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dc.contributor.authorRollón Rico, Emma
dc.contributor.authorLarrosa Bondia, Francisco Javier
dc.contributor.authorDechter, Rina
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2014-07-01T08:27:28Z
dc.date.created2013
dc.date.issued2013
dc.identifier.citationRollón, E.; Larrosa, J.; Dechter, R. Semiring-based mini-bucket partitioning schemes. A: International Joint Conference on Artificial Intelligence. "23rd International Joint Conference on Artificial Intelligence". Beijing: AAAI Press. Association for the Advancement of Artificial Intelligence, 2013, p. 644-650.
dc.identifier.isbn978-1-57735-633-2
dc.identifier.urihttp://hdl.handle.net/2117/23343
dc.description.abstractGraphical models are one of the most prominent frameworks to model complex systems and efficiently query them. Their underlying algebraic properties are captured by a valuation structure that, most usually, is a semiring. Depending on the semiring of choice, we can capture probabilistic models, constraint networks, cost networks, etc. In this paper we address the partitioning problem which occurs in many approximation techniques such as mini-bucket elimination and join-graph propagation algorithms. Roghly speaking, subject to complexity bounds, the algorithm needs to find a partition of a set of factors such that best approximates the whole set. While this problem has been addressed in the past in a particular case, we present here a general description. Furthermore, we also propose a general partitioning scheme. Our proposal is general in the sense that it is presented in terms of a generic semiring with the only additional requirements of a division operation and a refinement of its order. The proposed algorithm instantiates to the particular task of computing the probability of evidence, but also applies directly to other important reasoning tasks. We demonstrate its good empirical behaviour on the problem of computing the most probable explanation.
dc.format.extent7 p.
dc.language.isoeng
dc.publisherAAAI Press. Association for the Advancement of Artificial Intelligence
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshApproximation algorithms
dc.titleSemiring-based mini-bucket partitioning schemes
dc.typeConference report
dc.subject.lemacAlgorismes computacionals
dc.contributor.groupUniversitat Politècnica de Catalunya. LOGPROG - Lògica i Programació
dc.relation.publisherversionhttp://dl.acm.org/citation.cfm?id=2540222
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac13403642
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorRollón, E.; Larrosa, J.; Dechter, R.
local.citation.contributorInternational Joint Conference on Artificial Intelligence
local.citation.pubplaceBeijing
local.citation.publicationName23rd International Joint Conference on Artificial Intelligence
local.citation.startingPage644
local.citation.endingPage650


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