{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T05:46:48Z","timestamp":1774590408204,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"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":["52171332"],"award-info":[{"award-number":["52171332"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3072022JC0601"],"award-info":[{"award-number":["3072022JC0601"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of Avionics System Integrated Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10489-023-05058-6","type":"journal-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:02:08Z","timestamp":1697871728000},"page":"29076-29093","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multi-intent autonomous decision-making for air combat with deep reinforcement learning"],"prefix":"10.1007","volume":"53","author":[{"given":"Luyu","family":"Jia","sequence":"first","affiliation":[]},{"given":"Chengtao","family":"Cai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0281-0336","authenticated-orcid":false,"given":"Xingmei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhengkun","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Junzheng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Kejun","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jiaqi","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"issue":"10","key":"5058_CR1","doi-asserted-by":"publisher","first-page":"11474","DOI":"10.1007\/s10489-022-03986-3","volume":"53","author":"L Jiang","year":"2023","unstructured":"Jiang L, Wei R, Wang D (2023) Uavs rounding up inspired by communication multi-agent depth deterministic policy gradient. Appl Intell 53(10):11474\u201311489. https:\/\/doi.org\/10.1007\/s10489-022-03986-3","journal-title":"Appl Intell"},{"issue":"14","key":"5058_CR2","doi-asserted-by":"publisher","first-page":"16775","DOI":"10.1007\/s10489-021-02353-y","volume":"52","author":"R Zhao","year":"2022","unstructured":"Zhao R, Wang Y, Xiao G et al (2022) A method of path planning for unmanned aerial vehicle based on the hybrid of selfish herd optimizer and particle swarm optimizer. Appl Intell 52(14):16775\u201316798. https:\/\/doi.org\/10.1007\/s10489-021-02353-y","journal-title":"Appl Intell"},{"issue":"12","key":"5058_CR3","doi-asserted-by":"publisher","first-page":"14313","DOI":"10.1007\/s10489-022-03270-4","volume":"52","author":"H Shi","year":"2022","unstructured":"Shi H, Lu F, Wu L et al (2022) Optimal trajectories of multi-uavs with approaching formation for target tracking using improved harris hawks optimizer. Appl Intell 52(12):14313\u201314335. https:\/\/doi.org\/10.1007\/s10489-022-03270-4","journal-title":"Appl Intell"},{"key":"5058_CR4","doi-asserted-by":"publisher","unstructured":"Zhang A, Zhang B, Bi W et al (2022) Attention based trajectory prediction method under the air combat environment. Appl Intell 1\u201315. https:\/\/doi.org\/10.1007\/s10489-022-03292-y","DOI":"10.1007\/s10489-022-03292-y"},{"key":"5058_CR5","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.ins.2022.02.025","volume":"594","author":"S Li","year":"2022","unstructured":"Li S, Chen M, Wang Y et al (2022) A fast algorithm to solve large-scale matrix games based on dimensionality reduction and its application in multiple unmanned combat air vehicles attack-defense decision-making. Inf Sci 594:305\u2013321. https:\/\/doi.org\/10.1016\/j.ins.2022.02.025","journal-title":"Inf Sci"},{"key":"5058_CR6","doi-asserted-by":"publisher","first-page":"106815","DOI":"10.1016\/j.ast.2021.106815","volume":"115","author":"T Zhang","year":"2021","unstructured":"Zhang T, Li C, Ma D et al (2021) An optimal task management and control scheme for military operations with dynamic game strategy. Aerospace