Learning the parameters of global constraints using branch-and-bound

Authors: Picard-Cantin, ÉmilieBouchard, MathieuQuimper, Claude-GuySweeney, Jason Pierre
Abstract: Precise constraint satisfaction modeling requires specific knowledge acquired from multiple past cases. We address this issue with a general branch-and-bound algorithm that learns the parameters of a given global constraint from a small set of positive solutions. The idea is to cleverly explore the possible combinations taken by the constraint’s parameters without explicitly enumerating all combinations. We apply our method to learn parameters of global constraints used in timetabling problems such as Sequence and SubsetFocus. The later constraint is our adaptation of the constraint Focus to timetabling problems.
Document Type: Article dans une conférence
Issue Date: 23 August 2017
Open Access Date: 26 November 2018
Document version: AM
Permalink: http://hdl.handle.net/20.500.11794/32584
This document was published in: Principles and Practice of Constraint Programming, 512-528 (2017)
https://doi.org/10.1007/978-3-319-66158-2_33
Springer Link
Alternative version: 10.1007/978-3-319-66158-2_33
Collection:Articles publiés dans des revues avec comité de lecture

Files in this item:
Description SizeFormat 
cp2017.pdf359.01 kBAdobe PDFThumbnail
View/Open
All documents in CorpusUL are protected by Copyright Act of Canada.