{"title":"如何大规模协调决策?智慧城市及其他领域的集体学习实践教程","authors":"Evangelos Pournaras","doi":"10.1109/ACSOS-C52956.2021.00084","DOIUrl":null,"url":null,"abstract":"This 1.5-hour tutorial will provide an introduction to the theory and practice of multi-agent collective learning for coordinating distributed decisions at large scale. You will develop the required skills to work with the EPOS software artifact to solve distributed optimization problems in Smart Cities. The tutorial will also promote collaborations within the ACSOS community. PhD students and more senior colleagues are particularly encouraged to participate. No programming experience is required. You are also encouraged to bring in your own multi-agent optimization problem to explore a potential solution using collective learning.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How to Coordinate Decisions at Large Scale? A Hands-on Tutorial on Collective Learning for Smart Cities and Beyond\",\"authors\":\"Evangelos Pournaras\",\"doi\":\"10.1109/ACSOS-C52956.2021.00084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This 1.5-hour tutorial will provide an introduction to the theory and practice of multi-agent collective learning for coordinating distributed decisions at large scale. You will develop the required skills to work with the EPOS software artifact to solve distributed optimization problems in Smart Cities. The tutorial will also promote collaborations within the ACSOS community. PhD students and more senior colleagues are particularly encouraged to participate. No programming experience is required. You are also encouraged to bring in your own multi-agent optimization problem to explore a potential solution using collective learning.\",\"PeriodicalId\":268224,\"journal\":{\"name\":\"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)\",\"volume\":\"89 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSOS-C52956.2021.00084\",\"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 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSOS-C52956.2021.00084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How to Coordinate Decisions at Large Scale? A Hands-on Tutorial on Collective Learning for Smart Cities and Beyond
This 1.5-hour tutorial will provide an introduction to the theory and practice of multi-agent collective learning for coordinating distributed decisions at large scale. You will develop the required skills to work with the EPOS software artifact to solve distributed optimization problems in Smart Cities. The tutorial will also promote collaborations within the ACSOS community. PhD students and more senior colleagues are particularly encouraged to participate. No programming experience is required. You are also encouraged to bring in your own multi-agent optimization problem to explore a potential solution using collective learning.