A randomized algorithm for solving the satisfiability problem

Autores
Cecchi, Laura
Año de publicación
1997
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In spite of the NP-completeness of the satisfiability decision problem (SAT problem), many researchers have been attracted by it because SAT has many applications in Artificial Intelligence. This paper presents a randomized David-Putnam based algorithm (RSAT) which solves this problem. Instead of selecting the next literal to be set true or false through a heuristic selection rule, RSAT does it through a random algorithm. RSAT not only improves the well-know Davis-Putnam Procedure that has been implemented with a heuristic selection rule, but avoids the incompleteness problem of the local search algorithms as well. RSAT is described in detail and it is compared with the heuristic based Davis-Putnam algorithm HDPP. We discuss the main features of the RSAT implementation and we especially analyze the random number generator features. Although the scope of the experiment is bound by the number of variables, our results indicate that the heuristic can be guessed by a random number generator and even improved. Empirical analysis that support the final conclusions are shown.
Eje: Workshop sobre Aspectos Teoricos de la Inteligencia Artificial
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
complete problems
NP
Satisfiability problem
ARTIFICIAL INTELLIGENCE
Algorithms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/24079
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/24079
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A randomized algorithm for solving the satisfiability problemCecchi, LauraCiencias Informáticascomplete problemsNPSatisfiability problemARTIFICIAL INTELLIGENCEAlgorithmsIn spite of the NP-completeness of the satisfiability decision problem (SAT problem), many researchers have been attracted by it because SAT has many applications in Artificial Intelligence. This paper presents a randomized David-Putnam based algorithm (RSAT) which solves this problem. Instead of selecting the next literal to be set true or false through a heuristic selection rule, RSAT does it through a random algorithm. RSAT not only improves the well-know Davis-Putnam Procedure that has been implemented with a heuristic selection rule, but avoids the incompleteness problem of the local search algorithms as well. RSAT is described in detail and it is compared with the heuristic based Davis-Putnam algorithm HDPP. We discuss the main features of the RSAT implementation and we especially analyze the random number generator features. Although the scope of the experiment is bound by the number of variables, our results indicate that the heuristic can be guessed by a random number generator and even improved. Empirical analysis that support the final conclusions are shown.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI)1997info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/24079enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:28:29Zoai:sedici.unlp.edu.ar:10915/24079Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:30.155SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A randomized algorithm for solving the satisfiability problem
title A randomized algorithm for solving the satisfiability problem
spellingShingle A randomized algorithm for solving the satisfiability problem
Cecchi, Laura
Ciencias Informáticas
complete problems
NP
Satisfiability problem
ARTIFICIAL INTELLIGENCE
Algorithms
title_short A randomized algorithm for solving the satisfiability problem
title_full A randomized algorithm for solving the satisfiability problem
title_fullStr A randomized algorithm for solving the satisfiability problem
title_full_unstemmed A randomized algorithm for solving the satisfiability problem
title_sort A randomized algorithm for solving the satisfiability problem
dc.creator.none.fl_str_mv Cecchi, Laura
author Cecchi, Laura
author_facet Cecchi, Laura
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
complete problems
NP
Satisfiability problem
ARTIFICIAL INTELLIGENCE
Algorithms
topic Ciencias Informáticas
complete problems
NP
Satisfiability problem
ARTIFICIAL INTELLIGENCE
Algorithms
dc.description.none.fl_txt_mv In spite of the NP-completeness of the satisfiability decision problem (SAT problem), many researchers have been attracted by it because SAT has many applications in Artificial Intelligence. This paper presents a randomized David-Putnam based algorithm (RSAT) which solves this problem. Instead of selecting the next literal to be set true or false through a heuristic selection rule, RSAT does it through a random algorithm. RSAT not only improves the well-know Davis-Putnam Procedure that has been implemented with a heuristic selection rule, but avoids the incompleteness problem of the local search algorithms as well. RSAT is described in detail and it is compared with the heuristic based Davis-Putnam algorithm HDPP. We discuss the main features of the RSAT implementation and we especially analyze the random number generator features. Although the scope of the experiment is bound by the number of variables, our results indicate that the heuristic can be guessed by a random number generator and even improved. Empirical analysis that support the final conclusions are shown.
Eje: Workshop sobre Aspectos Teoricos de la Inteligencia Artificial
Red de Universidades con Carreras en Informática (RedUNCI)
description In spite of the NP-completeness of the satisfiability decision problem (SAT problem), many researchers have been attracted by it because SAT has many applications in Artificial Intelligence. This paper presents a randomized David-Putnam based algorithm (RSAT) which solves this problem. Instead of selecting the next literal to be set true or false through a heuristic selection rule, RSAT does it through a random algorithm. RSAT not only improves the well-know Davis-Putnam Procedure that has been implemented with a heuristic selection rule, but avoids the incompleteness problem of the local search algorithms as well. RSAT is described in detail and it is compared with the heuristic based Davis-Putnam algorithm HDPP. We discuss the main features of the RSAT implementation and we especially analyze the random number generator features. Although the scope of the experiment is bound by the number of variables, our results indicate that the heuristic can be guessed by a random number generator and even improved. Empirical analysis that support the final conclusions are shown.
publishDate 1997
dc.date.none.fl_str_mv 1997
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/24079
url http://sedici.unlp.edu.ar/handle/10915/24079
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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