{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:18Z","timestamp":1740108078472,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976034,U1808206"],"award-info":[{"award-number":["61976034,U1808206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017683","name":"Dalian Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["2022JJ12GX013"],"award-info":[{"award-number":["2022JJ12GX013"]}],"id":[{"id":"10.13039\/501100017683","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2022YGC20"],"award-info":[{"award-number":["2022YGC20"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["DUT21YG106"],"award-info":[{"award-number":["DUT21YG106"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00521-024-10655-8","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T20:15:44Z","timestamp":1734552944000},"page":"3925-3942","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MARIC: an efficient multi-agent real-time intention-based communication model for team cooperation"],"prefix":"10.1007","volume":"37","author":[{"given":"Xin","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8937-1515","authenticated-orcid":false,"given":"Hongwei","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangang","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqing","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"issue":"3, SI","key":"10655_CR1","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.1007\/s00521-022-06982-3","volume":"35","author":"P Zeng","year":"2023","unstructured":"Zeng P, Cui S, Song C, Wang Z, Li G (2023) A multiagent deep deterministic policy gradient-based distributed protection method for distribution network. Neural Comput Appl 35(3, SI):2267\u20132278","journal-title":"Neural Comput Appl"},{"key":"10655_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.120111","volume":"328","author":"K Park","year":"2022","unstructured":"Park K, Moon I (2022) Multi-agent deep reinforcement learning approach for ev charging scheduling in a smart grid. Appl Energy 328:120111","journal-title":"Appl Energy"},{"issue":"12","key":"10655_CR3","doi-asserted-by":"publisher","first-page":"9763","DOI":"10.1109\/JIOT.2020.3040768","volume":"8","author":"X Zhu","year":"2021","unstructured":"Zhu X, Luo Y, Liu A, Bhuiyan MZA, Zhang S (2021) Multiagent deep reinforcement learning for vehicular computation offloading in iot. IEEE Internet Things J 8(12):9763\u20139773","journal-title":"IEEE Internet Things J"},{"issue":"9, SI","key":"10655_CR4","doi-asserted-by":"publisher","first-page":"7227","DOI":"10.1007\/s00521-021-06855-1","volume":"34","author":"J Jia","year":"2022","unstructured":"Jia J, Yu R, Du Z, Chen J, Wang Q, Wang X (2022) Distributed localization for iot with multi-agent reinforcement learning. Neural Comput Appl 34(9, SI):7227\u20137240","journal-title":"Neural Comput Appl"},{"key":"10655_CR5","doi-asserted-by":"crossref","unstructured":"Ma T, Peng K, Rong H, Qian Y (2023) Agrcnet: communicate by attentional graph relations in multi-agent reinforcement learning for traffic signal control. Neural Comput Appl","DOI":"10.1007\/s00521-023-08875-5"},{"key":"10655_CR6","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.inffus.2023.02.009","volume":"94","author":"S Yang","year":"2023","unstructured":"Yang S, Yang B, Zeng Z, Kang Z (2023) Causal inference multi-agent reinforcement learning for traffic signal control. Inf Fusion 94:243\u2013256","journal-title":"Inf Fusion"},{"issue":"7540","key":"10655_CR7","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller MA, Fidjeland A, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533","journal-title":"Nature"},{"key":"10655_CR8","doi-asserted-by":"crossref","unstructured":"Gupta N, Srinivasaraghavan G, Mohalik S, Kumar N, Taylor ME (2023) Hammer: multi-level coordination of reinforcement learning agents via learned messaging. Neural Comput Appl","DOI":"10.1007\/s00521-023-09096-6"},{"key":"10655_CR9","first-page":"4190","volume":"30","author":"M Lanctot","year":"2017","unstructured":"Lanctot M, Zambaldi VF, Gruslys A, Lazaridou A, Tuyls K, P\u00e9rolat J, Silver D, Graepel T (2017) A unified game-theoretic approach to multiagent reinforcement learning. Proc Int Conf Neural Inf Process Syst 30:4190\u20134203","journal-title":"Proc Int Conf Neural Inf Process Syst"},{"key":"10655_CR10","unstructured":"Za\u00efem MS, Bennequin E (2019) Learning to communicate in multi-agent reinforcement learning: a review"},{"key":"10655_CR11","unstructured":"Zhu C, Dastani M, Wang S (2022) A survey of multi-agent reinforcement learning with communication"},{"key":"10655_CR12","unstructured":"Das A, Gervet T, Romoff J, Batra D, Parikh D, Rabbat M, Pineau J (2019) Tarmac: targeted multi-agent communication. In: Proceedings of the 36th international conference on machine learning, vol 97, pp 1538\u20131546"},{"key":"10655_CR13","unstructured":"Foerster JN, Assael YM, Freitas N, Whiteson S (2016) Learning to communicate with deep multi-agent reinforcement learning. In: Proceedings of the international conference on neural information processing systems, vol 29, pp 