Reasoning about uncertainty and explicit ignorance in generalized possibilistic logic

Dubois, D; Prade, H; Schockaert, S

Reasoning about uncertainty and explicit ignorance in generalized possibilistic logic

Dubois, D Prade, H

Schockaert, S

Permalink

Publication Type:: Conference Proceeding
Citation:: Frontiers in Artificial Intelligence and Applications, 2014, 263 pp. 261 - 266
Issue Date:: 2014-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (234.74 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Dubois, D	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.contributor.author	Schockaert, S	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Frontiers in Artificial Intelligence and Applications, 2014, 263 pp. 261 - 266	en_US
dc.identifier.isbn	9781614994183	en_US
dc.identifier.issn	0922-6389	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119613
dc.description.abstract	© 2014 The Authors and IOS Press. Generalized possibilistic logic (GPL) is a logic for reasoning about the revealed beliefs of another agent. It is a two-tier propositional logic, in which propositional formulas are encapsulated by modal operators that are interpreted in terms of uncertainty measures from possibility theory. Models of a GPL theory represent weighted epistemic states and are encoded as possibility distributions. One of the main features of GPL is that it allows us to explicitly reason about the ignorance of another agent. In this paper, we study two types of approaches for reasoning about ignorance in GPL, based on the idea of minimal specificity and on the notion of guaranteed possibility, respectively. We show how these approaches naturally lead to different flavours of the language of GPL and a number of decision problems, whose complexity ranges from the first to the third level of the polynomial hierarchy.	en_US
dc.relation.ispartof	Frontiers in Artificial Intelligence and Applications	en_US
dc.relation.isbasedon	10.3233/978-1-61499-419-0-261	en_US
dc.title	Reasoning about uncertainty and explicit ignorance in generalized possibilistic logic	en_US
dc.type	Conference Proceeding
utslib.citation.volume	263	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	open_access
pubs.publication-status	Published	en_US
pubs.volume	263	en_US

Abstract:

© 2014 The Authors and IOS Press. Generalized possibilistic logic (GPL) is a logic for reasoning about the revealed beliefs of another agent. It is a two-tier propositional logic, in which propositional formulas are encapsulated by modal operators that are interpreted in terms of uncertainty measures from possibility theory. Models of a GPL theory represent weighted epistemic states and are encoded as possibility distributions. One of the main features of GPL is that it allows us to explicitly reason about the ignorance of another agent. In this paper, we study two types of approaches for reasoning about ignorance in GPL, based on the idea of minimal specificity and on the notion of guaranteed possibility, respectively. We show how these approaches naturally lead to different flavours of the language of GPL and a number of decision problems, whose complexity ranges from the first to the third level of the polynomial hierarchy.

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

http://hdl.handle.net/10453/119613