{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:02:35Z","timestamp":1757617355769,"version":"3.44.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T00:00:00Z","timestamp":1736640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T00:00:00Z","timestamp":1736640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2023B1515120020","2023B1515120020","2023B1515120020"],"award-info":[{"award-number":["2023B1515120020","2023B1515120020","2023B1515120020"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB1714800"],"award-info":[{"award-number":["2021YFB1714800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s13042-024-02504-w","type":"journal-article","created":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T13:17:59Z","timestamp":1736687879000},"page":"4171-4188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Structural entropy-based scheduler for job planning problems using multi-agent reinforcement learning"],"prefix":"10.1007","volume":"16","author":[{"given":"Lixin","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,12]]},"reference":[{"issue":"3","key":"2504_CR1","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1111\/itor.12199","volume":"23","author":"IA Chaudhry","year":"2016","unstructured":"Chaudhry IA, Khan AA (2016) A research survey: review of flexible job shop scheduling techniques. Int Trans Oper Res 23(3):551\u2013591","journal-title":"Int Trans Oper Res"},{"key":"2504_CR2","doi-asserted-by":"crossref","unstructured":"Li J-q, Pan Q-k, Liang Y-C (2010) An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 59(4)","DOI":"10.1016\/j.cie.2010.07.014"},{"issue":"13","key":"2504_CR3","doi-asserted-by":"publisher","first-page":"2519","DOI":"10.1016\/j.ifacol.2019.11.585","volume":"52","author":"D Rooyani","year":"2019","unstructured":"Rooyani D, Defersha FM (2019) An efficient two-stage genetic algorithm for flexible job-shop scheduling. IFAC-PapersOnLine 52(13):2519\u20132524","journal-title":"IFAC-PapersOnLine"},{"key":"2504_CR4","doi-asserted-by":"publisher","first-page":"33125","DOI":"10.1109\/ACCESS.2020.2974014","volume":"8","author":"G Xiao-Lin","year":"2020","unstructured":"Xiao-Lin G, Huang M, Liang X (2020) A discrete particle swarm optimization algorithm with adaptive inertia weight for solving multiobjective flexible job-shop scheduling problem. IEEE Access 8:33125\u201333136","journal-title":"IEEE Access"},{"issue":"3","key":"2504_CR5","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.asoc.2009.10.006","volume":"10","author":"L-N Xing","year":"2010","unstructured":"Xing L-N, Chen Y-W, Wang P, Zhao Q-S, Xiong J (2010) A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl Soft Comput 10(3):888\u2013896","journal-title":"Appl Soft Comput"},{"issue":"4","key":"2504_CR6","first-page":"242","volume":"16","author":"M Hajibabaei","year":"2021","unstructured":"Hajibabaei M, Behnamian J (2021) Flexible job-shop scheduling problem with unrelated parallel machines and resources-dependent processing times: a tabu search algorithm. Int J Manag Sci Eng Manag 16(4):242\u2013253","journal-title":"Int J Manag Sci Eng Manag"},{"issue":"1","key":"2504_CR7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.ijpe.2012.03.034","volume":"141","author":"T-C Chiang","year":"2013","unstructured":"Chiang T-C, Lin H-J (2013) A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. Int J Prod Econ 141(1):87\u201398","journal-title":"Int J Prod Econ"},{"key":"2504_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.107969","volume":"190","author":"L Wang","year":"2021","unstructured":"Wang L, Xin H, Wang Y, Sujie X, Ma S, Yang K, Liu Z, Wang W (2021) Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning. Comput Netw 190:107969","journal-title":"Comput Netw"},{"key":"2504_CR9","doi-asserted-by":"crossref","unstructured":"Oren J, Ross C, Lefarov M, Richter F, Taitler A, Feldman Z, Di Castro D, Daniel C (2021) Solo: search online, learn offline for combinatorial optimization problems. In: Proceedings of the International Symposium on Combinatorial Search 12:97\u2013105","DOI":"10.1609\/socs.v12i1.18556"},{"key":"2504_CR10","unstructured":"De Schroeder WC, Gupta T, Makoviichuk D, Makoviychuk Vi, Whiteson S (2020) Is independent learning all you need in the starcraft multi-agent challenge? arXiv:2011.09533"},{"key":"2504_CR11","first-page":"24611","volume":"35","author":"C Yu","year":"2022","unstructured":"Yu C, Velu A, Vinitsky E et al (2022) The surprising effectiveness of Mappo in cooperative, multi-agent games. Adv Neural Inf Proc Syst 35:24611\u201324624","journal-title":"Adv