Uncertainty in Artificial Intelligence
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Assessing the value of a candidate. Comparing belief function and possibility theories
Didier Dubois, Michel Grabisch, Henri Prade, Philippe Smets
The problem of assessing the value of a candidate is viewed here as a multiple combination problem. On the one hand a candidate can be evaluated according to different criteria, and on the other hand several experts are supposed to assess the value of candidates according to each criterion. Criteria are not equally important, experts are not equally competent or reliable. Moreover levels of satisfaction of criteria, or levels of confidence are only assumed to take their values in qualitative scales which are just linearly ordered. The problem is discussed within two frameworks, the transferable belief model and the qualitative possibility theory. They respectively offer a quantitative and a qualitative setting for handling the problem, providing thus a way to compare the nature of the underlying assumptions.
Keywords: belief function, possibility theory, multiple criteria evaluation, expert opinion fus
Pages: 170-177
PS Link:
PDF Link: /papers/99/p170-dubois.pdf
AUTHOR = "Didier Dubois and Michel Grabisch and Henri Prade and Philippe Smets",
TITLE = "Assessing the value of a candidate. Comparing belief function and possibility theories",
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 = "170--177"

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