Return to search

Harnessing tractability in constraint satisfaction problems

The Constraint Satisfaction Problem (CSP) is a fundamental NP-complete problem with many applications in artificial intelligence. This problem has enjoyed considerable scientific attention in the past decades due to its practical usefulness and the deep theoretical questions it relates to. However, there is a wide gap between practitioners, who develop solving techniques that are efficient for industrial instances but exponential in the worst case, and theorists who design sophisticated polynomial-time algorithms for restrictions of CSP defined by certain algebraic properties. In this thesis we attempt to bridge this gap by providing polynomial-time algorithms to test for membership in a selection of major tractable classes. Even if the instance does not belong to one of these classes, we investigate the possibility of decomposing efficiently a CSP instance into tractable subproblems through the lens of parameterized complexity. Finally, we propose a general framework to adapt the concept of kernelization, central to parameterized complexity but hitherto rarely used in practice, to the context of constraint reasoning. Preliminary experiments on this last contribution show promising results.

Identiferoai:union.ndltd.org:univ-toulouse.fr/oai:oatao.univ-toulouse.fr:17821
Date07 December 2016
CreatorsCarbonnel, Clément
ContributorsInstitut National Polytechnique de Toulouse - INPT (FRANCE), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France)
Source SetsUniversité de Toulouse
LanguageEnglish
Detected LanguageEnglish
TypePhD Thesis, PeerReviewed, info:eu-repo/semantics/doctoralThesis
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
Relationhttp://oatao.univ-toulouse.fr/17821/

Page generated in 0.0021 seconds