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

Dubois, D; Fusco, G; Prade, H; Tettamanzi, A

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

Dubois, D Fusco, G Prade, H

Tettamanzi, A

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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:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Dubois, D	en_US
dc.contributor.author	Fusco, G	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.contributor.author	Tettamanzi, A	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9310 pp. 249 - 263	en_US
dc.identifier.isbn	9783319235394	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120206
dc.description.abstract	© 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.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-23540-0_17	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Uncertain logical gates in possibilistic networks. An application to human geography	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9310	en_US
utslib.for	0804 Data Format	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	9310	en_US

Abstract:

© 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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http://hdl.handle.net/10453/120206