A choice functions portfolio for solving constraint satisfaction problems: A performance evaluation

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Abstract

Constraint Programming (CP) allows to solve constraint satisfaction and optimization problems by building and then exploring a search tree of potential solutions. Potential solutions are generated by firstly selecting a variable and then a value from the given problem, phase known as enumeration. In this context, Autonomous Search (AS) that is a particular case of adaptive systems, enables the problem solver to control and adapt its internal configuration during solving time, based on performance metrics in order to be more efficient. The goal is to provide a mechanism for CP solvers, integrating a component able to evaluate the solving performance process. In particular, we employ a classic decision making method called Choice Function (CF). In this paper, we present an evaluation of different choice functions, based on performance exhibited in a indicators set. The results are promising and show that it is feasible to solve constraint satisfaction problems with this new technique.

Original languageEnglish
Title of host publicationProceedings - 2015 34th International Conference of the Chilean Computer Science Society, SCCC 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781467398176
DOIs
StatePublished - 23 Feb 2016
Event34th International Conference of the Chilean Computer Science Society, SCCC 2015 - Santiago, Chile
Duration: 9 Nov 201513 Nov 2015

Publication series

NameProceedings - International Conference of the Chilean Computer Science Society, SCCC
Volume2016-February
ISSN (Print)1522-4902

Conference

Conference34th International Conference of the Chilean Computer Science Society, SCCC 2015
CountryChile
CitySantiago
Period9/11/1513/11/15

Keywords

  • autonomous search
  • choice function
  • Constraint programming

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