Learning When to Use Automatic Tabulation in Constraint Model Reformulation

This repository contains the problem classes, instances, dataset and machine learning (ML) models used for the paper: "Learning When to Use Automatic Tabulation in Constraint Model Reformulation".

Table of contents

1. Organization

Organization

The repository is organized as follows:

• The instances of each problem class are grouped in a folder, along with problem's model.
• 'Generators' contains a few python and bash scripts used to generate the instances of some of the problem classes.
• 'Dataset' contains the results obtained by solving the instances with different solvers, i.e. minion, kissat, kissat-mdd and chuffed, as csv files. In the same folder are also available the instances' features (folder 'on-off-feature-instances') and the results obtained for the second task.
• 'ML_logs' should contain the results obtained when training a few ML models on the dataset (use the python script provided in the "Code" folder).
• 'Code' contains the code used by us to train the models and to study the results.

Citation

@inproceedings{cena2023learning, title={Learning When to Use Automatic Tabulation in Constraint Model Reformulation}, author={Cena, Carlo and Akg{"u}n, {"O}zg{"u}r and Kiziltan, Zeynep and Miguel, Ian James and Nightingale, Peter and Ulrich-Oltean, Felix}, booktitle={Proceedings of the 32nd International Joint Conference on Artificial Intelligence}, year={2023}, organization={IJCAI/AAAI} }