Sci Technol 115:106815. https:\/\/doi.org\/10.1016\/j.ast.2021.106815","journal-title":"Aerospace Sci Technol"},{"key":"5058_CR7","doi-asserted-by":"publisher","unstructured":"Guo C, Zhang J, Hu J, et\u00a0al (2023) Uav air combat algorithm based on bayesian probability model. In: Fu W, Gu M, Niu Y (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). Springer Nature Singapore, Singapore, pp 3176\u20133185, https:\/\/doi.org\/10.1007\/978-981-99-0479-2_292","DOI":"10.1007\/978-981-99-0479-2_292"},{"issue":"11","key":"5058_CR8","doi-asserted-by":"publisher","first-page":"9906","DOI":"10.1109\/TWC.2022.3180395","volume":"21","author":"H Wu","year":"2022","unstructured":"Wu H, Li M, Gao Q et al (2022) Eavesdropping and anti-eavesdropping game in uav wiretap system: A differential game approach. IEEE Trans Wirel Commun 21(11):9906\u20139920. https:\/\/doi.org\/10.1109\/TWC.2022.3180395","journal-title":"IEEE Trans Wirel Commun"},{"issue":"10","key":"5058_CR9","doi-asserted-by":"publisher","first-page":"3208","DOI":"10.1109\/JSAC.2021.3088694","volume":"39","author":"S Chai","year":"2021","unstructured":"Chai S, Lau VKN (2021) Multi-uav trajectory and power optimization for cached uav wireless networks with energy and content recharging-demand driven deep learning approach. IEEE J Sel Areas Commun 39(10):3208\u20133224. https:\/\/doi.org\/10.1109\/JSAC.2021.3088694","journal-title":"IEEE J Sel Areas Commun"},{"issue":"5","key":"5058_CR10","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.2514\/1.46815","volume":"33","author":"JS McGrew","year":"2010","unstructured":"McGrew JS, How JP, Williams B et al (2010) Air-combat strategy using approximate dynamic programming. J Guid Control Dyn 33(5):1641\u20131654. https:\/\/doi.org\/10.2514\/1.46815","journal-title":"J Guid Control Dyn"},{"key":"5058_CR11","doi-asserted-by":"publisher","first-page":"117448","DOI":"10.1016\/j.eswa.2022.117448","volume":"203","author":"JB Crumpacker","year":"2022","unstructured":"Crumpacker JB, Robbins MJ, Jenkins PR (2022) An approximate dynamic programming approach for solving an air combat maneuvering problem. Expert Syst Appl 203:117448. https:\/\/doi.org\/10.1016\/j.eswa.2022.117448","journal-title":"Expert Syst Appl"},{"key":"5058_CR12","doi-asserted-by":"publisher","first-page":"117994","DOI":"10.1016\/j.eswa.2022.117994","volume":"207","author":"CBR Ng","year":"2022","unstructured":"Ng CBR, Bil C, Sardina S et al (2022) Designing an expert system to support aviation occurrence investigations. Expert Syst Appl 207:117994. https:\/\/doi.org\/10.1016\/j.eswa.2022.117994","journal-title":"Expert Syst Appl"},{"key":"5058_CR13","doi-asserted-by":"publisher","unstructured":"Yu X, Gao X, Wang L, et\u00a0al (2022) Cooperative multi-uav task assignment in cross-regional joint operations considering ammunition inventory. Drones 6(3). https:\/\/doi.org\/10.3390\/drones6030077","DOI":"10.3390\/drones6030077"},{"key":"5058_CR14","doi-asserted-by":"publisher","unstructured":"Xue L, Zhou R, Ran H (2022) Air combat decision based on genetic fuzzy tree. In: Yan L, Duan H, Yu X (eds) Advances in Guidance, Navigation and Control. Springer Singapore, Singapore, pp 5515\u20135525, https:\/\/doi.org\/10.1007\/978-981-15-8155-7_456","DOI":"10.1007\/978-981-15-8155-7_456"},{"issue":"4","key":"5058_CR15","doi-asserted-by":"publisher","first-page":"2457","DOI":"10.1007\/s10462-018-9621-7","volume":"52","author":"GA Samigulina","year":"2019","unstructured":"Samigulina GA, Samigulina ZI (2019) Modified immune network algorithm based on the random forest approach for the complex objects control. Artif Intell Rev 52(4):2457\u20132473. https:\/\/doi.org\/10.1007\/s10462-018-9621-7","journal-title":"Artif