Uncertain logical gates in possibilistic networks. An application to human geography

Publisher:
SpringerLink
Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9310 pp. 249 - 263
Issue Date:
2015-09-16
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© Springer International Publishing Switzerland 2015. 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. This paper presents the possibilistic counterparts of the noisy probabilistic connectives (and, or, max, min,...). Their interest is illustrated on an example taken from a human geography modeling problem. The difference of behaviors in some cases of some possibilistic connectives, with respect to their probabilistic analogs, is discussed in details.
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