{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T19:46:38Z","timestamp":1776368798848,"version":"3.51.2"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/164","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T08:12:10Z","timestamp":1594195930000},"page":"1177-1183","source":"Crossref","is-referenced-by-count":29,"title":["NLocalSAT: Boosting Local Search with Solution Prediction"],"prefix":"10.24963","author":[{"given":"Wenjie","family":"Zhang","sequence":"first","affiliation":[{"name":"Peking University"}]},{"given":"Zeyu","family":"Sun","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Qihao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Ge","family":"Li","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Shaowei","family":"Cai","sequence":"additional","affiliation":[{"name":"Institute of Software, Chinese Academy of Sciences"}]},{"given":"Yingfei","family":"Xiong","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Peking University"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T22:13:41Z","timestamp":1594246421000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/164"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/164","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}