{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T16:09:03Z","timestamp":1782576543059,"version":"3.54.5"},"reference-count":20,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Technology Innovation Program"},{"name":"Ministry of Trade, Industry and Energy (MOTIE), South Korea","award":["20023907"],"award-info":[{"award-number":["20023907"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3501338","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T18:58:20Z","timestamp":1731956300000},"page":"172256-172265","source":"Crossref","is-referenced-by-count":7,"title":["Bayesian Reinforcement Learning for Adaptive Balancing in an Assembly Line With Human-Robot Collaboration"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3139-2177","authenticated-orcid":false,"given":"Hyun-Rok","family":"Lee","sequence":"first","affiliation":[{"name":"Industrial Engineering Department, Inha University, Incheon, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanghyun","family":"Park","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, Inha University, Incheon, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0878-938X","authenticated-orcid":false,"given":"Jimin","family":"Lee","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, Inha University, Incheon, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2020.01.043"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2017.07.141"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2023.106359"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2023.06.014"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2021.07.015"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2021.102227"},{"key":"ref7","first-page":"1432","article-title":"Hidden parameter Markov decision processes: A semiparametric regression approach for discovering latent task parametrizations","volume-title":"Proc. Int. Joint Conf. Artif. Intell.","author":"Doshi-Velez"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirp.2019.04.006"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2021.105674"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2021.1989077"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2018.1470695"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106394"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2022.05.006"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2022.3161993"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2022.102359"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2022.103771"},{"key":"ref17","article-title":"OpenAI gym","author":"Brockman","year":"2016","journal-title":"arXiv:1606.01540"},{"key":"ref18","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017","journal-title":"arXiv:1707.06347"},{"issue":"268","key":"ref19","first-page":"1","article-title":"Stable-Baselines3: Reliable reinforcement learning implementations","volume":"22","author":"Raffin","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref20","first-page":"1","article-title":"VariBAD: A very good method for Bayes-adaptive deep RL via meta-learning","volume-title":"Proc. ICLR","author":"Zintgraf"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10756596.pdf?arnumber=10756596","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T19:35:06Z","timestamp":1734636906000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10756596\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3501338","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}