Answer set programming for computing decisions under uncertainty

Confalonieri, R; Prade, H

Answer set programming for computing decisions under uncertainty

Confalonieri, R Prade, H

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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), 2011, 6717 LNAI pp. 485 - 496
Issue Date:: 2011-07-14

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Field	Value	Language
dc.contributor.author	Confalonieri, R	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.date.issued	2011-07-14	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 6717 LNAI pp. 485 - 496	en_US
dc.identifier.isbn	9783642221514	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32833
dc.description.abstract	Possibility theory offers a qualitative framework for modeling decision under uncertainty. In this setting, pessimistic and optimistic decision criteria have been formally justified. The computation by means of possibilistic logic inference of optimal decisions according to such criteria has been proposed. This paper presents an Answer Set Programming (ASP)-based methodology for modeling decision problems and computing optimal decisions in the sense of the possibilistic criteria. This is achieved by applying both a classic and a possibilistic ASP-based methodology in order to handle both a knowledge base pervaded with uncertainty and a prioritized preference base. © 2011 Springer-Verlag Berlin Heidelberg.	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	10.1007/978-3-642-22152-1_41	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Answer set programming for computing decisions under uncertainty	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6717 LNAI	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.publication-status	Published	en_US
pubs.volume	6717 LNAI	en_US

Abstract:

Possibility theory offers a qualitative framework for modeling decision under uncertainty. In this setting, pessimistic and optimistic decision criteria have been formally justified. The computation by means of possibilistic logic inference of optimal decisions according to such criteria has been proposed. This paper presents an Answer Set Programming (ASP)-based methodology for modeling decision problems and computing optimal decisions in the sense of the possibilistic criteria. This is achieved by applying both a classic and a possibilistic ASP-based methodology in order to handle both a knowledge base pervaded with uncertainty and a prioritized preference base. © 2011 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/32833