functions involved. Besides introducing a new algorithm using this method, this paper describes an empirical study that examines the appropriateness of these approximation procedures in decision making situations. It presents the empirical findings and discusses the various tradeoffs that have to be taken into account when actually applying one of these methods.
Keywords: Dempster-Shafer Theory, decision making, approximation algorithms.
Pages: 73-80
PS Link: http://www.dfki.uni-sb.de/~bauer/publications/uai96.ps.gz
PDF Link: /papers/96/p73-bauer.pdf
BibTex:
@INPROCEEDINGS{Bauer96,
AUTHOR = "Mathias Bauer ",
TITLE = "Approximations for Decision Making in the Dempster-Shafer Theory of Evidence",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "73--80"
}


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