{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:58:09Z","timestamp":1765961889750,"version":"3.44.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Natural Science Foundation of China under Grant","award":["62303467"],"award-info":[{"award-number":["62303467"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s40747-025-01938-1","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T07:01:56Z","timestamp":1754031716000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Logic-optimization behavior tree algorithm for enhanced autonomous underwater vehicle cooperative decision-making"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7698-3037","authenticated-orcid":false,"given":"Dan","family":"Yu","sequence":"first","affiliation":[]},{"given":"Changjian","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"1938_CR1","doi-asserted-by":"crossref","unstructured":"Sprague CI, \u00d6zkahraman \u00d6, Munafo A, Marlow R, Phillips A, \u00d6gren P (2018) Improving the modularity of auv control systems using behaviour trees. In: 2018 IEEE\/OES Autonomous Underwater Vehicle Workshop (AUV), pp. 1\u20136. IEEE","DOI":"10.1109\/AUV.2018.8729810"},{"issue":"4","key":"1938_CR2","doi-asserted-by":"publisher","first-page":"8815","DOI":"10.1109\/LRA.2022.3188902","volume":"7","author":"S Bhat","year":"2022","unstructured":"Bhat S, Lyons JB, Shi C, Yang XJ (2022) Clustering trust dynamics in a human-robot sequential decision-making task. IEEE Robot Auto Lett 7(4):8815\u20138822","journal-title":"IEEE Robot Auto Lett"},{"key":"1938_CR3","doi-asserted-by":"publisher","first-page":"59","DOI":"10.3389\/frobt.2019.00059","volume":"6","author":"M Birattari","year":"2019","unstructured":"Birattari M, Ligot A, Bozhinoski D, Brambilla M, Francesca G, Garattoni L, Garz\u00f3n Ramos D, Hasselmann K, Kegeleirs M, Kuckling J et al (2019) Automatic off-line design of robot swarms: a manifesto. Front. Robot. AI 6:59","journal-title":"Front. Robot. AI"},{"issue":"3","key":"1938_CR4","doi-asserted-by":"publisher","first-page":"4085","DOI":"10.1109\/TVT.2022.3220856","volume":"72","author":"W Wei","year":"2022","unstructured":"Wei W, Wang J, Fang Z, Chen J, Ren Y, Dong Y (2022) 3u: Joint design of uav\u2013usv\u2013uuv networks for cooperative target hunting. IEEE Trans. Vehicular Technol. 72(3):4085\u20134090","journal-title":"IEEE Trans. Vehicular Technol."},{"key":"1938_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2022.104096","volume":"154","author":"M Iovino","year":"2022","unstructured":"Iovino M, Scukins E, Styrud J, \u00d6gren P, Smith C (2022) A survey of behavior trees in robotics and ai. Robot Auto Syst 154:104096","journal-title":"Robot Auto Syst"},{"issue":"3","key":"1938_CR6","doi-asserted-by":"publisher","first-page":"5397","DOI":"10.1109\/LRA.2021.3074337","volume":"6","author":"O Biggar","year":"2021","unstructured":"Biggar O, Zamani M, Shames I (2021) An expressiveness hierarchy of behavior trees and related architectures. IEEE Robot Auto Lett 6(3):5397\u20135404","journal-title":"IEEE Robot Auto Lett"},{"issue":"3","key":"1938_CR7","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1109\/LRA.2021.3087442","volume":"6","author":"M Colledanchise","year":"2021","unstructured":"Colledanchise M, Natale L (2021) On the implementation of behavior trees in robotics. IEEE Robot Auto Lett 6(3):5929\u20135936","journal-title":"IEEE Robot Auto Lett"},{"key":"1938_CR8","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1613\/jair.1.11592","volume":"65","author":"D Zhang","year":"2019","unstructured":"Zhang D, Hannaford B (2019) Ikbt: Solving symbolic inverse kinematics with behavior tree. J Artificial Intell Res 65:457\u2013486","journal-title":"J Artificial Intell Res"},{"key":"1938_CR9","doi-asserted-by":"crossref","unstructured":"Venkata SSO, Parasuraman R, Pidaparti R (2023) Kt-bt: A framework for knowledge transfer through behavior trees in multirobot systems. IEEE Transactions on Robotics","DOI":"10.1109\/TRO.2023.3290449"},{"key":"1938_CR10","doi-asserted-by":"crossref","unstructured":"\u00d6zkahraman \u00d6, \u00d6gren P (2020) Combining control barrier functions and behavior trees for multi-agent underwater coverage missions. In: 2020 59th IEEE Conference on Decision and Control (CDC), pp. 