Autonomous search in constraint satisfaction via black hole: A performance evaluation using different choice functions

Ricardo Soto, Broderick Crawford, Rodrigo Olivares, Stefanie Niklander, Eduardo Olguín

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

Autonomous Search is a modern technique aimed at introducing self-adjusting features to problem-solvers. In the context of constraint satisfaction, the idea is to let the solver engine to autonomously replace its solving strategies by more promising ones when poor performances are identified. The replacement is controlled by a choice function, which takes decisions based on information collected during solving time. However, the design of choice functions can be done in very different ways, leading of course to very different resolution processes. In this paper, we present a performance evaluation of 16 rigorously designed choice functions. Our goal is to provide new and interesting knowledge about the behavior of such functions in autonomous search architectures. To this end, we employ a set of well-known benchmarks that share general features that may be present on most constraint satisfaction and optimization problems. We believe this information will be useful in order to design better autonomous search systems for constraint satisfaction.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages56-65
Number of pages10
DOIs
StatePublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9712 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Autonomous search
  • Choice functions
  • Constraint programming
  • Constraint satisfaction
  • Optimization

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