{"title":"建筑问题优化求解的深度学习方法研究","authors":"Phillip Roshon, Feng-Jen Yang","doi":"10.1109/CSCI54926.2021.00100","DOIUrl":null,"url":null,"abstract":"In this study, we focus on a problem domain, construction problems, for reinforcement learning systems to optimize. We relate our approach to existing research in the field of automated theorem proving and other related techniques to optimize the solutions in this domain. We expect this study can inspire more interest in the adoption of and improve the efficiency of existing production systems.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Study on Deep Learning Approach to Optimize Solving Construction Problems\",\"authors\":\"Phillip Roshon, Feng-Jen Yang\",\"doi\":\"10.1109/CSCI54926.2021.00100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, we focus on a problem domain, construction problems, for reinforcement learning systems to optimize. We relate our approach to existing research in the field of automated theorem proving and other related techniques to optimize the solutions in this domain. We expect this study can inspire more interest in the adoption of and improve the efficiency of existing production systems.\",\"PeriodicalId\":206881,\"journal\":{\"name\":\"2021 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSCI54926.2021.00100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCI54926.2021.00100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Study on Deep Learning Approach to Optimize Solving Construction Problems
In this study, we focus on a problem domain, construction problems, for reinforcement learning systems to optimize. We relate our approach to existing research in the field of automated theorem proving and other related techniques to optimize the solutions in this domain. We expect this study can inspire more interest in the adoption of and improve the efficiency of existing production systems.
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