{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:48:18Z","timestamp":1771613298027,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819764686","type":"print"},{"value":"9789819764693","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-6469-3_1","type":"book-chapter","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T23:25:51Z","timestamp":1740871551000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["LLM Multi-agent Decision Optimization"],"prefix":"10.1007","author":[{"given":"J.","family":"de Curt\u00f2","sequence":"first","affiliation":[]},{"given":"I.","family":"de Zarz\u00e0","sequence":"additional","affiliation":[]},{"given":"Carlos T.","family":"Calafate","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"issue":"11","key":"1_CR1","doi-asserted-by":"publisher","first-page":"13677","DOI":"10.1007\/s10489-022-04105-y","volume":"53","author":"A Oroojlooy","year":"2023","unstructured":"Oroojlooy, A., Hajinezhad, D.: A review of cooperative multi-agent deep reinforcement learning. Appl. Intell. 53(11), 13677\u201313722 (2023)","journal-title":"Appl. Intell."},{"key":"1_CR2","first-page":"24611","volume":"35","author":"C Yu","year":"2022","unstructured":"Yu, C., Velu, A., Vinitsky, E., Gao, J., Wang, Y., Bayen, A., Wu, Y.: The surprising effectiveness of ppo in cooperative multi-agent games. Adv. Neural Inf. Process. Syst. 35, 24611\u201324624 (2022)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1_CR3","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the Advances in Neural Information Processing Systems 30, (NIPS 2017), Long Beach, CA, USA, 4\u20139 December 2017, pp. 5998\u20136008 (2017)"},{"key":"1_CR4","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning, Virtual, 18\u201324 July 2021 (2021)"},{"key":"1_CR5","unstructured":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Adv. Neural Inf. Process. Syst. 35, 27730\u201327744 (2022)"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"de Zarz\u00e0, I., de Curt\u00f2, J., Roig, G., Manzoni, P., Calafate, C.T.: Emergent cooperation and strategy adaptation in multi-agent systems: an extended coevolutionary theory with llms. Electronics 12, 2722 (2023)","DOI":"10.3390\/electronics12122722"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Van der Hoek, W., Wooldridge, M.: Multi-agent systems. In: Foundations of Artificial Intelligence, vol. 3, pp. 887\u2013928 (2008)","DOI":"10.1016\/S1574-6526(07)03024-6"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Dorri, A., Kanhere, S.S., Jurdak, R.: Multi-agent systems: a survey. IEEE. Access 6, 28573\u201328593 (2018)","DOI":"10.1109\/ACCESS.2018.2831228"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"De Curt\u00f2, J., de Zarz\u00e0, I., Roig, G., Cano, J.C., Manzoni, P., Calafate, C.T.: LLM-informed multi-armed bandit strategies for non-stationary environments. Electronics 12, 2814 (2023)","DOI":"10.3390\/electronics12132814"},{"issue":"1","key":"1_CR10","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s10586-022-03621-3","volume":"26","author":"A Javadpour","year":"2023","unstructured":"Javadpour, A., Pinto, P., Ja\u2019fari, F., Zhang, W.: DMAIDPS: a distributed multi-agent intrusion detection and prevention system for cloud IoT environments. Cluster Comput. 26(1), 367\u2013384 (2023)","journal-title":"Cluster Comput."},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Mehr, N., Wang, M., Bhatt, M., Schwager, M.: Maximum-entropy multi-agent dynamic games: forward and inverse solutions. IEEE Trans. Robot. (2023)","DOI":"10.1109\/TRO.2022.3232300"},{"key":"1_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2022.104335","volume":"161","author":"A Dahiya","year":"2023","unstructured":"Dahiya, A., Aroyo, A.M., Dautenhahn, K., Smith, S.L.: A survey of multiagent human\u2013robot interaction systems. Robot. Autonom. Syst. 161, 104335 (2023)","journal-title":"Robot. Autonom. Syst."},{"key":"1_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110083","volume":"259","author":"JD Zhang","year":"2023","unstructured":"Zhang, J.D., He, Z., Chan, W.H., Chow, C.Y.: DeepMAG: deep reinforcement learning with multi-agent graphs for? exible job shop scheduling. Knowl.-Based Syst. 259, 110083 (2023)","journal-title":"Knowl.-Based Syst."},{"key":"1_CR14","unstructured":"Schwartz, J., Kurniawati, H.: Bayes-adaptive Monte-Carlo planning for type-based reasoning in large partially observable, multi-agent environments. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multia-gent Systems (2023)"},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.ins.2022.11.062","volume":"619","author":"R Zhu","year":"2023","unstructured":"Zhu, R., Li, L., Wu, S., Lv, P., Li, Y., Xu, M.: Multi-agent broad reinforcement learning for intelligent traffic light control. Inform. Sci. 619, 509\u2013525 (2023)","journal-title":"Inform. Sci."