{"title":"基于生成式对抗网络的复杂智能系统行为建模概览","authors":"Yali Lv , Jingpu Duan , Xiong Li","doi":"10.1016/j.cosrev.2024.100635","DOIUrl":null,"url":null,"abstract":"<div><p>This paper provides an extensive and in-depth survey of behavior modeling for complex intelligent systems, focusing specifically on the innovative applications of Generative Adversarial Networks (GANs). The survey not only delves into the fundamental principles of GANs, but also elucidates their pivotal role in accurately modeling the behaviors exhibited by complex intelligent systems. By categorizing behavior modeling into prediction and learning, this survey meticulously examines the current landscape of research in each domain, shedding light on the latest advancements and methodologies driven by GANs. Furthermore, the paper offers insights into both the theoretical underpinnings and practical implications of GANs in behavior modeling for complex intelligent systems, and proposes potential future research directions to advance the field. Overall, this comprehensive survey serves as a valuable resource for researchers, practitioners, and scholars seeking to deepen their understanding of behavior modeling using GANs and to chart a course for future exploration and innovation in this dynamic field.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"52 ","pages":"Article 100635"},"PeriodicalIF":13.3000,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A survey on modeling for behaviors of complex intelligent systems based on generative adversarial networks\",\"authors\":\"Yali Lv , Jingpu Duan , Xiong Li\",\"doi\":\"10.1016/j.cosrev.2024.100635\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper provides an extensive and in-depth survey of behavior modeling for complex intelligent systems, focusing specifically on the innovative applications of Generative Adversarial Networks (GANs). The survey not only delves into the fundamental principles of GANs, but also elucidates their pivotal role in accurately modeling the behaviors exhibited by complex intelligent systems. By categorizing behavior modeling into prediction and learning, this survey meticulously examines the current landscape of research in each domain, shedding light on the latest advancements and methodologies driven by GANs. Furthermore, the paper offers insights into both the theoretical underpinnings and practical implications of GANs in behavior modeling for complex intelligent systems, and proposes potential future research directions to advance the field. Overall, this comprehensive survey serves as a valuable resource for researchers, practitioners, and scholars seeking to deepen their understanding of behavior modeling using GANs and to chart a course for future exploration and innovation in this dynamic field.</p></div>\",\"PeriodicalId\":48633,\"journal\":{\"name\":\"Computer Science Review\",\"volume\":\"52 \",\"pages\":\"Article 100635\"},\"PeriodicalIF\":13.3000,\"publicationDate\":\"2024-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Science Review\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574013724000194\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science Review","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574013724000194","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A survey on modeling for behaviors of complex intelligent systems based on generative adversarial networks
This paper provides an extensive and in-depth survey of behavior modeling for complex intelligent systems, focusing specifically on the innovative applications of Generative Adversarial Networks (GANs). The survey not only delves into the fundamental principles of GANs, but also elucidates their pivotal role in accurately modeling the behaviors exhibited by complex intelligent systems. By categorizing behavior modeling into prediction and learning, this survey meticulously examines the current landscape of research in each domain, shedding light on the latest advancements and methodologies driven by GANs. Furthermore, the paper offers insights into both the theoretical underpinnings and practical implications of GANs in behavior modeling for complex intelligent systems, and proposes potential future research directions to advance the field. Overall, this comprehensive survey serves as a valuable resource for researchers, practitioners, and scholars seeking to deepen their understanding of behavior modeling using GANs and to chart a course for future exploration and innovation in this dynamic field.
期刊介绍:
Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.