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Importance-based semantics of polynomial comparative preference inference

  • Nic Wilson

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

A basic task in preference reasoning is inferring a preference between a pair of outcomes (alternatives) from an input set of preference statements. This preference inference task for comparative preferences has been shown to be computationally very hard for the standard kind of inference. Recently, a new kind of preference inference has been developed, which is polynomial for relatively expressive preference languages, and has the additional property of being much less conservative; this can be a major advantage, since it will tend to make the number of undominated outcomes smaller. It derives from a semantics where models are weak orders that are generated by objects called cp-trees, which represent a kind of conditional lexicographic order. We show that there are simple conditions, based on the notion of importance, that determine whether a weak order can be generated by a cp-tree of the given form. This enables a simple characterisation of the less conservative preference inference. We go on to study the importance properties satisfied by a simple kind of cp-tree, leading to another characterisation of the corresponding preference inference.

Original languageEnglish
Title of host publicationECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration
PublisherIOS Press BV
Pages852-857
Number of pages6
ISBN (Print)9781614990970
DOIs
Publication statusPublished - 2012
Event20th European Conference on Artificial Intelligence, ECAI 2012 - Montpellier, France
Duration: 27 Aug 201231 Aug 2012

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume242
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference20th European Conference on Artificial Intelligence, ECAI 2012
Country/TerritoryFrance
CityMontpellier
Period27/08/1231/08/12

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