NLocalSAT: Boosting Local Search with Solution Prediction
Wenjie Zhang, Zeyu Sun, Qihao Zhu, Ge Li, Shaowei Cai, Yingfei Xiong, Lu Zhang
Abstract
The Boolean satisfiability problem (SAT) is a famous NP-complete problem in computer science. An effective way for solving a satisfiable SAT problem is the stochastic local search (SLS). However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers. To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network. We evaluated NLocalSAT on five SLS solvers (CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random track of SAT Competition 2018. The experimental results show that solvers with NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers.
BibTeX
@inproceedings{ijcai2020p164,
title = {NLocalSAT: Boosting Local Search with Solution Prediction},
author = {Zhang, Wenjie and Sun, Zeyu and Zhu, Qihao and Li, Ge and Cai, Shaowei and Xiong, Yingfei and Zhang, Lu},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {1177--1183},
year = {2020},
month = {7},
note = {Main track},
doi = {10.24963/ijcai.2020/164},
url = {https://doi.org/10.24963/ijcai.2020/164},
}