Automatic generation and selection of streamlined constraint models via Monte Carlo search on a model lattice

Patrick Spracklen, Ozgur Akgun, Ian James Miguel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
3 Downloads (Pure)

Abstract

Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously it has been established that it is possible to generate streamliners automatically from abstract constraint specifications in Essence and that effective combinations of streamliners can allow instances of much larger scale to be solved. A shortcoming of the previous approach was the crude exploration of the power set of all combinations using depth and breadth first search. We present a new approach based on Monte Carlo search over the lattice of streamlined models, which efficiently identifies effective streamliner combinations.
Original languageEnglish
Title of host publicationPrinciples and Practice of Constraint Programming
Subtitle of host publication24th International Conference, CP 2018, Lille, France, August 27-31, 2018, Proceedings
EditorsJohn Hooker
Place of PublicationCham
PublisherSpringer
Pages362-372
ISBN (Electronic)9783319983349
ISBN (Print)9783319983332
DOIs
Publication statusPublished - 2018

Publication series

NameLecture Notes in Computer Science (including subseries Programming and Software Engineering)
PublisherSpringer
Volume11008 LNCS
ISSN (Print)0302-9743

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