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ML-Based Automation of Constraint Satisfaction Model Transformation and Solver Configuration

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Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference (DCAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 585))

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Abstract

Constraint Programming is a powerful paradigm for tackling many real life challenges in the space of NP-hard combinatorial problems. While many consumer grade implementations of constraint solvers are available, the processes of correctly modelling a problem, as well as choosing and configuring a suitable solver remain an art usually reserved for experts. In this paper we outline a PhD research project aimed to reduce expert knowledge in and improve performance by Constraint Satisfaction/Optimization Problem Transformation and Constraint Solver Configuration. The paper describes the problems, poses research questions, proposes experiments, summarizes the related work and presents the current experimental progress.

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Acknowledgements

We would like to thank the organizers of the MiniZinc Challenge both for organizing it, as well as providing all submitted problems and results open access. We would also like to thank Hans Würfel, Michael Marte as well as the scikit-learn contributors for the tools they provide that made this research so far possible [9, 13, 14].

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Authors and Affiliations

  1. Programming Languages and Compiler Construction Group, Brandenburg University of Technology, 03046, Cottbus, Germany

    Ilja Becker, Sven Löffler & Petra Hofstedt

Authors
  1. Ilja Becker
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  2. Sven Löffler
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  3. Petra Hofstedt
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Corresponding author

Correspondence to Ilja Becker .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    José Manuel Machado

  2. University of Salamanca, Salamanca, Spain

    Pablo Chamoso

  3. University of Salamanca, Salamanca, Spain

    Guillermo Hernández

  4. Koszalin University of Technology, Koszalin, Poland

    Grzegorz Bocewicz

  5. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Roussanka Loukanova

  6. University of Coruña, La Coruña, Spain

    Esteban Jove

  7. Departamento de Matemática Aplicada, University of Salamanca, Salamanca, Spain

    Angel Martin del Rey

  8. University of Calabria, Arcavacata di Rende, Cosenza, Italy

    Michela Ricca

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Becker, I., Löffler, S., Hofstedt, P. (2023). ML-Based Automation of Constraint Satisfaction Model Transformation and Solver Configuration. In: Machado, J.M., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-031-23210-7_19

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