A Knowledge Compilation Map for Ordered Real-Valued Decision Diagrams

Hélène Fargier 1 Pierre Marquis 2 Alexandre Niveau 3 Nicolas Schmidt 1, 2
1 ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
IRIT - Institut de recherche en informatique de Toulouse
3 Equipe MAD - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Valued decision diagrams (VDDs) are data structures that represent functions mapping variable-value assignments to non-negative real numbers. They prove useful to compile cost functions, utility functions, or probability distributions. While the complexity of some queries (notably optimization) and transformations (notably conditioning) on VDD languages has been known for some time, there remain many significant queries and transformations, such as the various kinds of cuts, marginalizations, and combinations, the complexity of which has not been identified so far. This paper contributes to filling this gap and completing previous results about the time and space efficiency of VDD languages, thus leading to a knowledge compilation map for real-valued functions. Our results show that many tasks that are hard on valued CSPs are actually tractable on VDDs.
Type de document :
Communication dans un congrès
Twenty-Eighth AAAI Conference on Artificial Intelligence, Dec 2014, Québec, Canada
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https://hal.archives-ouvertes.fr/hal-01095571
Contributeur : Alexandre Niveau <>
Soumis le : lundi 15 décembre 2014 - 18:46:57
Dernière modification le : mardi 5 février 2019 - 12:12:42

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  • HAL Id : hal-01095571, version 1

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Hélène Fargier, Pierre Marquis, Alexandre Niveau, Nicolas Schmidt. A Knowledge Compilation Map for Ordered Real-Valued Decision Diagrams. Twenty-Eighth AAAI Conference on Artificial Intelligence, Dec 2014, Québec, Canada. 〈hal-01095571〉

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