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SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning., , , , , , , , , and 1 other author(s). ICAART (1), page 28-37. SCITEPRESS, (2021)Specification Aware Multi-Agent Reinforcement Learning., , , , , , , , , and 1 other author(s). ICAART (Revised Selected Papers), volume 13251 of Lecture Notes in Computer Science, page 3-21. Springer, (2021)Learning and Testing Resilience in Cooperative Multi-Agent Systems., , , , , , , , , and 1 other author(s). AAMAS, page 1055-1063. International Foundation for Autonomous Agents and Multiagent Systems, (2020)SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning., , , , , , , , , and 1 other author(s). CoRR, (2020)Towards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain., , , and . CoRR, (2023)Toward a safe MLOps process for the continuous development and safety assurance of ML-based systems in the railway domain., , , and . AI Ethics, 4 (1): 123-130 (February 2024)Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm., , , , , , , and . CoRR, (2019)Capturing Dependencies Within Machine Learning via a Formal Process Model., , , , , , , , , and . ISoLA (3), volume 13703 of Lecture Notes in Computer Science, page 249-265. Springer, (2022)Fuzzy-based Support for Service Composition in Mobile Ad Hoc Networks., , , and . ICPS, page 379-384. IEEE Computer Society, (2007)The scenario coevolution paradigm: adaptive quality assurance for adaptive systems., , , , , , , , , and 3 other author(s). Int. J. Softw. Tools Technol. Transf., 22 (4): 457-476 (2020)