Uncertainty in Artificial Intelligence
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Practical Uses of Belief Functions
Philippe Smets
Abstract:
We present examples where the use of belief functions provided sound and elegant solutions to real life problems. These are essentially characterized by ‘missing' information. The examples deal with 1) discriminant analysis using a learning set where classes are only partially known; 2) an information retrieval systems handling inter-documents relationships; 3) the combination of data from sensors competent on partially overlapping frames; 4) the determination of the number of sources in a multi-sensor environment by studying the inter-sensors contradiction. The purpose of the paper is to report on such applications where the use of belief functions provides a convenient tool to handle ‘messy' data problems.
Keywords: belief function, hints model, transferable belief model
Pages: 612-621
PS Link: http://iridia.ulb.ac.be/~psmets
PDF Link: /papers/99/p612-smets.pdf
BibTex:
@INPROCEEDINGS{Smets99,
AUTHOR = "Philippe Smets ",
TITLE = "Practical Uses of Belief Functions",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "612--621"
}


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