},{"key":"1_CR16","unstructured":"Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., et al.: Metagpt: meta programming for multi-agent collaborative framework. arXiv:2308.00352 (2023)"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Costa, V.G., Pedreira, C.E.: Recent advances in decision trees: an updated survey. Artif. Intell. Rev. 56(5), 4765\u20134800 (2023)","DOI":"10.1007\/s10462-022-10275-5"},{"key":"1_CR18","unstructured":"Lehnert, L., Sukhbaatar, S., Mcvay, P., Rabbat, M., Tian, Y.D.: Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping. arXiv:2402.14083 (2024)"},{"key":"1_CR19","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.trb.2018.01.006","volume":"109","author":"N Boysen","year":"2018","unstructured":"Boysen, N., Briskorn, D., Schwerdfeger, S.: The identical-path truck platooning problem. Transp. Res. Part B: Methodol. 109, 26\u201339 (2018)","journal-title":"Transp. Res. Part B: Methodol."},{"issue":"1","key":"1_CR20","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/TIV.2016.2577499","volume":"1","author":"S Tsugawa","year":"2016","unstructured":"Tsugawa, S., Jeschke, S., Shladover, S.E.: A review of truck platooning projects for energy savings. IEEE Trans. Intell. Veh. 1(1), 68\u201377 (2016)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"de Zarz\u00e0, I., de Curt\u00f2, J., Roig, G., Calafate, C.T.: LLM adaptive PID control for B5G truck platooning systems. Sensors 23, 5899 (2023)","DOI":"10.3390\/s23135899"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Li, X., Qiao, L., Chen, B., Zheng, Y., Zhi, C., Zhang, S., et al.: Z. SSR markers development and their application in genetic diversity evaluation of garlic (Allium sativum) germplasm. Plant Divers. 44(5), 481\u2013491 (2022)","DOI":"10.1016\/j.pld.2021.08.001"},{"issue":"7972","key":"1_CR23","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/s41586-023-06221-2","volume":"620","author":"H Wang","year":"2023","unstructured":"Wang, H., et al.: Scientific discovery in the age of artificial intelligence. Nature 620(7972), 47\u201360 (2023)","journal-title":"Nature"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Hassoun, S., Jefferson, F., Shi, X., Stucky, B., Wang, J., Rosa, E.: Artificial intelligence for biology. Integr. Comp. Biol. 61(6), 2267\u20132275 (2021)","DOI":"10.1093\/icb\/icab188"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Mhlanga, D.: Industry 4.0 in finance: the impact of AI on digital financial inclusion. Int. J. Financ. Stud. 8, 45 (2020)","DOI":"10.3390\/ijfs8030045"},{"key":"1_CR26","unstructured":"Weber, P., Carl, K.V., Hinz, O.: Applications of explainable artificial intelligence in finance\u2014a systematic review of finance, information systems, and computer science literature. Manage. Rev. Quart. 1\u201341 (2023)"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Pallathadka, H., Ramirez-Asis, E.H., Loli-Poma, T.P., Kaliyaperumal, K., Ventayen, R.J.M., Naved, M.: Applications of artificial intelligence in business management. E-commer. Financ. Mater. Today: Proceed. 80, 2610\u20132613 (2023)","DOI":"10.1016\/j.matpr.2021.06.419"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"de Zarz\u00e0, I., de Curt\u00f2, J., Roig, G., Calafate, C.T.: Optimized financial planning: integrating individual and cooperative budgeting models with LLM recommendations. AI 5, 91\u2013114 (2024)","DOI":"10.3390\/ai5010006"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Minderer, M., Gritsenko, A., Stone, A., Neumann, M., Weis-senborn, D., Dosovitskiy, A., Mahendran, A., Arnab, A., De-hghani, M., Shen, Z., Wang, X., Zhai, X., Kipf, T., Houlsby, N.: Simple Open-Vocabulary Object Detection with Vision Transformers. arXiv:2205.06230 (2022)","DOI":"10.1007\/978-3-031-20080-9_42"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Minderer, M., Gritsenko, A., Houlsby, N.: Scaling Open-Vocabulary Object Detection. arXiv:2306.09683 (2023)","DOI":"10.1007\/978-3-031-20080-9_42"},{"key":"1_CR31","unstructured":"Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., Hashimoto, T.B.: Stanford alpaca: an instruction-following llama model (2023)"},{"key":"1_CR32","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Roziere., B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: open and efficient foundation language models. arXiv:2302.13971 (2023)"},{"key":"1_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N.A., Khashabi, D., Hajishirzi, H.: Self-instruct: aligning language model with self generated instructions. arXiv:2212.10560 (2022)","DOI":"10.18653\/v1\/2023.acl-long.754"},{"key":"1_CR34","unstructured":"Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv:2301.12597 (2023)"}],"container-title":["Smart Innovation, Systems and Technologies","Agents and Multi-agent Systems: Technologies and Applications 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-6469-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T23:26:01Z","timestamp":1740871561000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-6469-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819764686","9789819764693"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-6469-3_1","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"value":"2190-3018","type":"print"},{"value":"2190-3026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KES-AMSTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madeira","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"kesamsta2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/amsta-24.kesinternational.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}