Reasoning about soft constraints and conditional preferences: complexity results and approximation techniques

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dc.creatorDomshlak, Carmel
dc.creatorRossi, Francesca
dc.creatorVenable, Kristen Brent
dc.creatorWalsh, Toby
dc.date2009-05-22
dc.date.accessioned2026-07-07T13:17:45Z
dc.date.available2026-07-07T13:17:45Z
dc.descriptionMany real life optimization problems contain both hard and soft constraints, as well as qualitative conditional preferences. However, there is no single formalism to specify all three kinds of information. We therefore propose a framework, based on both CP-nets and soft constraints, that handles both hard and soft constraints as well as conditional preferences efficiently and uniformly. We study the complexity of testing the consistency of preference statements, and show how soft constraints can faithfully approximate the semantics of conditional preference statements whilst improving the computational complexity
dc.descriptionProceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03)
dc.identifierhttps://arxiv.org/abs/0905.3766
dc.identifierhttp://arxiv.org/abs/0905.3766
dc.identifierIJCAI 2003: 215-220
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/231217
dc.subjectArtificial Intelligence
dc.subjectI.2.4
dc.titleReasoning about soft constraints and conditional preferences: complexity results and approximation techniques
dc.typetext

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