CSP problems as algorithmic benchmarks: measures, methods and models

On Computer Science research, traditionally, most efforts have been devoted to research hardness for the worst case of problems (proving NP completeness and comparing and reducing problems between them are the two most known). Artifcial Intelligence research, recently, has focused also on how some c...

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Detalles Bibliográficos
Autor: Mateu Piñol, Carles
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2009
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/63626
Acceso en línea:http://www.tdx.cat/TDX-0311109-172201
http://hdl.handle.net/10803/8122
Access Level:acceso abierto
Palabra clave:CSP
SAT
problemes de satisfacció de restriccions
algorismes
Ciència de la Computació i Intel. Artificial
004
id ES_9f4f87199209c20e0e461540639ddd21
oai_identifier_str oai:repositori.udl.cat:10459.1/63626
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv CSP problems as algorithmic benchmarks: measures, methods and models
title CSP problems as algorithmic benchmarks: measures, methods and models
spellingShingle CSP problems as algorithmic benchmarks: measures, methods and models
Mateu Piñol, Carles
CSP
SAT
problemes de satisfacció de restriccions
algorismes
Ciència de la Computació i Intel. Artificial
004
title_short CSP problems as algorithmic benchmarks: measures, methods and models
title_full CSP problems as algorithmic benchmarks: measures, methods and models
title_fullStr CSP problems as algorithmic benchmarks: measures, methods and models
title_full_unstemmed CSP problems as algorithmic benchmarks: measures, methods and models
title_sort CSP problems as algorithmic benchmarks: measures, methods and models
dc.creator.none.fl_str_mv Mateu Piñol, Carles
author Mateu Piñol, Carles
author_facet Mateu Piñol, Carles
author_role author
dc.contributor.none.fl_str_mv Fernàndez Camon, César
Béjar Torres, Ramón
Universitat de Lleida. Departament d'Informàtica i Enginyeria Industrial
dc.subject.none.fl_str_mv CSP
SAT
problemes de satisfacció de restriccions
algorismes
Ciència de la Computació i Intel. Artificial
004
topic CSP
SAT
problemes de satisfacció de restriccions
algorismes
Ciència de la Computació i Intel. Artificial
004
description On Computer Science research, traditionally, most efforts have been devoted to research hardness for the worst case of problems (proving NP completeness and comparing and reducing problems between them are the two most known). Artifcial Intelligence research, recently, has focused also on how some characteristics of concrete instances have dramatic effects on complexity and hardness while worst-case complexity remains the same. This has lead to focus research efforts on understanding which aspects and properties of problems or instances affect hardness, why very similar problems can require very diferent times to be solved. Research search based problems has been a substantial part of artificial intelligence research since its beginning. Big part of this research has been focused on developing faster and faster algorithms, better heuristics, new pruning techniques to solve ever harder problems. One aspect of this effort to create better solvers consists on benchmarking solver performance on selected problem sets, and, an, obviously, important part of that benchmarking is creating and defining new sets of hard problems. This two folded effort, on one hand to have at our disposal new problems, harder than previous ones, to test our solvers, and on the other hand, to obtain a deeper understanding on why such new problems are so hard, thus making easier to understand why some solvers outperform others, knowledge that can contribute towards designing and building better and faster algorithms and solvers. This work deals with designing better, that is harder and easy to generate, problems for CSP solvers, also usable for SAT solvers. In the first half of the work general concepts on hardness and CSP are introduced, including a complete description of the chosen problems for our study. This chosen problems are, Random Binary CSP Problems (BCSP), Quasi-group Completion Problems (QCP), Generalised Sudoku Problems (GSP), and a newly defined problem, Edge-Matching Puzzles (GEMP). Although BCSP and QCP are already well studied problems, that is not the case with GSP and GEMP. For GSP we will define new creation methods that ensure higher hardness than standard random methods. GEMP on the other hand is a newly formalised problem, we will define it, will provide also algorithms to build easily problems of tunable hardness and study its complexity and hardness. On the second part of the work we will propose and study new methods to increase the hardness of such problems. Providing both, algorithms to build harder problems and an in-depth study of the effect of such methods on hardness, specially on resolution time.
publishDate 2009
dc.date.none.fl_str_mv 2009
2009
2009
2011
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://www.tdx.cat/TDX-0311109-172201
http://hdl.handle.net/10803/8122
url http://www.tdx.cat/TDX-0311109-172201
http://hdl.handle.net/10803/8122
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universitat de Lleida
publisher.none.fl_str_mv Universitat de Lleida
dc.source.none.fl_str_mv TDX (Tesis Doctorals en Xarxa)
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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spelling CSP problems as algorithmic benchmarks: measures, methods and modelsMateu Piñol, CarlesCSPSATproblemes de satisfacció de restriccionsalgorismesCiència de la Computació i Intel. Artificial004On Computer Science research, traditionally, most efforts have been devoted to research hardness for the worst case of problems (proving NP completeness and comparing and reducing problems between them are the two most known). Artifcial Intelligence research, recently, has focused also on how some characteristics of concrete instances have dramatic effects on complexity and hardness while worst-case complexity remains the same. This has lead to focus research efforts on understanding which aspects and properties of problems or instances affect hardness, why very similar problems can require very diferent times to be solved. Research search based problems has been a substantial part of artificial intelligence research since its beginning. Big part of this research has been focused on developing faster and faster algorithms, better heuristics, new pruning techniques to solve ever harder problems. One aspect of this effort to create better solvers consists on benchmarking solver performance on selected problem sets, and, an, obviously, important part of that benchmarking is creating and defining new sets of hard problems. This two folded effort, on one hand to have at our disposal new problems, harder than previous ones, to test our solvers, and on the other hand, to obtain a deeper understanding on why such new problems are so hard, thus making easier to understand why some solvers outperform others, knowledge that can contribute towards designing and building better and faster algorithms and solvers. This work deals with designing better, that is harder and easy to generate, problems for CSP solvers, also usable for SAT solvers. In the first half of the work general concepts on hardness and CSP are introduced, including a complete description of the chosen problems for our study. This chosen problems are, Random Binary CSP Problems (BCSP), Quasi-group Completion Problems (QCP), Generalised Sudoku Problems (GSP), and a newly defined problem, Edge-Matching Puzzles (GEMP). Although BCSP and QCP are already well studied problems, that is not the case with GSP and GEMP. For GSP we will define new creation methods that ensure higher hardness than standard random methods. GEMP on the other hand is a newly formalised problem, we will define it, will provide also algorithms to build easily problems of tunable hardness and study its complexity and hardness. On the second part of the work we will propose and study new methods to increase the hardness of such problems. Providing both, algorithms to build harder problems and an in-depth study of the effect of such methods on hardness, specially on resolution time.Universitat de LleidaFernàndez Camon, CésarBéjar Torres, RamónUniversitat de Lleida. Departament d'Informàtica i Enginyeria Industrial2011200920092009info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersionhttp://www.tdx.cat/TDX-0311109-172201http://hdl.handle.net/10803/8122TDX (Tesis Doctorals en Xarxa)reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.info:eu-repo/semantics/openAccessoai:repositori.udl.cat:10459.1/636262026-06-24T12:42:17Z
score 15,198674

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