IJCAI 2022poster2 citations
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.
Search and Optimization (SO): GeneralKnowledge Representation and Reasoning (KRR): GeneralMachine Learning (ML): General
BibTeX
@inproceedings{ijcai2022p840,
title = {A Model-Oriented Approach for Lifting Symmetry-Breaking Constraints in Answer Set Programming},
author = {Tarzariol, Alice},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5875--5876},
year = {2022},
month = {7},
note = {Doctoral Consortium},
doi = {10.24963/ijcai.2022/840},
url = {https://doi.org/10.24963/ijcai.2022/840},
}