{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:23:55Z","timestamp":1743020635408,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031460012"},{"type":"electronic","value":"9783031460029"}],"license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-46002-9_26","type":"book-chapter","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T16:02:36Z","timestamp":1702483356000},"page":"395-417","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating Distributed Component-Based Systems Through Deep Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Itay","family":"Cohen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Doron","family":"Peled","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"26_CR1","unstructured":"Brown, T.B., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6\u201312, 2020, virtual (2020)"},{"issue":"2","key":"26_CR2","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/TSMCC.2007.913919","volume":"38","author":"L Busoniu","year":"2008","unstructured":"Busoniu, L., Babuska, R., Schutter, B.D.: A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Syst. Man Cybern. Part C 38(2), 156\u2013172 (2008)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"26_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-68612-7","volume-title":"Introduction to Discrete Event Systems","author":"CG Cassandras","year":"2008","unstructured":"Cassandras, C.G., Lafortune, S.: Introduction to Discrete Event Systems, 2nd edn. Springer, Cham (2008). https:\/\/doi.org\/10.1007\/978-0-387-68612-7","edition":"2"},{"issue":"1\u20133","key":"26_CR4","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.scico.2004.05.014","volume":"55","author":"G G\u00f6\u00dfler","year":"2005","unstructured":"G\u00f6\u00dfler, G., Sifakis, J.: Composition for component-based modeling. Sci. Comput. Program. 55(1\u20133), 161\u2013183 (2005)","journal-title":"Sci. Comput. Program."},{"key":"26_CR5","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: International Conference on Machine Learning, pp. 1861\u20131870. PMLR (2018)"},{"issue":"8","key":"26_CR6","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1145\/359576.359585","volume":"21","author":"CAR Hoare","year":"1978","unstructured":"Hoare, C.A.R.: Communicating sequential processes. Commun. ACM 21(8), 666\u2013677 (1978)","journal-title":"Commun. ACM"},{"key":"26_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-030-61470-6_27","volume-title":"Leveraging Applications of Formal Methods, Verification and Validation: Engineering Principles","author":"S Iosti","year":"2020","unstructured":"Iosti, S., Peled, D., Aharon, K., Bensalem, S., Goldberg, Y.: Synthesizing control for a system with black box environment, based on deep learning. In: Margaria, T., Steffen, B. (eds.) ISoLA 2020. LNCS, vol. 12477, pp. 457\u2013472. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61470-6_27"},{"key":"26_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Kravaris, T., Vouros, G.A.: Deep multiagent reinforcement learning methods addressing the scalability challenge. Appl. Intell. (2023)","DOI":"10.5772\/intechopen.105627"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Lehmann, D., Rabin, M.O.: On the advantages of free choice: a symmetric and fully distributed solution to the dining philosophers problem. In: Proceedings of the 8th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, pp. 133\u2013138 (1981)","DOI":"10.1145\/567532.567547"},{"key":"26_CR11","unstructured":"Mnih, V., et al.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)"},{"key":"26_CR12","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, pp. 8024\u20138035. Curran Associates, Inc. (2019). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"26_CR13","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"key":"26_CR14","volume-title":"Reinforcement Learning - An Introduction. Adaptive Computation and Machine Learning","author":"RS Sutton","year":"2018","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction. Adaptive Computation and Machine Learning, 2nd edn. MIT Press, London (2018)","edition":"2"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Tan, M.: Multi-agent reinforcement learning: Independent versus cooperative agents. In: Utgoff, P.E. (ed.) Machine Learning, Proceedings of the Tenth International Conference, University of Massachusetts, Amherst, MA, USA, June 27\u201329, 1993, pp. 330\u2013337. Morgan Kaufmann (1993)","DOI":"10.1016\/B978-1-55860-307-3.50049-6"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, K., Yang, Z., Liu, H., Zhang, T., Basar, T.: Fully decentralized multi-agent reinforcement learning with networked agents. In: International Conference on Machine Learning, pp. 5872\u20135881. PMLR (2018)","DOI":"10.1109\/CDC.2018.8619581"}],"container-title":["Lecture Notes in Computer Science","Bridging the Gap Between AI and Reality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46002-9_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T16:06:51Z","timestamp":1702483611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46002-9_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"ISBN":["9783031460012","9783031460029"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46002-9_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"14 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AISoLA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bridging the Gap between AI and Reality","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Crete","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aisola2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023-aisola.isola-conference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}