{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T21:05:26Z","timestamp":1778015126655,"version":"3.51.4"},"reference-count":5,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:00:00Z","timestamp":1749081600000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Real-Time Syst"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Reinforcement Learning (RL) has emerged as a promising tool for decision-making in various applications, particularly in uncertain environments. While its adoption in embedded systems\u2014especially hard real-time systems\u2014faces challenges due to stringent timing constraints, integrating shielding mechanisms may offer a pathway for RL to optimize its scheduling decisions, preserving worst-case timing guarantees. This position paper shows a use case where RL selects compliant execution versions for fault-tolerant real-time systems while minimizing the system utilization in runtime. Furthermore, we discuss possible directions for further exploring RL\u2019s role in real-time systems for improved adaptability.<\/jats:p>","DOI":"10.1007\/s11241-025-09441-z","type":"journal-article","created":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T03:05:39Z","timestamp":1749092739000},"page":"306-310","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Shielded reinforcement learning for fault-tolerant scheduling in real-time systems"],"prefix":"10.1007","volume":"61","author":[{"given":"Junjie","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan-Hsun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,5]]},"reference":[{"key":"9441_CR1","doi-asserted-by":"crossref","unstructured":"Alshiekh M, Bloem R, Ehlers R, K\u00f6nighofer B, Niekum S, Topcu U (2018) Safe reinforcement learning via shielding. In: proceedings of the 32nd conference on artificial intelligence, (AAAI), 2669\u20132678","DOI":"10.1609\/aaai.v32i1.11797"},{"key":"9441_CR2","doi-asserted-by":"crossref","unstructured":"Bo Z, Qiao Y, Leng C, Wang H, Guo C, Zhang S (2021) Developing real-time scheduling policy by deep reinforcement learning. In: 27th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, 131\u2013142","DOI":"10.1109\/RTAS52030.2021.00019"},{"key":"9441_CR3","doi-asserted-by":"crossref","unstructured":"Liu M, Lu P, hen, X, Sokolsky O, Lee I, Kong F (2024) Deadline-safe reach-avoid control synthesis for cyber-physical systems with reinforcement learning. In: IEEE Real-Time Systems Symposium, RTSS, 96\u2013108","DOI":"10.1109\/RTSS62706.2024.00018"},{"key":"9441_CR4","doi-asserted-by":"crossref","unstructured":"Shi J, Ueter N, Chen J-J, Chen K-H (2023) Average task execution time minimization under (m, k) soft error constraint. In: 29th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, 1\u201313","DOI":"10.1109\/RTAS58335.2023.00008"},{"key":"9441_CR5","doi-asserted-by":"crossref","unstructured":"Huang S, Zeng J, Deng X, Wang S, Sifat AH, Bharmal B, Huang J, Williams RK, Zeng H, Jung C (2023) Rtailor: Parameterizing soft error resilience for mixed-criticality real-time systems. In: IEEE Real-Time Systems Symposium, RTSS, 344\u2013357","DOI":"10.1109\/RTSS59052.2023.00037"}],"container-title":["Real-Time Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11241-025-09441-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11241-025-09441-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11241-025-09441-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T19:14:38Z","timestamp":1753470878000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11241-025-09441-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":5,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["9441"],"URL":"https:\/\/doi.org\/10.1007\/s11241-025-09441-z","relation":{},"ISSN":["0922-6443","1573-1383"],"issn-type":[{"value":"0922-6443","type":"print"},{"value":"1573-1383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"9 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}