2137\u20132145"},{"key":"10655_CR14","unstructured":"Zhang Qian, Zhang Q, Lin J (2020) Succinct and robust multi-agent communication with temporal message control. In: Proceedings of the international conference on neural information processing systems, vol 33"},{"key":"10655_CR15","unstructured":"Sukhbaatar S, Szlam A, Fergus R (2016) Learning multiagent communication with backpropagation. In: Proceedings of the international conference on neural information processing systems, vol 29, pp 2244\u20132252"},{"issue":"05","key":"10655_CR16","doi-asserted-by":"publisher","first-page":"7211","DOI":"10.1609\/aaai.v34i05.6211","volume":"34","author":"Y Liu","year":"2020","unstructured":"Liu Y, Wang W, Hu Y, Hao J, Chen X, Gao Y (2020) Multi-agent game abstraction via graph attention neural network. Proc AAAI Conf Artif Intell 34(05):7211\u20137218. https:\/\/doi.org\/10.1609\/aaai.v34i05.6211","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10655_CR17","unstructured":"Niu Y, Paleja RR, Gombolay MC (2021) Multi-agent graph-attention communication and teaming. In: Proceedings of the 20th international conference on autonomous agents and multiagent systems, pp 964\u2013973"},{"key":"10655_CR18","unstructured":"Singh A, Jain T, Sukhbaatar S (2019) Learning when to communicate at scale in multiagent cooperative and competitive tasks. In: Proceedings of the 7th international conference on learning"},{"issue":"22","key":"10655_CR19","doi-asserted-by":"publisher","first-page":"16359","DOI":"10.1109\/JIOT.2021.3097947","volume":"8","author":"Q Yuan","year":"2021","unstructured":"Yuan Q, Fu X, Li Z, Luo G, Li J, Yang F (2021) Graphcomm: efficient graph convolutional communication for multiagent cooperation. IEEE Internet Things J 8(22):16359\u201316369","journal-title":"IEEE Internet Things J"},{"issue":"3","key":"10655_CR20","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.jsis.2005.07.003","volume":"14","author":"J Lu","year":"2005","unstructured":"Lu J, Yao JE, Yu C (2005) Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. J Strateg Inf Syst 14(3):245\u2013268","journal-title":"J Strateg Inf Syst"},{"issue":"6","key":"10655_CR21","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1037\/a0027425","volume":"31","author":"M Bruin","year":"2012","unstructured":"Bruin M, Sheeran P, Kok G, Hiemstra A, Prins JM, Hospers HJ, Breukelen GJP (2012) Self-regulatory processes mediate the intention-behavior relation for adherence and exercise behaviors. Health Psychol 31(6):695\u2013703","journal-title":"Health Psychol"},{"issue":"04","key":"10655_CR22","doi-asserted-by":"publisher","first-page":"6259","DOI":"10.1609\/aaai.v34i04.6093","volume":"34","author":"S Wang","year":"2020","unstructured":"Wang S, Hu L, Wang Y, Sheng QZ, Orgun M, Cao L (2020) Intention Nets: psychology-inspired user choice behavior modeling for next-basket prediction. Proc AAAI Conf Artif Intell 34(04):6259\u20136266. https:\/\/doi.org\/10.1609\/aaai.v34i04.6093","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10655_CR23","unstructured":"Sunehag P, Lever G, Gruslys A, Czarnecki WM, Zambaldi VF, Jaderberg M, Lanctot M, Sonnerat N, Leibo JZ, Tuyls K, Graepel T (2018) Value-decomposition networks for cooperative multi-agent learning based on team reward. In: Proceedings of the 17th international conference on autonomous agents and multiagent systems, pp 2085\u20132087"},{"key":"10655_CR24","unstructured":"Rashid T, Samvelyan M, Witt CS, Farquhar G, Foerster JN, Whiteson S (2018) QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning. In: Proceedings of the 35th international conference on machine learning, vol 80, pp 4292\u20134301"},{"key":"10655_CR25","unstructured":"Lowe R, Wu Y, Tamar A, Harb J, Abbeel P, Mordatch I (2017) Multi-agent actor-critic for mixed cooperative-competitive environments. In: Proceedings of the international conference on neural information processing systems, vol 30, pp 6379\u20136390"},{"key":"10655_CR26","doi-asserted-by":"crossref","unstructured":"Foerster JN, Farquhar G, Afouras T, Nardelli N, Whiteson S (2018) Counterfactual multi-agent policy gradients. In: Proceedings of the 32th AAAI conference on artificial intelligence, pp 2974\u20132982","DOI":"10.1609\/aaai.v32i1.11794"},{"key":"10655_CR27","unstructured":"Hoshen Y (2017) VAIN: attentional multi-agent predictive modeling. In: Proceedings of the international conference on neural information processing systems, vol 30, pp 2701\u20132711"},{"key":"10655_CR28","unstructured":"Peng P, Wen Y, Yang Y, Yuan Q, Tang Z, Long H, Wang J (2017) Multiagent bidirectionally-coordinated nets: emergence of human-level coordination in learning to play starcraft combat games. https:\/\/arxiv.org\/abs\/1703.10069"},{"key":"10655_CR29","unstructured":"Rangwala M, Williams R (2020) Learning multi-agent communication through structured attentive reasoning. In: Proceedings of the international conference on neural information processing systems, vol 33"},{"key":"10655_CR30","unstructured":"Zhang SQ, Zhang Q, Lin J (2019) Efficient communication in multi-agent reinforcement learning via variance based control. In: Proceedings