Handling uncertainty and defeasibility in a possibilistic logic setting

Dupin de Saint-Cyr, F; Prade, H

Handling uncertainty and defeasibility in a possibilistic logic setting

Dupin de Saint-Cyr, F Prade, H

Permalink

Publication Type:: Journal Article
Citation:: International Journal of Approximate Reasoning, 2008, 49 (1), pp. 67 - 82
Issue Date:: 2008-09-01

Closed Access

	Filename	Description	Size
	2013007912OK.pdf	Published Version	235.47 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Dupin de Saint-Cyr, F	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.date.issued	2008-09-01	en_US
dc.identifier.citation	International Journal of Approximate Reasoning, 2008, 49 (1), pp. 67 - 82	en_US
dc.identifier.issn	0888-613X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32565
dc.description.abstract	Default rules express concise pieces of knowledge having implicit exceptions, which is appropriate for reasoning under incomplete information. Specific rules that explicitly refer to exceptions of more general default rules can then be handled in a non-monotonic setting. However, there is no assessment of the certainty with which the conclusion of a default rule holds when it applies. We propose a formalism in which uncertain default rules can be expressed, but still preserving the distinction between the defeasibility and uncertainty semantics by means of a two steps processing. Possibility theory is used for representing both uncertainty and defeasibility. The approach is illustrated in persistence modeling problems. © 2007 Elsevier Inc. All rights reserved.	en_US
dc.relation.ispartof	International Journal of Approximate Reasoning	en_US
dc.relation.isbasedon	10.1016/j.ijar.2007.08.001	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Handling uncertainty and defeasibility in a possibilistic logic setting	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	49	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	49	en_US

Abstract:

Default rules express concise pieces of knowledge having implicit exceptions, which is appropriate for reasoning under incomplete information. Specific rules that explicitly refer to exceptions of more general default rules can then be handled in a non-monotonic setting. However, there is no assessment of the certainty with which the conclusion of a default rule holds when it applies. We propose a formalism in which uncertain default rules can be expressed, but still preserving the distinction between the defeasibility and uncertainty semantics by means of a two steps processing. Possibility theory is used for representing both uncertainty and defeasibility. The approach is illustrated in persistence modeling problems. © 2007 Elsevier Inc. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/32565