Intell Rev"},{"key":"5058_CR16","doi-asserted-by":"publisher","first-page":"16532","DOI":"10.1109\/ACCESS.2021.3051971","volume":"9","author":"VF Yu","year":"2021","unstructured":"Yu VF, Qiu M, Pan H et al (2021) An improved immunoglobulin-based artificial immune system for the aircraft scheduling problem with alternate aircrafts. IEEE Access 9:16532\u201316545. https:\/\/doi.org\/10.1109\/ACCESS.2021.3051971","journal-title":"IEEE Access"},{"key":"5058_CR17","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.neucom.2019.10.115","volume":"408","author":"X Xu","year":"2020","unstructured":"Xu X, Duan H, Guo Y et al (2020) A cascade adaboost and cnn algorithm for drogue detection in uav autonomous aerial refueling. Neurocomputing 408:121\u2013134. https:\/\/doi.org\/10.1016\/j.neucom.2019.10.115","journal-title":"Neurocomputing"},{"key":"5058_CR18","doi-asserted-by":"publisher","first-page":"5793","DOI":"10.1007\/s10489-020-02065-9","volume":"51","author":"H Jiang","year":"2021","unstructured":"Jiang H, Shi D, Xue C et al (2021) Multi-agent deep reinforcement learning with type-based hierarchical group communication. Appl Intell 51:5793\u20135808. https:\/\/doi.org\/10.1007\/s10489-020-02065-9","journal-title":"Appl Intell"},{"issue":"12","key":"5058_CR19","doi-asserted-by":"publisher","first-page":"14101","DOI":"10.1007\/s10489-022-03254-4","volume":"52","author":"A Puente-Castro","year":"2022","unstructured":"Puente-Castro A, Rivero D, Pazos A et al (2022) Uav swarm path planning with reinforcement learning for field prospecting. Appl Intell 52(12):14101\u201314118. https:\/\/doi.org\/10.1007\/s10489-022-03254-4","journal-title":"Appl Intell"},{"key":"5058_CR20","doi-asserted-by":"publisher","unstructured":"Wu K, Yang Y, Liu Q et al (2023a) Hierarchical independent coding scheme for varifocal multiview images based on angular-focal joint prediction. IEEE Trans Multimed 1\u201313. https:\/\/doi.org\/10.1109\/TMM.2023.3306072","DOI":"10.1109\/TMM.2023.3306072"},{"key":"5058_CR21","doi-asserted-by":"publisher","first-page":"3975","DOI":"10.1109\/TMM.2022.3169055","volume":"25","author":"K Wu","year":"2023","unstructured":"Wu K, Yang Y, Liu Q et al (2023b) Focal stack image compression based on basis-quadtree representation. IEEE Trans Multimed 25:3975\u20133988. https:\/\/doi.org\/10.1109\/TMM.2022.3169055","journal-title":"IEEE Trans Multimed"},{"issue":"7","key":"5058_CR22","doi-asserted-by":"publisher","first-page":"11659","DOI":"10.1364\/OE.482141","volume":"31","author":"K Wu","year":"2023","unstructured":"Wu K, Liu Q, Wang Y et al (2023c) End-to-end varifocal multiview images coding framework from data acquisition end to vision application end. Optics Express 31(7):11659\u201311679. https:\/\/doi.org\/10.1364\/OE.482141","journal-title":"Optics Express"},{"key":"5058_CR23","doi-asserted-by":"publisher","first-page":"104112","DOI":"10.1016\/j.engappai.2020.104112","volume":"98","author":"Z Sun","year":"2021","unstructured":"Sun Z, Piao H, Yang Z et al (2021) Multi-agent hierarchical policy gradient for air combat tactics emergence via self-play. Eng Appl Artif Intell 98:104112. https:\/\/doi.org\/10.1016\/j.engappai.2020.104112","journal-title":"Eng Appl Artif Intell"},{"key":"5058_CR24","doi-asserted-by":"publisher","first-page":"104767","DOI":"10.1016\/j.engappai.2022.104767","volume":"111","author":"D Hu","year":"2022","unstructured":"Hu D, Yang R, Zhang Y et al (2022) Aerial combat maneuvering policy learning based on confrontation demonstrations and dynamic quality replay. Eng Appl Artif Intell 111:104767. https:\/\/doi.org\/10.1016\/j.engappai.2022.104767","journal-title":"Eng Appl Artif Intell"},{"key":"5058_CR25","doi-asserted-by":"publisher","unstructured":"Wang