Neural Inf Proc Syst"},{"issue":"6","key":"2504_CR12","doi-asserted-by":"publisher","first-page":"3290","DOI":"10.1109\/TIT.2016.2555904","volume":"62","author":"A Li","year":"2016","unstructured":"Li A, Pan Y (2016) Structural information and dynamical complexity of networks. IEEE Trans Inf Theory 62(6):3290\u20133339","journal-title":"IEEE Trans Inf Theory"},{"key":"2504_CR13","unstructured":"Wu J, Chen X,\u00a0Xu K,Li S (2022) Structural entropy guided graph hierarchical pooling. In: International Conference on machine learning, pp 24017\u201324030. PMLR"},{"key":"2504_CR14","doi-asserted-by":"crossref","unstructured":"Wu J, Li S, Li J, Pan Y,\u00a0Xu K (2022) A simple yet effective method for graph classification. In: International Joint Conference on artificial intelligence","DOI":"10.24963\/ijcai.2022\/497"},{"key":"2504_CR15","doi-asserted-by":"crossref","unstructured":"Zou D, Peng H, Huang X, Yang R, Li J, Wu J, Liu C, Yu PS (2023) Se-gsl: A general and effective graph structure learning framework through structural entropy optimization. In: Web Conference (WWW)","DOI":"10.1145\/3543507.3583453"},{"key":"2504_CR16","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang Y, Zhang Z, Yang S, Zhao K, Liu J (2023) User: unsupervised structural entropy-based robust graph neural network. In: Proceedings of the AAAI Conference on Artificial Intelligence 37:10235\u201310243","DOI":"10.1609\/aaai.v37i8.26219"},{"key":"2504_CR17","doi-asserted-by":"crossref","unstructured":"Yang Z,\u00a0Zhang G, Wu J, Yang J, Sheng QZ, Peng H, Li A, Xue S, Su J (2023) Minimum entropy principle guided graph neural networks. In: Proceedings of the Sixteenth ACM International Conference on web search and data mining, pages 114\u2013122","DOI":"10.1145\/3539597.3570467"},{"issue":"6","key":"2504_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3660522","volume":"42","author":"H Peng","year":"2024","unstructured":"Peng H, Zhang J, Huang X, Hao Z, Li A, Yu Z, Yu PS (2024) Unsupervised social bot detection via structural information theory. ACM Trans Inform Syst 42(6):1\u201342","journal-title":"ACM Trans Inform Syst"},{"key":"2504_CR19","doi-asserted-by":"crossref","unstructured":"Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder\u2013decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), page 1724. Association for Computational Linguistics","DOI":"10.3115\/v1\/D14-1179"},{"issue":"1","key":"2504_CR20","doi-asserted-by":"publisher","first-page":"20412","DOI":"10.1038\/srep20412","volume":"6","author":"A Li","year":"2016","unstructured":"Li A, Yin X, Pan Y (2016) Three-dimensional gene map of cancer cell types: structural entropy minimisation principle for defining tumour subtypes. Sci Rep 6(1):20412","journal-title":"Sci Rep"},{"key":"2504_CR21","unstructured":"Pan Y, Zheng F, Fan B (2021) An information-theoretic perspective of hierarchical clustering. arXiv:2108.06036"},{"key":"2504_CR22","doi-asserted-by":"crossref","unstructured":"Zeng X, Peng H, Li A (2023) Effective and stable role-based multi-agent collaboration by structural information principles. In: Proceedings of the AAAI conference on artificial intelligence 37:11772\u201311780","DOI":"10.1609\/aaai.v37i10.26390"},{"key":"2504_CR23","doi-asserted-by":"crossref","unstructured":"Zeng X,\u00a0Peng H,\u00a0Li A,\u00a0Liu C,\u00a0He L,\u00a0Yu PS (2023) Hierarchical state abstraction based on structural information principles. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23)","DOI":"10.24963\/ijcai.2023\/506"},{"issue":"3","key":"2504_CR24","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/BF02023073","volume":"41","author":"P Brandimarte","year":"1993","unstructured":"Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Ann Oper Res 41(3):157\u2013183","journal-title":"Ann Oper Res"},{"key":"2504_CR25","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/BF01719451","volume":"15","author":"J Hurink","year":"1994","unstructured":"Hurink J, Jurisch B, Thole M (1994) Tabu search for the job-shop scheduling problem with multi-purpose machines. Operations-Research-Spektrum 15:205\u2013215","journal-title":"Operations-Research-Spektrum"},{"key":"2504_CR26","volume-title":"Test instances for the flexible job shop scheduling problem with work centers","author":"Dennis Behnke and Martin Josef Geiger","year":"2012","unstructured":"Dennis Behnke and Martin Josef Geiger (2012) Test instances for the flexible job shop scheduling problem with work centers. Arbeitspapier\/Research Paper\/Helmut-Schmidt-Universit\u00e4t, Lehrstuhl