{"title":"Dynamic Sampling for Feasibility Determination","authors":"Yijie Peng, Jie Song, Jie Xu, E. Chong","doi":"10.1109/COASE.2018.8560526","DOIUrl":null,"url":null,"abstract":"We formulate the sampling allocation decision for feasibility determination as a dynamic policy in a Bayesian setting. This new formulation addresses the limitations of previous static optimization formulation. In an approximate dynamic programming paradigm, we propose an approximately optimal allocation policy that maximizes a single-feature of the value function one-step ahead. Numerical results demonstrate the efficiency of the proposed method.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"25 1","pages":"887-892"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COASE.2018.8560526","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
We formulate the sampling allocation decision for feasibility determination as a dynamic policy in a Bayesian setting. This new formulation addresses the limitations of previous static optimization formulation. In an approximate dynamic programming paradigm, we propose an approximately optimal allocation policy that maximizes a single-feature of the value function one-step ahead. Numerical results demonstrate the efficiency of the proposed method.
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