of the international conference on neural information processing systems, vol 32, pp 3230\u20133239"},{"key":"10655_CR31","unstructured":"Wang R, He X, Yu R, Qiu W, An B, Rabinovich Z (2020) Learning efficient multi-agent communication: An information bottleneck approach. In: Proceedings of the 37th international conference on machine learning, vol 119, pp 9908\u20139918"},{"key":"10655_CR32","unstructured":"Guan C, Chen F, Yuan L, Wang C, Yin H, Zhang Z, Yu Y (2022) Efficient multi-agent communication via self-supervised information aggregation. In: Proceedings of the international conference on neural information processing systems, vol 36"},{"key":"10655_CR33","unstructured":"Jiang J, Lu Z (2018) Learning attentional communication for multi-agent cooperation. In: Proceedings of the international conference on neural information processing systems, vol 31, pp 7265\u20137275"},{"key":"10655_CR34","unstructured":"Kim D, Moon S, Hostallero D, Kang WJ, Lee T, Son K, Yi Y (2019) Learning to schedule communication in multi-agent reinforcement learning. In: Proceedings of the 7th international conference on learning representations"},{"key":"10655_CR35","unstructured":"Ding Z, Huang T, Lu Z (2020) Learning individually inferred communication for multi-agent cooperation. In: Proceedings of the international conference on neural information processing systems, vol 33"},{"key":"10655_CR36","unstructured":"Wang Y, Zhong F, Xu J, Wang Y (2022) Tom2c: target-oriented multi-agent communication and cooperation with theory of mind. In: Proceedings of the 10th international conference on learning representations"},{"key":"10655_CR37","unstructured":"Wang X, Li X, Shao J, Zhang J (2023) AC2C: adaptively controlled two-hop communication for multi-agent reinforcement learning. In: Proceedings of the 20th international conference on autonomous agents and multiagent systems, pp 427\u2013435"},{"key":"10655_CR38","unstructured":"Kim W, Park J, Sung Y (2021) Communication in multi-agent reinforcement learning: intention sharing. In: Proceedings of the 9th international conference on learning representations"},{"key":"10655_CR39","series-title":"Springer briefs in intelligent systems","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-28929-8","volume-title":"A concise introduction to decentralized POMDPs","author":"FA Oliehoek","year":"2016","unstructured":"Oliehoek FA, Amato C (2016) A concise introduction to decentralized POMDPs. Springer briefs in intelligent systems. Springer, Berlin"},{"issue":"3\u20134","key":"10655_CR40","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/BF00992696","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 8(3\u20134):229\u2013256","journal-title":"Mach Learn"},{"key":"10655_CR41","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the international conference on neural information processing systems, vol 30, pp 5998\u20136008"},{"key":"10655_CR42","unstructured":"Xiong R, Yang Y, He D, Zheng K, Zheng S, Xing C, Zhang H, Lan Y, Wang L, Liu T (2020) On layer normalization in the transformer architecture. In: Proceedings of the 37th international conference on machine learning, vol 119, pp 10524\u201310533"},{"issue":"1","key":"10655_CR43","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61\u201380","journal-title":"IEEE Trans Neural Netw"},{"key":"10655_CR44","unstructured":"Jang E, Gu S, Poole B (2017) Categorical reparameterization with gumbel-softmax. In: Proceedings of the 5th international conference on learning representations"},{"key":"10655_CR45","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: Proceedings of the 6th international conference on learning representations"},{"key":"10655_CR46","unstructured":"Jiang J, Dun C, Huang T, Lu Z (2020) Graph convolutional reinforcement learning. In: Proceedings of the 8th international conference on learning representations"},{"key":"10655_CR47","unstructured":"Wang T, Wang J, Zheng C, Zhang C (2020) Learning nearly decomposable value functions via communication minimization. In: Proceedings of the 8th international conference on learning representations"},{"key":"10655_CR48","doi-asserted-by":"crossref","unstructured":"Kurach K, Raichuk A, Stanczyk P, Zajac M, Bachem O, Espeholt L, Riquelme C, Vincent D, Michalski M, Bousquet O, Gelly S (2020) Google research football: A novel reinforcement learning environment. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp 4501\u20134510","DOI":"10.1609\/aaai.v34i04.5878"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10655-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10655-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10655-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T09:02:39Z","timestamp":1738918959000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10655-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,18]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10655"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10655-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,12,18]]},"assertion":[{"value":"29 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}