A Model-Oriented Approach for Lifting Symmetry-Breaking Constraints in Answer Set Programming
Abstract
Writing correct models for combinatorial problems is relatively straightforward; however, they must be efficient to be usable with instances producing many solution candidates. In this work, we aim to automatically generalise the discarding of symmetric solutions of Answer Set Programming instances, improving the efficiency of the programs with first-order constraints derived from propositional symmetry-breaking constraints.
Cite
Text
Tarzariol. "A Model-Oriented Approach for Lifting Symmetry-Breaking Constraints in Answer Set Programming." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/840Markdown
[Tarzariol. "A Model-Oriented Approach for Lifting Symmetry-Breaking Constraints in Answer Set Programming." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/tarzariol2022ijcai-model/) doi:10.24963/IJCAI.2022/840BibTeX
@inproceedings{tarzariol2022ijcai-model,
title = {{A Model-Oriented Approach for Lifting Symmetry-Breaking Constraints in Answer Set Programming}},
author = {Tarzariol, Alice},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5875-5876},
doi = {10.24963/IJCAI.2022/840},
url = {https://mlanthology.org/ijcai/2022/tarzariol2022ijcai-model/}
}