5275\u20135282. IEEE","DOI":"10.1109\/CDC42340.2020.9304151"},{"issue":"12","key":"1938_CR11","doi-asserted-by":"publisher","first-page":"7968","DOI":"10.1109\/TII.2021.3059676","volume":"17","author":"H Hu","year":"2021","unstructured":"Hu H, Jia X, Liu K, Sun B (2021) Self-adaptive traffic control model with behavior trees and reinforcement learning for agv in industry 4.0. IEEE Trans Ind Inform 17(12):7968\u20137979","journal-title":"IEEE Trans Ind Inform"},{"issue":"4","key":"1938_CR12","doi-asserted-by":"publisher","first-page":"357","DOI":"10.23919\/CSMS.2023.0017","volume":"3","author":"S Yang","year":"2023","unstructured":"Yang S, Zhang Q (2023) Towards efficient robotic software development by reusing behavior tree structures for task planning paradigms. Complex Syst Model Simul 3(4):357\u2013380","journal-title":"Complex Syst Model Simul"},{"issue":"5","key":"1938_CR13","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.robot.2008.10.024","volume":"57","author":"BD Argall","year":"2009","unstructured":"Argall BD, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstration. Robot Auto Syst 57(5):469\u2013483","journal-title":"Robot Auto Syst"},{"issue":"6","key":"1938_CR14","doi-asserted-by":"publisher","first-page":"3534","DOI":"10.1109\/LRA.2023.3268598","volume":"8","author":"S Gugliermo","year":"2023","unstructured":"Gugliermo S, Schaffernicht E, Koniaris C, Pecora F (2023) Learning behavior trees from planning experts using decision tree and logic factorization. IEEE Robot Auto Lett 8(6):3534\u20133541","journal-title":"IEEE Robot Auto Lett"},{"issue":"2","key":"1938_CR15","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/TRO.2016.2633567","volume":"33","author":"M Colledanchise","year":"2016","unstructured":"Colledanchise M, \u00d6gren P (2016) How behavior trees modularize hybrid control systems and generalize sequential behavior compositions, the subsumption architecture, and decision trees. IEEE Trans Robot 33(2):372\u2013389","journal-title":"IEEE Trans Robot"},{"issue":"1","key":"1938_CR16","doi-asserted-by":"publisher","first-page":"19277","DOI":"10.1038\/s41598-022-23875-6","volume":"12","author":"Y Zhu","year":"2022","unstructured":"Zhu Y, Filipov ET (2022) Harnessing interpretable machine learning for holistic inverse design of origami. Sci Rep 12(1):19277","journal-title":"Sci Rep"},{"issue":"1","key":"1938_CR17","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/TKDE.2009.175","volume":"23","author":"S Tsang","year":"2009","unstructured":"Tsang S, Kao B, Yip KY, Ho W-S, Lee SD (2009) Decision trees for uncertain data. IEEE Trans Knowl Data Eng 23(1):64\u201378","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1938_CR18","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1109\/JOE.2022.3177858","volume":"47","author":"D Wei","year":"2022","unstructured":"Wei D, Ma H, Yu H, Dai X, Wang G, Peng B (2022) A hyperheuristic algorithm based on evolutionary strategy for complex mission planning of auvs in marine environment. IEEE J Oceanic Eng 47(4):936\u2013949","journal-title":"IEEE J Oceanic Eng"},{"key":"1938_CR19","doi-asserted-by":"crossref","unstructured":"Aksjonov A, Kyrki V (2021) Rule-based decision-making system for autonomous vehicles at intersections with mixed traffic environment. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 660\u2013666. IEEE","DOI":"10.1109\/ITSC48978.2021.9565085"},{"issue":"1","key":"1938_CR20","first-page":"206","volume":"237","author":"Y Wang","year":"2023","unstructured":"Wang Y, Wang Y, Zhang T, Yao F (2023) Bayesian network-based security decision-making method for the full-ocean-depth \u201cwukong\u2019\u2019auv. Proc Inst Mech Eng Part MEnvironment 237(1):206\u2013214","journal-title":"Proc Inst Mech Eng Part MEnvironment"},{"issue":"19","key":"1938_CR21","doi-asserted-by":"publisher","first-page":"3097","DOI":"10.3390\/electronics11193097","volume":"11","author":"L Guo","year":"2022","unstructured":"Guo L, Ma L, Zhang H, Yang J, Cheng Z, Jiang W (2022) An unmanned underwater vehicle torpedoes attack behavior autonomous decision-making method based on model fusion. Electronics 11(19):3097","journal-title":"Electronics"},{"issue":"3","key":"1938_CR22","doi-asserted-by":"publisher","first-page":"383","DOI":"10.3390\/jmse10030383","volume":"10","author":"Y