Modeling positive and negative information in possibility theory

Benferhat, S; Dubois, D; Kaci, S; Prade, H

Modeling positive and negative information in possibility theory

Benferhat, S Dubois, D Kaci, S Prade, H

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Publication Type:: Journal Article
Citation:: International Journal of Intelligent Systems, 2008, 23 (10), pp. 1094 - 1118
Issue Date:: 2008-10-01

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Field	Value	Language
dc.contributor.author	Benferhat, S	en_US
dc.contributor.author	Dubois, D	en_US
dc.contributor.author	Kaci, S	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.date.issued	2008-10-01	en_US
dc.identifier.citation	International Journal of Intelligent Systems, 2008, 23 (10), pp. 1094 - 1118	en_US
dc.identifier.issn	0884-8173	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32518
dc.description.abstract	From a knowledge representation point of view, it may be interesting to distinguish between (i) what is potentially possible because it is not inconsistent with the available knowledge on the one hand, and (ii) what is actually possible because it is reported from observations on the other hand. Such a distinction also makes sense when expressing preferences, to point out positively desired choices among merely tolerated ones. Possibility theory provides a representation framework where this distinction can be made in a graded way. The two types of information can be encoded by two types of constraints expressed in terms of necessity measures and in terms of so-called guaranteed possibility functions. These two set-functions are min-decomposable with respect to conjunction and disjunction, respectively. This gives birth to two forms of possibilistic logic bases, where clauses (resp., phrases) are weighted in terms of a necessity measure (resp., a guaranteed possibility function). By application of a minimal commitment principle, the two bases induce a pair of possibility distributions at the semantic level, for which a consistency condition should hold to ensure that what is claimed to be actually possible is indeed not impossible. The paper provides a survey of this bipolar representation framework, including the use of conditional measures, or the handling of comparative context-dependent constraints. The interest of the framework is stressed for expressing preferences, as well as in the representation of "if-then" rules in terms of examples and counterexamples. © 2008 Wiley Periodicals, Inc.	en_US
dc.relation.ispartof	International Journal of Intelligent Systems	en_US
dc.relation.isbasedon	10.1002/int.20308	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Modeling positive and negative information in possibility theory	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	23	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	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.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

From a knowledge representation point of view, it may be interesting to distinguish between (i) what is potentially possible because it is not inconsistent with the available knowledge on the one hand, and (ii) what is actually possible because it is reported from observations on the other hand. Such a distinction also makes sense when expressing preferences, to point out positively desired choices among merely tolerated ones. Possibility theory provides a representation framework where this distinction can be made in a graded way. The two types of information can be encoded by two types of constraints expressed in terms of necessity measures and in terms of so-called guaranteed possibility functions. These two set-functions are min-decomposable with respect to conjunction and disjunction, respectively. This gives birth to two forms of possibilistic logic bases, where clauses (resp., phrases) are weighted in terms of a necessity measure (resp., a guaranteed possibility function). By application of a minimal commitment principle, the two bases induce a pair of possibility distributions at the semantic level, for which a consistency condition should hold to ensure that what is claimed to be actually possible is indeed not impossible. The paper provides a survey of this bipolar representation framework, including the use of conditional measures, or the handling of comparative context-dependent constraints. The interest of the framework is stressed for expressing preferences, as well as in the representation of "if-then" rules in terms of examples and counterexamples. © 2008 Wiley Periodicals, Inc.

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http://hdl.handle.net/10453/32518