On balanced CSPs with high treewidth

Tractable cases of the binary CSP are mainly divided in two classes: constraint language restrictions and constraint graph restrictions. To better understand and identify the hardest binary CSPs, in this work we propose methods to increase their hardness by increasing the balance of both the constra...

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Detalles Bibliográficos
Autores: Ansótegui Gil, Carlos José, Béjar Torres, Ramón, Fernàndez Camon, César, Mateu Piñol, Carles
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2007
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/46640
Acceso en línea:http://hdl.handle.net/10459.1/46640
Access Level:acceso abierto
Palabra clave:Intel·ligència artificial
CSP (Llenguatge de programació)
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spelling On balanced CSPs with high treewidthAnsótegui Gil, Carlos JoséBéjar Torres, RamónFernàndez Camon, CésarMateu Piñol, CarlesIntel·ligència artificialCSP (Llenguatge de programació)Tractable cases of the binary CSP are mainly divided in two classes: constraint language restrictions and constraint graph restrictions. To better understand and identify the hardest binary CSPs, in this work we propose methods to increase their hardness by increasing the balance of both the constraint language and the constraint graph. The balance of a constraint is increased by maximizing the number of domain elements with the same number of occurrences. The balance of the graph is defined using the classical definition from graph the- ory. In this sense we present two graph models; a first graph model that increases the balance of a graph maximizing the number of vertices with the same degree, and a second one that additionally increases the girth of the graph, because a high girth implies a high treewidth, an important parameter for binary CSPs hardness. Our results show that our more balanced graph models and constraints result in harder instances when compared to typical random binary CSP instances, by several orders of magnitude. Also we detect, at least for sparse constraint graphs, a higher treewidth for our graph models.Association for the Advancement of Artificial Intelligence2007info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10459.1/46640reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésVersió postprint del document publicat a: http://www.aaai.orgProceedings of the twenty-second National Conference on Artificial Intelligence, 2007, p. 161-166(c) Association for the Advancement of Artificial Intelligence, 2007info:eu-repo/semantics/openAccessoai:repositori.udl.cat:10459.1/466402026-06-24T12:42:17Z
dc.title.none.fl_str_mv On balanced CSPs with high treewidth
title On balanced CSPs with high treewidth
spellingShingle On balanced CSPs with high treewidth
Ansótegui Gil, Carlos José
Intel·ligència artificial
CSP (Llenguatge de programació)
title_short On balanced CSPs with high treewidth
title_full On balanced CSPs with high treewidth
title_fullStr On balanced CSPs with high treewidth
title_full_unstemmed On balanced CSPs with high treewidth
title_sort On balanced CSPs with high treewidth
dc.creator.none.fl_str_mv Ansótegui Gil, Carlos José
Béjar Torres, Ramón
Fernàndez Camon, César
Mateu Piñol, Carles
author Ansótegui Gil, Carlos José
author_facet Ansótegui Gil, Carlos José
Béjar Torres, Ramón
Fernàndez Camon, César
Mateu Piñol, Carles
author_role author
author2 Béjar Torres, Ramón
Fernàndez Camon, César
Mateu Piñol, Carles
author2_role author
author
author
dc.subject.none.fl_str_mv Intel·ligència artificial
CSP (Llenguatge de programació)
topic Intel·ligència artificial
CSP (Llenguatge de programació)
description Tractable cases of the binary CSP are mainly divided in two classes: constraint language restrictions and constraint graph restrictions. To better understand and identify the hardest binary CSPs, in this work we propose methods to increase their hardness by increasing the balance of both the constraint language and the constraint graph. The balance of a constraint is increased by maximizing the number of domain elements with the same number of occurrences. The balance of the graph is defined using the classical definition from graph the- ory. In this sense we present two graph models; a first graph model that increases the balance of a graph maximizing the number of vertices with the same degree, and a second one that additionally increases the girth of the graph, because a high girth implies a high treewidth, an important parameter for binary CSPs hardness. Our results show that our more balanced graph models and constraints result in harder instances when compared to typical random binary CSP instances, by several orders of magnitude. Also we detect, at least for sparse constraint graphs, a higher treewidth for our graph models.
publishDate 2007
dc.date.none.fl_str_mv 2007
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10459.1/46640
url http://hdl.handle.net/10459.1/46640
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: http://www.aaai.org
Proceedings of the twenty-second National Conference on Artificial Intelligence, 2007, p. 161-166
dc.rights.none.fl_str_mv (c) Association for the Advancement of Artificial Intelligence, 2007
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Association for the Advancement of Artificial Intelligence, 2007
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Association for the Advancement of Artificial Intelligence
publisher.none.fl_str_mv Association for the Advancement of Artificial Intelligence
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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