research-article

An enhanced features extractor for a portfolio of constraint solvers

Authors:

Roberto Amadini,

Maurizio Gabbrielli,

Jacopo MauroAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 1357 - 1359

https://doi.org/10.1145/2554850.2555114

Published: 24 March 2014 Publication History

Abstract

Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specification. In this paper we present an useful and flexible framework that is able to extract an extensive set of features from a Constraint (Satisfaction/Optimization) Problem defined in possibly different modeling languages: MiniZinc, FlatZinc or XCSP.

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Cited By

Cena CAkgün ÖKiziltan ZMiguel INightingale PUlrich-Oltean FElkind E(2023)Learning when to use automatic tabulation in constraint model reformulationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/211(1902-1910)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/211
Spracklen PDang NAkgün ÖMiguel I(2023)Automated streamliner portfolios for constraint satisfaction problemsArtificial Intelligence10.1016/j.artint.2023.103915319:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.artint.2023.103915
Ulrich-Oltean FNightingale PWalker J(2023)Learning to select SAT encodings for pseudo-Boolean and linear integer constraintsConstraints10.1007/s10601-023-09364-128:3(397-426)Online publication date: 2-Nov-2023
https://doi.org/10.1007/s10601-023-09364-1
Show More Cited By

Index Terms

An enhanced features extractor for a portfolio of constraint solvers
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence

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Published In

cover image ACM Conferences

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

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SAC 2014

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SIGAPP

SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

Gyeongju, Republic of Korea

Acceptance Rates

SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Citations

Cited By

Cena CAkgün ÖKiziltan ZMiguel INightingale PUlrich-Oltean FElkind E(2023)Learning when to use automatic tabulation in constraint model reformulationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/211(1902-1910)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/211
Spracklen PDang NAkgün ÖMiguel I(2023)Automated streamliner portfolios for constraint satisfaction problemsArtificial Intelligence10.1016/j.artint.2023.103915319:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.artint.2023.103915
Ulrich-Oltean FNightingale PWalker J(2023)Learning to select SAT encodings for pseudo-Boolean and linear integer constraintsConstraints10.1007/s10601-023-09364-128:3(397-426)Online publication date: 2-Nov-2023
https://doi.org/10.1007/s10601-023-09364-1
Spieker HGotlieb A(2021)Predictive Machine Learning of Objective Boundaries for Solving COPsAI10.3390/ai20400332:4(527-551)Online publication date: 28-Oct-2021
https://doi.org/10.3390/ai2040033
Amadini RGange GSchachte PSøndergaard HStuckey P(2021)Algorithm Selection for Dynamic Symbolic Execution: A Preliminary StudyLogic-Based Program Synthesis and Transformation10.1007/978-3-030-68446-4_10(192-209)Online publication date: 13-Feb-2021
https://doi.org/10.1007/978-3-030-68446-4_10
Spieker HGotlieb A(2021)Learning Objective Boundaries for Constraint Optimization ProblemsMachine Learning, Optimization, and Data Science10.1007/978-3-030-64580-9_33(394-408)Online publication date: 7-Jan-2021
https://doi.org/10.1007/978-3-030-64580-9_33
Akgün ÖDang NMiguel ISalamon ASpracklen PStone C(2020)Discriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIPIntegration of Constraint Programming, Artificial Intelligence, and Operations Research10.1007/978-3-030-58942-4_3(41-51)Online publication date: 19-Sep-2020
https://doi.org/10.1007/978-3-030-58942-4_3
Freuder E(2018)Progress towards the Holy GrailConstraints10.1007/s10601-017-9275-023:2(158-171)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10601-017-9275-0
Xu HKoenig SKumar T(2018)Towards Effective Deep Learning for Constraint Satisfaction ProblemsPrinciples and Practice of Constraint Programming10.1007/978-3-319-98334-9_38(588-597)Online publication date: 23-Aug-2018
https://doi.org/10.1007/978-3-319-98334-9_38
AMADINI RGABBRIELLI MMAURO J(2017)SUNNY-CP and the MiniZinc challengeTheory and Practice of Logic Programming10.1017/S147106841700020518:1(81-96)Online publication date: 10-Aug-2017
https://doi.org/10.1017/S1471068417000205
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