f\u00fcr Betriebswirtschaftslehre, insbes. Logistik-Management"},{"issue":"4","key":"2504_CR27","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1080\/00207549008942754","volume":"28","author":"M Montazeri","year":"1990","unstructured":"Montazeri M, Van Wassenhove LN (1990) Analysis of scheduling rules for an fms. Int J Prod Res 28(4):785\u2013802","journal-title":"Int J Prod Res"},{"key":"2504_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117796","volume":"205","author":"K Lei","year":"2022","unstructured":"Lei K, Guo P, Zhao W, Wang Y, Qian L, Meng X, Tang L (2022) A multi-action deep reinforcement learning framework for flexible job-shop scheduling problem. Expert Syst Appl 205:117796","journal-title":"Expert Syst Appl"},{"key":"2504_CR29","first-page":"1621","volume":"33","author":"C Zhang","year":"2020","unstructured":"Zhang C, Song W, Cao Z, Zhang J, Tan PS, Chi X (2020) Learning to dispatch for job shop scheduling via deep reinforcement learning. Adv Neural Inform Process Syst 33:1621\u20131632","journal-title":"Adv Neural Inform Process Syst"},{"issue":"11","key":"2504_CR30","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.1080\/00207543.2020.1870013","volume":"59","author":"P Junyoung","year":"2021","unstructured":"Junyoung P, Jaehyeong C, Hun KS, Youngkook K, Jinkyoo P (2021) Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning. Int J Prod Res 59(11):3360\u20133377","journal-title":"Int J Prod Res"},{"issue":"4","key":"2504_CR31","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/BF02238804","volume":"45","author":"P Brucker","year":"1991","unstructured":"Brucker P, Schlie R (1991) Job-shop scheduling with multi-purpose machines. Computing 45(4):369\u2013375","journal-title":"Computing"},{"key":"2504_CR32","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s10845-007-0026-8","volume":"18","author":"P Fattahi","year":"2007","unstructured":"Fattahi P, Mehrabad MS, Jolai F (2007) Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. J Intell Manuf 18:331\u2013342","journal-title":"J Intell Manuf"},{"issue":"6","key":"2504_CR33","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1016\/j.apm.2009.09.002","volume":"34","author":"C \u00d6zg\u00fcven","year":"2010","unstructured":"\u00d6zg\u00fcven C, \u00d6zbak\u0131r L, Yavuz Y (2010) Mathematical models for job-shop scheduling problems with routing and process plan flexibility. Appl Math Model 34(6):1539\u20131548","journal-title":"Appl Math Model"},{"issue":"3","key":"2504_CR34","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1016\/j.apm.2012.03.020","volume":"37","author":"Y Demir","year":"2013","unstructured":"Demir Y, K\u00fcr\u015fat \u0130\u015fleyen S (2013) Evaluation of mathematical models for flexible job-shop scheduling problems. Appl Math Model 37(3):977\u2013988","journal-title":"Appl Math Model"},{"key":"2504_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101374","volume":"82","author":"L Meng","year":"2023","unstructured":"Meng L, Zhang C, Zhang B, Gao K, Ren Y, Sang H (2023) Milp modeling and optimization of multi-objective flexible job shop scheduling problem with controllable processing times. Swarm Evol Comput 82:101374","journal-title":"Swarm Evol Comput"},{"key":"2504_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106778","volume":"149","author":"R Chen","year":"2020","unstructured":"Chen R, Yang B, Li S, Wang S (2020) A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Comput Ind Eng 149:106778","journal-title":"Comput Ind Eng"},{"issue":"2","key":"2504_CR37","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1111\/itor.12878","volume":"30","author":"S Mahdi Homayouni","year":"2023","unstructured":"Mahdi Homayouni S, Fontes DBMM, Gon\u00e7alves JF (2023) A multistart biased random key genetic algorithm for the flexible job shop scheduling problem with transportation. Int Trans Oper Res 30(2):688\u2013716","journal-title":"Int Trans Oper Res"},{"key":"2504_CR38","doi-asserted-by":"crossref","unstructured":"Gao Y-J, Shang Q-X, Yang Y-Y, Hu R, Qian B (2023) Improved particle swarm optimization algorithm combined with reinforcement learning for solving flexible job shop scheduling problem. In: International Conference on intelligent computing, pages 288\u2013298. Springer","DOI":"10.1007\/978-981-99-4755-3_25"},{"key":"2504_CR39","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1007\/s00170-011-3437-9","volume":"58","author":"SHA Rahmati","year":"2012","unstructured":"Rahmati SHA, Zandieh M (2012) A new biogeography-based optimization (bbo) algorithm for the flexible