B, Li S, Gao X et al (2022) Weighted mean field reinforcement learning for large-scale uav swarm confrontation. Appl Intell 1\u201316. https:\/\/doi.org\/10.1007\/s10489-022-03840-6","DOI":"10.1007\/s10489-022-03840-6"},{"key":"5058_CR26","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1109\/ACCESS.2019.2961426","volume":"8","author":"Q Yang","year":"2019","unstructured":"Yang Q, Zhang J, Shi G et al (2019) Maneuver decision of uav in short-range air combat based on deep reinforcement learning. IEEE Access 8:363\u2013378. https:\/\/doi.org\/10.1109\/ACCESS.2019.2961426","journal-title":"IEEE Access"},{"issue":"6","key":"5058_CR27","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.23919\/JSEE.2021.000121","volume":"32","author":"Z Jiandong","year":"2021","unstructured":"Jiandong Z, Qiming Y, Guoqing S et al (2021) Uav cooperative air combat maneuver decision based on multi-agent reinforcement learning. J Syst Eng Electron 32(6):1421\u20131438. https:\/\/doi.org\/10.23919\/JSEE.2021.000121","journal-title":"J Syst Eng Electron"},{"key":"5058_CR28","doi-asserted-by":"publisher","first-page":"92426","DOI":"10.1109\/ACCESS.2022.3202918","volume":"10","author":"J Xianyong","year":"2022","unstructured":"Xianyong J, Hou M, Wu G et al (2022) Research on maneuvering decision algorithm based on improved deep deterministic policy gradient. IEEE Access 10:92426\u201392445. https:\/\/doi.org\/10.1109\/ACCESS.2022.3202918","journal-title":"IEEE Access"},{"key":"5058_CR29","doi-asserted-by":"publisher","first-page":"26427","DOI":"10.1109\/ACCESS.2023.3257849","volume":"11","author":"JH Bae","year":"2023","unstructured":"Bae JH, Jung H, Kim S et al (2023) Deep reinforcement learning-based air-to-air combat maneuver generation in a realistic environment. IEEE Access 11:26427\u201326440. https:\/\/doi.org\/10.1109\/ACCESS.2023.3257849","journal-title":"IEEE Access"},{"key":"5058_CR30","doi-asserted-by":"publisher","unstructured":"Pope AP, Ide JS, Mi\u0107ovi\u0107 D et\u00a0al (2021) Hierarchical reinforcement learning for air-to-air combat. In: 2021 international conference on unmanned aircraft systems (ICUAS), IEEE, pp 275\u2013284, https:\/\/doi.org\/10.1109\/ICUAS51884.2021.9476700","DOI":"10.1109\/ICUAS51884.2021.9476700"},{"key":"5058_CR31","doi-asserted-by":"publisher","unstructured":"Yuan W, Xiwen Z, Rong Z et\u00a0al (2022) Research on ucav maneuvering decision method based on heuristic reinforcement learning. Comput Intell Neurosci 2022. https:\/\/doi.org\/10.1155\/2022\/1477078","DOI":"10.1155\/2022\/1477078"},{"issue":"1","key":"5058_CR32","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1049\/cit2.12109","volume":"8","author":"B Li","year":"2023","unstructured":"Li B, Huang J, Bai S et al (2023) Autonomous air combat decision-making of uav based on parallel self-play reinforcement learning. CAAI Trans Intell Technol 8(1):64\u201381. https:\/\/doi.org\/10.1049\/cit2.12109","journal-title":"CAAI Trans Intell Technol"},{"issue":"2","key":"5058_CR33","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1093\/jcde\/qwad020","volume":"10","author":"Kong Wr","year":"2023","unstructured":"Wr Kong, Dy Zhou, Zhou Y et al (2023) Hierarchical reinforcement learning from competitive self-play for dual-aircraft formation air combat. J Comput Des Eng 10(2):830\u2013859. https:\/\/doi.org\/10.1093\/jcde\/qwad020","journal-title":"J Comput Des Eng"},{"issue":"3","key":"5058_CR34","doi-asserted-by":"publisher","first-page":"467","DOI":"10.3390\/electronics11030467","volume":"11","author":"J Hu","year":"2022","unstructured":"Hu J, Wang L, Hu T et al (2022) Autonomous maneuver decision making of dual-uav cooperative air combat based on deep