The impact of balancing on problem hardness in a highly structured domain

Random problem distributions have played a key role in the study and design of algorithms for constraint satisfaction and Boolean satisfiability, as well as in ourunderstanding of problem hardness, beyond standard worst-case complexity. We consider random problem distributions from a highly structur...

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Detalhes bibliográficos
Autores: Ansótegui Gil, Carlos José, Béjar Torres, Ramón, Fernàndez Camon, César, Gomes, Carla, Mateu Piñol, Carles
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2006
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/46638
Acesso em linha:http://hdl.handle.net/10459.1/46638
Access Level:acceso abierto
Palavra-chave:Tecnologia Innovacions
Intel·ligència artificial
Sudoku
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spelling The impact of balancing on problem hardness in a highly structured domainAnsótegui Gil, Carlos JoséBéjar Torres, RamónFernàndez Camon, CésarGomes, CarlaMateu Piñol, CarlesTecnologia InnovacionsIntel·ligència artificialSudokuRandom problem distributions have played a key role in the study and design of algorithms for constraint satisfaction and Boolean satisfiability, as well as in ourunderstanding of problem hardness, beyond standard worst-case complexity. We consider random problem distributions from a highly structured problem domain that generalizes the Quasigroup Completion problem (QCP) and Quasigroup with Holes (QWH), a widely used domain that captures the structure underlying a range of real-world applications. Our problem domain is also a generalization of the well-known Sudoku puz- zle: we consider Sudoku instances of arbitrary order, with the additional generalization that the block regions can have rectangular shape, in addition to the standard square shape. We evaluate the computational hardness of Generalized Sudoku instances, for different parameter settings. Our experimental hardness results show that we can generate instances that are considerably harder than QCP/QWH instances of the same size. More interestingly, we show the impact of different balancing strategies on problem hardness. We also provide insights into backbone variables in Generalized Sudoku instances and how they correlate to problem hardness.Association for the Advancement of Artificial Intelligence2006info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10459.1/46638reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: http://www.aaai.orgProceedings of the twenty-first National Conference on Artificial Intelligence, 2006, p. 10-15(c) Association for the Advancement of Artificial Intelligence, 2006info:eu-repo/semantics/openAccessoai:recercat.cat:10459.1/466382026-05-29T05:05:01Z
dc.title.none.fl_str_mv The impact of balancing on problem hardness in a highly structured domain
title The impact of balancing on problem hardness in a highly structured domain
spellingShingle The impact of balancing on problem hardness in a highly structured domain
Ansótegui Gil, Carlos José
Tecnologia Innovacions
Intel·ligència artificial
Sudoku
title_short The impact of balancing on problem hardness in a highly structured domain
title_full The impact of balancing on problem hardness in a highly structured domain
title_fullStr The impact of balancing on problem hardness in a highly structured domain
title_full_unstemmed The impact of balancing on problem hardness in a highly structured domain
title_sort The impact of balancing on problem hardness in a highly structured domain
dc.creator.none.fl_str_mv Ansótegui Gil, Carlos José
Béjar Torres, Ramón
Fernàndez Camon, César
Gomes, Carla
Mateu Piñol, Carles
author Ansótegui Gil, Carlos José
author_facet Ansótegui Gil, Carlos José
Béjar Torres, Ramón
Fernàndez Camon, César
Gomes, Carla
Mateu Piñol, Carles
author_role author
author2 Béjar Torres, Ramón
Fernàndez Camon, César
Gomes, Carla
Mateu Piñol, Carles
author2_role author
author
author
author
dc.subject.none.fl_str_mv Tecnologia Innovacions
Intel·ligència artificial
Sudoku
topic Tecnologia Innovacions
Intel·ligència artificial
Sudoku
description Random problem distributions have played a key role in the study and design of algorithms for constraint satisfaction and Boolean satisfiability, as well as in ourunderstanding of problem hardness, beyond standard worst-case complexity. We consider random problem distributions from a highly structured problem domain that generalizes the Quasigroup Completion problem (QCP) and Quasigroup with Holes (QWH), a widely used domain that captures the structure underlying a range of real-world applications. Our problem domain is also a generalization of the well-known Sudoku puz- zle: we consider Sudoku instances of arbitrary order, with the additional generalization that the block regions can have rectangular shape, in addition to the standard square shape. We evaluate the computational hardness of Generalized Sudoku instances, for different parameter settings. Our experimental hardness results show that we can generate instances that are considerably harder than QCP/QWH instances of the same size. More interestingly, we show the impact of different balancing strategies on problem hardness. We also provide insights into backbone variables in Generalized Sudoku instances and how they correlate to problem hardness.
publishDate 2006
dc.date.none.fl_str_mv 2006
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/10459.1/46638
url http://hdl.handle.net/10459.1/46638
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-first National Conference on Artificial Intelligence, 2006, p. 10-15
dc.rights.none.fl_str_mv (c) Association for the Advancement of Artificial Intelligence, 2006
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Association for the Advancement of Artificial Intelligence, 2006
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:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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