job shop scheduling problem. Int J Adv Manuf Technol 58:1115\u20131129","journal-title":"Int J Adv Manuf Technol"},{"key":"2504_CR40","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.jmsy.2023.09.002","volume":"71","author":"J Xie","year":"2023","unstructured":"Xie J, Li X, Gao L, Gui L (2023) A hybrid genetic tabu search algorithm for distributed flexible job shop scheduling problems. J Manuf Syst 71:82\u201394","journal-title":"J Manuf Syst"},{"issue":"12","key":"2504_CR41","doi-asserted-by":"publisher","first-page":"3748","DOI":"10.1080\/00207543.2013.765074","volume":"51","author":"H-H Doh","year":"2013","unstructured":"Doh H-H, Jae-Min Yu, Kim J-S, Lee D-H, Nam S-H (2013) A priority scheduling approach for flexible job shops with multiple process plans. Int J Prod Res 51(12):3748\u20133764","journal-title":"Int J Prod Res"},{"issue":"5","key":"2504_CR42","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1109\/TEVC.2023.3244607","volume":"28","author":"M Xu","year":"2024","unstructured":"Xu M,\u00a0Mei Y, Zhang F, Zhang M (2024) Genetic programming with lexicase selection for large-scale dynamic flexible job shop scheduling. IEEE Trans Evol Comput 28(5):1235\u20131249","journal-title":"IEEE Trans Evol Comput"},{"key":"2504_CR43","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1016\/j.procir.2018.03.212","volume":"72","author":"B Waschneck","year":"2018","unstructured":"Waschneck B, Reichstaller A, Belzner L, Altenm\u00fcller T, Bauernhansl T, Knapp A, Kyek A (2018) Optimization of global production scheduling with deep reinforcement learning. Proc Cirp 72:1264\u20131269","journal-title":"Proc Cirp"},{"key":"2504_CR44","first-page":"1621","volume":"33","author":"C Zhang","year":"2020","unstructured":"Zhang C, Song W, Cao Z, Zhang J, Xu C (2020) Learning to dispatch for job shop scheduling via deep reinforcement learning. Adv Neural Inf Proc Syst 33:1621\u20131632","journal-title":"Adv Neural Inf Proc Syst"},{"issue":"2","key":"2504_CR45","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.1109\/TII.2022.3189725","volume":"19","author":"W Song","year":"2022","unstructured":"Song W, Chen X, Li Q, Cao Z (2022) Flexible job-shop scheduling via graph neural network and deep reinforcement learning. IEEE Trans Industr Inf 19(2):1600\u20131610","journal-title":"IEEE Trans Industr Inf"},{"key":"2504_CR46","unstructured":"Chen X, Tian Y (2019) Learning to perform local rewriting for combinatorial optimization. Adv Neural Inf Proc Syst 32"},{"key":"2504_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2022.102324","volume":"77","author":"X Wang","year":"2022","unstructured":"Wang X, Zhang L, Lin T, Zhao C, Wang K, Chen Z (2022) Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement learning. Robot Comput-Integr Manuf 77:102324","journal-title":"Robot Comput-Integr Manuf"},{"key":"2504_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2022.102412","volume":"78","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Zhu H, Tang D, Zhou T, Gui Y (2022) Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems. Robot Comput-Integr Manuf 78:102412","journal-title":"Robot Comput-Integr Manuf"},{"key":"2504_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110083","volume":"259","author":"J-D Zhang","year":"2023","unstructured":"Zhang J-D, He Z, Chan W-H, Chow C-Y (2023) Deepmag: deep reinforcement learning with multi-agent graphs for flexible job shop scheduling. Knowl-Based Syst 259:110083","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"2504_CR50","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3390\/pr11010267","volume":"11","author":"X Zhu","year":"2023","unstructured":"Zhu X, Jiazhong X, Ge J, Wang Y, Xie Z (2023) Multi-task multi-agent reinforcement learning for real-time scheduling of a dual-resource flexible job shop with robots. Processes 11(1):267","journal-title":"Processes"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02504-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02504-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02504-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T03:02:56Z","timestamp":1757127776000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02504-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,12]]},"references-count":50,"journal-issue":{"issue":"7-8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2504"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02504-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,1,12]]},"assertion":[{"value":"10 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}