Learning parameters for the sequence constraint from solutions

Authors: Picard-Cantin, ÉmilieBouchard, MathieuQuimper, Claude-GuySweeney, Jason Pierre
Abstract: This paper studies the problem of learning parameters for global constraints such as Sequence from a small set of positive examples. The proposed technique computes the probability of observing a given constraint in a random solution. This probability is used to select the more likely constraint in a list of candidates. The learning method can be applied to both soft and hard constraints
Document Type: Article dans une conférence
Issue Date: 23 August 2016
Open Access Date: 27 November 2018
Document version: AM
Permalink: http://hdl.handle.net/20.500.11794/32623
This document was published in: Principles and Practice of Constraint Programming, 405-420 (2016)
https://doi.org/10.1007/978-3-319-44953-1_26
SpringerLink
Collection:Articles publiés dans des revues avec comité de lecture

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