Collaborative learning for constraint solving

  • Susan L. Epstein
  • , Eugene C. Freuder

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

Abstract

Although constraint programming offers a wealth of strong, general-purpose methods, in practice a complex, real application demands a person who selects, combines, and refines various available techniques for constraint satisfaction and optimization. Although such tuning produces efficient code, the scarcity of human experts slows commercialization. The necessary expertise is of two forms: constraint programming expertise and problem-domain expertise. The former is in short supply, and even experts can be reduced to trial and error prototyping; the latter is difficult to extract. The project described here seeks to automate both the application of constraint programming expertise and the extraction of domain-specific expertise. It applies FORR, an architecture for learning and problem-solving, to constraint solving. FORR develops expertise from multiple heuristics. A successful case study is presented on coloring problems.

Original languageEnglish
Title of host publicationPrinciples and Practice of Constraint Programming - CP 2001 - 7th International Conference, CP 2001, Proceedings
EditorsToby Walsh
PublisherSpringer Verlag
Pages46-60
Number of pages15
ISBN (Print)3540428631, 9783540428633
DOIs
Publication statusPublished - 2001
Event7th International Conference on Principles and Practice of Constraint Programming, CP 2001 - Paphos, Cyprus
Duration: 26 Nov 20011 Dec 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2239
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Principles and Practice of Constraint Programming, CP 2001
Country/TerritoryCyprus
CityPaphos
Period26/11/011/12/01

Fingerprint

Dive into the research topics of 'Collaborative learning for constraint solving'. Together they form a unique fingerprint.

Cite this