{"title":"A multi-agent simulation for intelligence economy","authors":"Huang Yue","doi":"10.1109/CINC.2010.5643823","DOIUrl":null,"url":null,"abstract":"This paper introduces an intelligence economy and information network approach for intelligence economic using a Multi-agent system. While regular intelligent economy structure try to induce a general decision function for a learning task, multi-agent take into account a particular test set and try to simulate those particular example. The paper presents an analysis of why multi-agent is well suited for intelligence economy information management. These theoretical findings are supported by experiments on real-world data collections. The case studies show substantial improvements over inductive methods, especially for small Multi-agent-based computer information system training sets, improving the research activities as well as the economic value associated with such intelligence economy assets. This work also proposes a model for evaluate y efficiently, handling 1,909 examples and more.","PeriodicalId":227004,"journal":{"name":"2010 Second International Conference on Computational Intelligence and Natural Computing","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Computational Intelligence and Natural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINC.2010.5643823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper introduces an intelligence economy and information network approach for intelligence economic using a Multi-agent system. While regular intelligent economy structure try to induce a general decision function for a learning task, multi-agent take into account a particular test set and try to simulate those particular example. The paper presents an analysis of why multi-agent is well suited for intelligence economy information management. These theoretical findings are supported by experiments on real-world data collections. The case studies show substantial improvements over inductive methods, especially for small Multi-agent-based computer information system training sets, improving the research activities as well as the economic value associated with such intelligence economy assets. This work also proposes a model for evaluate y efficiently, handling 1,909 examples and more.