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An introduction to reasoning over qualitative multi-attribute preferences

Published online by Cambridge University Press:  03 March 2015

Ingrid Nunes ,
Simon Miles ,
Michael Luck  and
Carlos J. P. Lucena
Ingrid Nunes
Affiliation:
Instituto de Informática, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, 91501-970, Brazil e-mail: ingridnunes@inf.ufrgs.br
Simon Miles
Affiliation:
Department of Informatics, King’s College London, London, WC2R 2LS, UK e-mail: simon.miles@kcl.ac.uk, michael.luck@kcl.ac.uk
Michael Luck
Affiliation:
Department of Informatics, King’s College London, London, WC2R 2LS, UK e-mail: simon.miles@kcl.ac.uk, michael.luck@kcl.ac.uk
Carlos J. P. Lucena
Affiliation:
Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, 22451-900, Brazil e-mail: lucena@inf.puc-rio.br
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Abstract

Research on preferences has significantly increased in recent years, as it involves not only many subproblems to be investigated, such as elicitation, representation, and reasoning, but has also been the target of different research areas, for example, artificial intelligence and databases. In particular, much work has focused on qualitative preferences, because these are closer to the way people express their preferences in comparison with quantitative preferences. Against this background, a large number of approaches have been proposed, associated with heterogeneous areas, so that these approaches are usually just compared with those of the same area. In response, we present in this paper a survey of approaches to qualitative multi-attribute preference reasoning, covering different research areas. We introduce selected approaches that propose different techniques and algorithms, which take as input qualitative multi-attribute preference statements following a particular structure specified by the approach. We analyse each approach in a systematic way and discuss their commonalities and limitations.

Type
Articles
Information
The Knowledge Engineering Review , Volume 30 , Issue 3 , May 2015 , pp. 342 - 372
Copyright
© Cambridge University Press, 2015 

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