Contradicting conventional wisdom in constraint satisfaction

  • Daniel Sabin
  • , Eugene C. Freuder

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

Constraint satisfaction problems have wide application in artificial intelligence. They involve finding values for problem variables where the values must be consistent in that they satisfy restrictions on which combinations of values are allowed. Two standard techniques used in solving such problems are backtrack search and consistency inference. Conventional wisdom in the constraint satisfaction community suggests: 1) using consistency inference as preprocessing before search to prune values from consideration reduces subsequent search effort and 2) using consistency inference during search to prune values from consideration is best done at the limited level embodied in the forward checking algorithm. We present evidence contradicting both pieces of conventional wisdom, and suggesting renewed consideration of an approach which fully maintains arc consistency during backtrack search.

Original languageEnglish
Title of host publicationPrinciples and Practice of Constraint Programming - 2nd International Workshop, PPCP 1994, Proceedings
EditorsAlan Borning
PublisherSpringer Verlag
Pages10-20
Number of pages11
ISBN (Print)9783540586012
DOIs
Publication statusPublished - 1994
Externally publishedYes
Event2nd International Workshop on the Principles and Practice of Constraint Programming, PPCP 1994 - Rosario, Orcas Island, United States
Duration: 2 May 19944 May 1994

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume874 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on the Principles and Practice of Constraint Programming, PPCP 1994
Country/TerritoryUnited States
CityRosario, Orcas Island
Period2/05/944/05/94

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