Improving the Effectiveness of SAT-Based Preprocessing for MaxSAT
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 239-244.
Abstract
Solvers for the Maximum satisfiability (MaxSAT) problem find an increasing number of applications today. We focus on improving MaxHS — one of the most successful recent MaxSAT algorithms — via SAT-based preprocessing. We show that employing SAT-based preprocessing via the so-called labelled CNF (LCNF) framework before calling MaxHS can in some cases greatly degrade the performance of the solver. As a remedy, we propose a lifting of MaxHS that works directly on LCNFs, allowing for a tighter integration of SAT-based preprocessing and MaxHS. Our empirical results on standard crafted and industrial weighted partial MaxSAT Evaluation benchmarks show overall improvements over the original MaxHS algorithm both with and without SAT-based preprocessing.
@inproceedings{ijcai2015p40,
title = {Improving the Effectiveness of SAT-Based Preprocessing for MaxSAT},
author = {Berg, Jeremias and Saikko, Paul and Järvisalo, Matti},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {239--244},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/40},
}
TY - CPAPER TI - Improving the Effectiveness of SAT-Based Preprocessing for MaxSAT AU - Berg, Jeremias AU - Saikko, Paul AU - Järvisalo, Matti T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 239 EP - 244 UR - https://ijcai.org/proceedings/2015/40 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: