{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:24:59Z","timestamp":1757622299817,"version":"3.44.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032006851"},{"type":"electronic","value":"9783032006868"}],"license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-00686-8_36","type":"book-chapter","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T22:07:02Z","timestamp":1754431622000},"page":"411-420","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating Functionalities to a System via Autoencoder Hippocampus Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3527-0030","authenticated-orcid":false,"given":"Siwei","family":"Luo","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"issue":"2","key":"36_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/S0896-6273(00)80773-4","volume":"23","author":"H Eichenbaum","year":"1999","unstructured":"Eichenbaum, H., Dudchenko, P., Wood, E., Shapiro, M., Tanila, H.: The hippocampus, memory, and place cells: is it spatial memory or a memory space? Neuron 23(2), 209\u2013226 (1999)","journal-title":"Neuron"},{"issue":"2","key":"36_CR2","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1038\/nrn730","volume":"3","author":"DJ Heeger","year":"2002","unstructured":"Heeger, D.J., Ress, D.: What does fMRI tell us about neuronal activity? Nat. Rev. Neurosci. 3(2), 142\u2013151 (2002)","journal-title":"Nat. Rev. Neurosci."},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Logothetis, Nikos K., Pauls, J., Augath, M., Trinath, T., Oeltermann., A : Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843), 150\u2013157(2001)","DOI":"10.1038\/35084005"},{"key":"36_CR4","unstructured":"Tuomas, H., Hartikainen, K., Abbeel, P., Levine, S.: Latent space policies for hierarchical reinforcement learning. In: International Conference on Machine Learning, pp. 1851\u20131860. PMLR (2018)"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Freek, S., Schaal, S.: Hierarchical reinforcement learning with movement primitives. In: 2011 11th IEEE-RAS International Conference on Humanoid Robots, pp. 231-238. IEEE (2011)","DOI":"10.1109\/Humanoids.2011.6100841"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G.: Hierarchical reinforcement learning with the MAXQ value function decomposition. J. Artif. Intell. Res. 13, 227\u2013303 (2000)","DOI":"10.1613\/jair.639"},{"key":"36_CR7","unstructured":"Peter, D., Hinton, G.E.: Feudal reinforcement learning. Adv. Neural Info. Process. Syst. 5 (1992)"},{"key":"36_CR8","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1023\/A:1019956318069","volume":"18","author":"R Vilalta","year":"2002","unstructured":"Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18, 77\u201395 (2002)","journal-title":"Artif. Intell. Rev."},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237\u2013285 (1996)","DOI":"10.1613\/jair.301"},{"issue":"3","key":"36_CR10","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1109\/TITS.2019.2901791","volume":"21","author":"T Chu","year":"2019","unstructured":"Chu, T., Wang, J., Codec\u00e0, L., Li, Z.: Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Trans. Intell. Transp. Syst. 21(3), 1086\u20131095 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"36_CR11","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/TNSE.2021.3068340","volume":"9","author":"J Du","year":"2021","unstructured":"Du, J., et al.: Resource pricing and allocation in MEC enabled blockchain systems: an A3C deep reinforcement learning approach. IEEE Trans. Netw .Sci. Eng. 9(1), 33\u201344 (2021)","journal-title":"IEEE Trans. Netw .Sci. Eng."},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Mazyavkina, N., Sviridov, S., Ivanov, S., Burnaev, E.: Reinforcement learning for combinatorial optimization: a survey. Comput. Oper. Res. 134, 105400 (2021)","DOI":"10.1016\/j.cor.2021.105400"},{"key":"36_CR13","unstructured":"Praveen, P.: Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning. Packt Publishing Ltd (2018)"},{"issue":"254","key":"36_CR14","first-page":"1","volume":"24","author":"A Serrano-Mu\u00f1oz","year":"2023","unstructured":"Serrano-Mu\u00f1oz, A., Chrysostomou, D., B\u00f8gh, S., Arana-Arexolaleiba, N.: SKRL: modular and flexible library for reinforcement learning. J. Mach. Learn. Res. 24(254), 1\u20139 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR15","unstructured":"Jacky, L., Makoviychuk, V., Handa, A., Chentanez, N., Macklin, M., Fox, D.: Gpu-accelerated robotic simulation for distributed reinforcement learning. In: Conference on Robot Learning, pp. 270-282. PMLR (2018)"},{"key":"36_CR16","unstructured":"Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. Vol. 1, no. 1. Cambridge: MIT press, 1998"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Carl, K.: PID control. IEEE Control Syst. Mag. 26(1), 30\u201331 (2006)","DOI":"10.1109\/MCS.2006.1580151"},{"issue":"1","key":"36_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF01818535","volume":"17","author":"R Bellman","year":"1978","unstructured":"Bellman, R., Lee, E.S.: Functional equations in dynamic programming. Aequationes Math. 17(1), 1\u201318 (1978). https:\/\/doi.org\/10.1007\/BF01818535","journal-title":"Aequationes Math."