Active Constraint Acquisition

This repository contains implementations of various constraint acquisition (CA) algorithms and methods.

Implemented Algorithms

This repository includes the code for the following papers:

Guided Bottom-Up Interactive Constraint Acquisition (CP2023)

• Paper: Guided Bottom-Up Interactive Constraint Acquisition
• Authors: Dimosthenis C. Tsouros, Senne Berden, Tias Guns
• Conference: 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Cite as:

@inproceedings{tsouros2023guided,
  title={Guided Bottom-Up Interactive Constraint Acquisition},
  author={Tsouros, Dimosthenis C and Berden, Senne and Guns, Tias},
  booktitle={29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  year={2023},
  organization={Schloss-Dagstuhl-Leibniz Zentrum f{\"u}r Informatik}
}

Learning to Learn in Interactive Constraint Acquisition (AAAI2024)

• Paper: Learning to Learn in Interactive Constraint Acquisition
• Authors: Dimosthenis Tsouros, Senne Berden, Tias Guns
• Conference: 38th AAAI Conference on Artificial Intelligence

Cite as:

@inproceedings{tsouros2024learning,
  title={Learning to learn in interactive constraint acquisition},
  author={Tsouros, Dimosthenis and Berden, Senne and Guns, Tias},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={8},
  pages={8154--8162},
  year={2024}
}

Usage

In each folder, you can find detailed usage guidelines

Requirements

This repository uses the constraint solving library CPMpy for modeling constraint problems. Scikit learn is used for implementing ML algorithms in the AAAI24 implementation.

Contact

For any questions or issues, please contact Dimos Tsouros (dimos.tsouros@kuleuven.be) or open an issue.