reinforcement learning. Electronics 11(3):467. https:\/\/doi.org\/10.3390\/electronics11030467","journal-title":"Electronics"},{"key":"5058_CR35","doi-asserted-by":"publisher","unstructured":"Cao Y, Kou YX, Li ZW et al (2023) Autonomous maneuver decision of ucav air combat based on double deep q network algorithm and stochastic game theory. Int J Aerosp Eng 2023. https:\/\/doi.org\/10.1155\/2023\/3657814","DOI":"10.1155\/2023\/3657814"},{"key":"5058_CR36","doi-asserted-by":"publisher","first-page":"106358","DOI":"10.1016\/j.engappai.2023.106358","volume":"123","author":"F Jiang","year":"2023","unstructured":"Jiang F, Xu M, Li Y et al (2023) Short-range air combat maneuver decision of uav swarm based on multi-agent transformer introducing virtual objects. Eng Appl Artif Intell 123:106358. https:\/\/doi.org\/10.1016\/j.engappai.2023.106358","journal-title":"Eng Appl Artif Intell"},{"key":"5058_CR37","doi-asserted-by":"publisher","first-page":"996412","DOI":"10.3389\/fnbot.2022.996412","volume":"16","author":"H Zhang","year":"2022","unstructured":"Zhang H, Zhou H, Wei Y et al (2022) Autonomous maneuver decision-making method based on reinforcement learning and monte carlo tree search. Front Neurorobot 16:996412. https:\/\/doi.org\/10.3389\/fnbot.2022.996412","journal-title":"Front Neurorobot"},{"key":"5058_CR38","volume-title":"Reinforcement learning: An introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton RS (2018) Reinforcement learning: An introduction. MIT press"},{"key":"5058_CR39","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"G Van Houdt","year":"2020","unstructured":"Van Houdt G, Mosquera C, N\u00e1poles G (2020) A review on the long short-term memory model. Artif Intell Rev 53:5929\u20135955. https:\/\/doi.org\/10.1007\/s10462-020-09838-1","journal-title":"Artif Intell Rev"},{"key":"5058_CR40","doi-asserted-by":"publisher","unstructured":"Austin F, Carbone G, Falco M, et\u00a0al (1987) Automated maneuvering decisions for air-to-air combat. In: Guidance, navigation and control conference, p 2393, https:\/\/doi.org\/10.2514\/6.1987-2393","DOI":"10.2514\/6.1987-2393"},{"issue":"12","key":"5058_CR41","doi-asserted-by":"publisher","first-page":"15372","DOI":"10.1007\/s10489-022-04281-x","volume":"53","author":"X Yu","year":"2023","unstructured":"Yu X, Wang Y, Qin J et al (2023) A q-based policy gradient optimization approach for doudizhu. Appl Intell 53(12):15372\u201315389. https:\/\/doi.org\/10.1007\/s10489-022-04281-x","journal-title":"Appl Intell"},{"issue":"4","key":"5058_CR42","doi-asserted-by":"publisher","first-page":"4801","DOI":"10.1007\/s10489-022-03844-2","volume":"53","author":"X Li","year":"2023","unstructured":"Li X, Xiao J, Cheng Y et al (2023) An actor-critic learning framework based on lyapunov stability for automatic assembly. Appl Intell 53(4):4801\u20134812. https:\/\/doi.org\/10.1007\/s10489-022-03844-2","journal-title":"Appl Intell"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05058-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05058-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05058-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T14:26:36Z","timestamp":1701267996000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05058-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":42,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["5058"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05058-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"26 September 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Ethical and informed consent for data used not applicable to this article as no datasets were used during the current study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}