{"type":"ScholarlyArticle","title":"NLocalSAT: Boosting Local Search with Solution Prediction","authors":[{"name":"Wenjie Zhang","slug":"wenjie-zhang","given":"Wenjie","family":"Zhang","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , MoE; Software Institute , Peking University , China"},{"name":"Zeyu Sun","slug":"zeyu-sun","given":"Zeyu","family":"Sun","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , MoE; Software Institute , Peking University , China"},{"name":"Qihao Zhu","slug":"qihao-zhu","given":"Qihao","family":"Zhu","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , MoE; Software Institute , Peking University , China"},{"name":"Ge Li","slug":"ge-li","given":"Ge","family":"Li","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , MoE; Software Institute , Peking University , China"},{"name":"Shaowei Cai","slug":"shaowei-cai","given":"Shaowei","family":"Cai","affiliation":"State Key Laboratory of Computer Science , Institute of Software , Chinese Academy of Sciences , China; School of Computer Science and Technology , University of Chinese Academy of Sciences , China"},{"name":"Yingfei Xiong","slug":"yingfei-xiong","given":"Yingfei","family":"Xiong","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , MoE; Software Institute , Peking University , China"},{"name":"Lu Zhang","slug":"lu-zhang","given":"Lu","family":"Zhang","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , MoE; Software Institute , Peking University , China"}],"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.","doi":"10.24963/ijcai.2020/164","url":"https://ijcai.org/proceedings/2020/164","pdf":"https://ijcai.org/proceedings/2020/0164.pdf","markdown":"https://ijcai.org/proceedings/2020/164.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/164","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Constraints and SAT","track":"Main track","number":164,"pages":{"first":1177,"last":1183},"keywords":["Constraints and SAT: SAT: Algorithms and Techniques","Constraints and SAT: SAT: : Solvers and Applications"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24826/file_24826.m3u8","length_sec":298,"provider":"hls"}],"references":[{"position":1,"raw":"[Amizadeh et al., 2019] Saeed Amizadeh, Sergiy Matu- sevych, and Markus Weimer. 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