},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Shige, P.: A generalized dynamic programming principle and Hamilton-Jacobi-Bellman equation. Stochast. Int. J. Probab. Stoch. Process. 38(2), 119\u2013134 (1992)","DOI":"10.1080\/17442509208833749"},{"issue":"1","key":"36_CR20","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1287\/opre.50.1.48.17791","volume":"50","author":"S Dreyfus","year":"2002","unstructured":"Dreyfus, S.: Richard Bellman on the birth of dynamic programming. Oper. Res. 50(1), 48\u201351 (2002)","journal-title":"Oper. Res."},{"key":"36_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404, 132306 (2020)","journal-title":"Physica D"},{"key":"36_CR22","unstructured":"Yuan, G., Glowacka, D.: Deep gate recurrent neural network. In: Asian Conference on Machine Learning, pp. 350-365. PMLR (2016)"},{"issue":"8","key":"36_CR23","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","volume":"79","author":"JJ Hopfield","year":"1982","unstructured":"Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79(8), 2554\u20132558 (1982)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"36_CR24","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.neuroimage.2019.05.039","volume":"198","author":"K Han","year":"2019","unstructured":"Han, K., et al.: Variational autoencoder: an unsupervised model for encoding and decoding fMRI activity in visual cortex. Neuroimage 198, 125\u2013136 (2019)","journal-title":"Neuroimage"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Ricardo, M., Puentes, J., Uriza, L.F., Hoyos, M.H.: Single-slice Alzheimer\u2019s disease classification and disease regional analysis with supervised switching autoencoders. Comput. Biol. Med. 116, 103527 (2020)","DOI":"10.1016\/j.compbiomed.2019.103527"},{"key":"36_CR26","doi-asserted-by":"crossref","unstructured":"Junhai, Z., Zhang, S., Chen, J., He, Q.: Autoencoder and its various variants. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 415\u2013419. IEEE (2018)","DOI":"10.1109\/SMC.2018.00080"},{"key":"36_CR27","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57\u201381 (2020)","journal-title":"AI Open"},{"issue":"16","key":"36_CR28","doi-asserted-by":"publisher","first-page":"24138","DOI":"10.1007\/s11227-024-06383-4","volume":"80","author":"Z Zhang","year":"2024","unstructured":"Zhang, Z., Chen, X., Liu, K., Shaohua, X., Huang, L.: A resource optimization scheduling model and algorithm for heterogeneous computing clusters based on GNN and RL. J. Supercomput. 80(16), 24138\u201324172 (2024)","journal-title":"J. Supercomput."},{"key":"36_CR29","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.ins.2022.08.041","volume":"611","author":"Z Yao","year":"2022","unstructured":"Yao, Z., Jun, Yu., Zhang, J., He, W.: Graph and dynamics interpretation in robotic reinforcement learning task. Inf. Sci. 611, 317\u2013334 (2022)","journal-title":"Inf. Sci."},{"key":"36_CR30","unstructured":"Bojan, M., Alavi, Y., Chartrand, G., Oellermann, O.: The Laplacian spectrum of graphs. Graph Theory Comb. Appl. 2(12), 871\u2013898 (1991)"}],"container-title":["Lecture Notes in Computer Science","Artificial General Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-00686-8_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T18:33:37Z","timestamp":1757356417000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-00686-8_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"ISBN":["9783032006851","9783032006868"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-00686-8_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,6]]},"assertion":[{"value":"6 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial General Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Reykjavic","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iceland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2025","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":"agi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/agi-conf.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}