Mao","year":"2022","unstructured":"Mao Y, Gao F, Zhang Q, Yang Z (2022) An auv target-tracking method combining imitation learning and deep reinforcement learning. J Mar Sci Eng 10(3):383","journal-title":"J Mar Sci Eng"},{"key":"1938_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.109794","volume":"239","author":"J Xu","year":"2021","unstructured":"Xu J, Huang F, Wu D, Cui Y, Yan Z, Zhang K (2021) Deep reinforcement learning based multi-auvs cooperative decision-making for attack-defense confrontation missions. Ocean Eng 239:109794","journal-title":"Ocean Eng"},{"issue":"6","key":"1938_CR24","doi-asserted-by":"publisher","first-page":"4015","DOI":"10.1109\/TCYB.2022.3225106","volume":"53","author":"L Liu","year":"2022","unstructured":"Liu L, Zhang S, Zhang L, Pan G, Yu J (2022) Multi-uuv maneuvering counter-game for dynamic target scenario based on fractional-order recurrent neural network. IEEE Trans Cybernet 53(6):4015\u20134028","journal-title":"IEEE Trans Cybernet"},{"issue":"11","key":"1938_CR25","first-page":"13953","volume":"72","author":"Y Hou","year":"2023","unstructured":"Hou Y, Liang X, Zhang J, Lv M, Yang A (2023) Hierarchical decision-making framework for multiple ucavs autonomous confrontation. IEEE Trans Vehicular Technol 72(11):13953\u201313968","journal-title":"IEEE Trans Vehicular Technol"},{"key":"1938_CR26","doi-asserted-by":"crossref","unstructured":"Tavafi A, Banzhaf W (2017) A hybrid genetic programming decision making system for robocup soccer simulation. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1025\u20131032","DOI":"10.1145\/3071178.3071194"},{"key":"1938_CR27","doi-asserted-by":"crossref","unstructured":"Svec P, Schwartz M, Thakur A, Anand DK, Gupta SK (2010) A simulation based framework for discovering planning logic for autonomous unmanned surface vehicles. In: Engineering Systems Design and Analysis, vol. 49170, pp. 711\u2013720","DOI":"10.1115\/ESDA2010-24894"},{"issue":"6","key":"1938_CR28","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1109\/TLT.2022.3207979","volume":"15","author":"J Smith","year":"2022","unstructured":"Smith J, Musharraf M, Veitch B, Khan F (2022) Pilot study using decision trees to diagnose the efficacy of virtual offshore egress training. IEEE Trans Learn Technol 15(6):812\u2013826","journal-title":"IEEE Trans Learn Technol"},{"key":"1938_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2018.06.007","volume":"194","author":"M Musharraf","year":"2020","unstructured":"Musharraf M, Smith J, Khan F, Veitch B (2020) Identifying route selection strategies in offshore emergency situations using decision trees. Reliab Eng Syst Saf 194:106179","journal-title":"Reliab Eng Syst Saf"},{"issue":"4","key":"1938_CR30","doi-asserted-by":"publisher","first-page":"3482","DOI":"10.1016\/j.eswa.2009.10.040","volume":"37","author":"S Cho","year":"2010","unstructured":"Cho S, Hong H, Ha B-C (2010) A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the mahalanobis distance: For bankruptcy prediction. Expert Syst Appl 37(4):3482\u20133488","journal-title":"Expert Syst Appl"},{"key":"1938_CR31","doi-asserted-by":"publisher","first-page":"174830262311981","DOI":"10.1177\/17483026231198181","volume":"17","author":"F Aaboub","year":"2023","unstructured":"Aaboub F, Chamlal H, Ouaderhman T (2023) Statistical analysis of various splitting criteria for decision trees. J Algorithms Comput Technol 17:17483026231198180","journal-title":"J Algorithms Comput Technol"},{"issue":"1","key":"1938_CR32","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/S0165-0114(01)00152-X","volume":"127","author":"H-P Huang","year":"2002","unstructured":"Huang H-P, Liang C-C (2002) Strategy-based decision making of a soccer robot system using a real-time self-organizing fuzzy decision tree. Fuzzy Sets Syst 127(1):49\u201364","journal-title":"Fuzzy Sets Syst"},{"key":"1938_CR33","doi-asserted-by":"crossref","unstructured":"Li L, Chen Z, Wang H, Kan Z (2023) Fast task allocation of heterogeneous robots with temporal logic and inter-task constraints. IEEE Robotics and Automation Letters","DOI":"10.1109\/LRA.2023.3290531"},{"key":"1938_CR34","doi-asserted-by":"crossref","unstructured":"Sarker IH, Colman A, Han J, Khan AI, Abushark YB, Salah K (2020) Behavdt: A behavioral decision tree learning to build user-centric context-aware predictive model. Mobile Networks and Applications","DOI":"10.1007\/s11036-019-01443-z"},{"key":"1938_CR35","doi-asserted-by":"crossref","unstructured":"Marzinotto A, Colledanchise M, Smith C, \u00d6gren P (2014) Towards a unified behavior trees framework for robot control. