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Uncertain logical gates in possibilistic networks: theory and application to human geography. (English) Zbl 1404.68160
Summary: Possibilistic networks offer a qualitative approach for modeling epistemic uncertainty. Their practical implementation requires the specification of conditional possibility tables, as in the case of Bayesian networks for probabilities. The elicitation of probability tables by experts is made much easier by means of noisy logical gates that enable multidimensional tables to be constructed from the knowledge of a few parameters. This paper presents the possibilistic counterparts of usual noisy connectives (and, or, \(\max\), \(\min\), \(\ldots\)). Their interest and limitations are illustrated on an example taken from a human geography modeling problem. The difference of behavior between probabilistic and possibilistic connectives is discussed in detail. Results in this paper may be useful to bring possibilistic networks closer to applications.
MSC:
68T37 Reasoning under uncertainty in the context of artificial intelligence
91D20 Mathematical geography and demography
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