{"title":"利用MaxSAT中的多值变量","authors":"Josep Argelich, Chu Min Li, F. Manyà","doi":"10.1109/ISMVL.2017.42","DOIUrl":null,"url":null,"abstract":"Solving combinatorial optimization problems by reducing them to MaxSAT has shown to be a competitive problem solving approach. Since a lot of optimization problems have many-valued variables, we propose to exploit the domain information of the many-valued variables to enhance MaxSAT-based problem solving: first, we define a new way of encoding weighted maximum constraint satisfaction problems to both Boolean MaxSAT and many-valued MaxSAT, and second, we define a variable selection heuristic that takes into account the domain information and allow us to easily implement a many-valued MaxSAT solver. Moreover, the empirical results provide evidence of the good performance of the new encodings and the new branching heuristic on a representative set of instances.","PeriodicalId":393724,"journal":{"name":"2017 IEEE 47th International Symposium on Multiple-Valued Logic (ISMVL)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploiting Many-Valued Variables in MaxSAT\",\"authors\":\"Josep Argelich, Chu Min Li, F. Manyà\",\"doi\":\"10.1109/ISMVL.2017.42\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Solving combinatorial optimization problems by reducing them to MaxSAT has shown to be a competitive problem solving approach. Since a lot of optimization problems have many-valued variables, we propose to exploit the domain information of the many-valued variables to enhance MaxSAT-based problem solving: first, we define a new way of encoding weighted maximum constraint satisfaction problems to both Boolean MaxSAT and many-valued MaxSAT, and second, we define a variable selection heuristic that takes into account the domain information and allow us to easily implement a many-valued MaxSAT solver. Moreover, the empirical results provide evidence of the good performance of the new encodings and the new branching heuristic on a representative set of instances.\",\"PeriodicalId\":393724,\"journal\":{\"name\":\"2017 IEEE 47th International Symposium on Multiple-Valued Logic (ISMVL)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 47th International Symposium on Multiple-Valued Logic (ISMVL)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISMVL.2017.42\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 47th International Symposium on Multiple-Valued Logic (ISMVL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISMVL.2017.42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Solving combinatorial optimization problems by reducing them to MaxSAT has shown to be a competitive problem solving approach. Since a lot of optimization problems have many-valued variables, we propose to exploit the domain information of the many-valued variables to enhance MaxSAT-based problem solving: first, we define a new way of encoding weighted maximum constraint satisfaction problems to both Boolean MaxSAT and many-valued MaxSAT, and second, we define a variable selection heuristic that takes into account the domain information and allow us to easily implement a many-valued MaxSAT solver. Moreover, the empirical results provide evidence of the good performance of the new encodings and the new branching heuristic on a representative set of instances.