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Titlebook: NASA Formal Methods; 15th International S Kristin Yvonne Rozier,Swarat Chaudhuri Conference proceedings 2023 The Editor(s) (if applicable)

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書目名稱NASA Formal Methods
副標(biāo)題15th International S
編輯Kristin Yvonne Rozier,Swarat Chaudhuri
視頻videohttp://file.papertrans.cn/661/660025/660025.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: NASA Formal Methods; 15th International S Kristin Yvonne Rozier,Swarat Chaudhuri Conference proceedings 2023 The Editor(s) (if applicable)
描述.This book constitutes the proceedings of the 15th International Symposium on NASA Formal Methods, NFM 2023, held in Houston, Texas, USA, during May 16-18, 2023. ..The 26 full and 3 short papers presented in this volume were carefully reviewed and selected from 75 submissions. .The papers deal with advances in formal methods, formal methods techniques, and formal methods in practice. ?.
出版日期Conference proceedings 2023
關(guān)鍵詞reliability; validation; verification; model checking; coverage metrics; equivalence checking; semi-formal
版次1
doihttps://doi.org/10.1007/978-3-031-33170-1
isbn_softcover978-3-031-33169-5
isbn_ebook978-3-031-33170-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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0302-9743 ring May 16-18, 2023. ..The 26 full and 3 short papers presented in this volume were carefully reviewed and selected from 75 submissions. .The papers deal with advances in formal methods, formal methods techniques, and formal methods in practice. ?.978-3-031-33169-5978-3-031-33170-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
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https://doi.org/10.1007/978-3-031-33170-1reliability; validation; verification; model checking; coverage metrics; equivalence checking; semi-formal
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,Reward Shaping from?Hybrid Systems Models in?Reinforcement Learning,einforcement learning more tightly by generating safety-oriented aspects of reward functions from verified hybrid systems models. We demonstrate the approach on a standard reinforcement learning environment for longitudinal vehicle control.
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