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5420\u20135427. IEEE","DOI":"10.1109\/ICRA.2014.6907656"},{"key":"1938_CR36","doi-asserted-by":"crossref","unstructured":"Mayr M, Chatzilygeroudis K, Ahmad F, Nardi L, Krueger V (2021) Learning of parameters in behavior trees for movement skills. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7572\u20137579. IEEE","DOI":"10.1109\/IROS51168.2021.9636292"},{"issue":"2","key":"1938_CR37","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1109\/TG.2018.2816806","volume":"11","author":"M Colledanchise","year":"2018","unstructured":"Colledanchise M, Parasuraman R, \u00d6gren P (2018) Learning of behavior trees for autonomous agents. IEEE Trans Games 11(2):183\u2013189","journal-title":"IEEE Trans Games"},{"key":"1938_CR38","doi-asserted-by":"crossref","unstructured":"Li X, Li Y, Zhang J, Xu X, Liu D (2024) Embedding multi-agent reinforcement learning into behavior trees with unexpected interruptions. Complex & Intelligent Systems, 1\u201310","DOI":"10.1007\/s40747-023-01326-7"},{"key":"1938_CR39","unstructured":"Scheper K (2014) Behaviour trees for evolutionary robotics: reducing the reality gap. Artificial Life"},{"key":"1938_CR40","doi-asserted-by":"crossref","unstructured":"French K, Wu S, Pan T, Zhou Z, Jenkins OC (2019) Learning behavior trees from demonstration. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 7791\u20137797 . IEEE","DOI":"10.1109\/ICRA.2019.8794104"},{"key":"1938_CR41","doi-asserted-by":"publisher","first-page":"1891","DOI":"10.1109\/LCSYS.2021.3134453","volume":"6","author":"CI Sprague","year":"2021","unstructured":"Sprague CI, \u00d6gren P (2021) Continuous-time behavior trees as discontinuous dynamical systems. IEEE Control Syst Lett 6:1891\u20131896","journal-title":"IEEE Control Syst Lett"},{"issue":"1","key":"1938_CR42","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s10515-022-00328-y","volume":"29","author":"S Yang","year":"2022","unstructured":"Yang S, Mao X, Lu Y, Xu Y (2022) Towards a behavior tree-based robotic software architecture with adjoint observation schemes for robotic software development. Auto Softw Eng 29(1):31","journal-title":"Auto Softw Eng"},{"issue":"8","key":"1938_CR43","doi-asserted-by":"publisher","first-page":"5202","DOI":"10.1109\/TCYB.2022.3181054","volume":"53","author":"F Qin","year":"2022","unstructured":"Qin F, Xu D, Hannaford B, Hao T (2022) Object-agnostic vision measurement framework based on one-shot learning and behavior tree. IEEE Trans Cybernet 53(8):5202\u20135215","journal-title":"IEEE Trans Cybernet"},{"key":"1938_CR44","doi-asserted-by":"crossref","unstructured":"Sagredo-Olivenza I, G\u00f3mez-Mart\u00edn PP, G\u00f3mez-Mart\u00edn MA, Gonz\u00e1lez-Calero PA (2017) Trained behavior trees: Programming by demonstration to support ai game designers. IEEE Trans Games 11(1):5\u201314","DOI":"10.1109\/TG.2017.2771831"},{"key":"1938_CR45","doi-asserted-by":"crossref","unstructured":"Wathieu A, Groechel TR, Lee HJ, Kuo C, Matari\u0107 MJ (2022) Re: Bt-espresso: Improving interpretability and expressivity of behavior trees learned from robot demonstrations. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11518\u201311524","DOI":"10.1109\/ICRA46639.2022.9812046"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01938-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01938-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01938-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T10:42:42Z","timestamp":1757328162000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01938-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"references-count":45,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["1938"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01938-1","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,8,1]]},"assertion":[{"value":"22 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"406"}}