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Titlebook: NASA Formal Methods; 16th International S Nathaniel Benz,Divya Gopinath,Nija Shi Conference proceedings 2024 The Editor(s) (if applicable)

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樓主: 積聚
21#
發(fā)表于 2025-3-25 05:33:30 | 只看該作者
22#
發(fā)表于 2025-3-25 08:57:41 | 只看該作者
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發(fā)表于 2025-3-25 12:56:38 | 只看該作者
24#
發(fā)表于 2025-3-25 19:31:22 | 只看該作者
25#
發(fā)表于 2025-3-25 20:11:54 | 只看該作者
Verification of?Scapegoat Trees Using Dafnyementation. Through Dafny’s powerful specification and verification techniques, we prove the correctness of its core operations within our chosen implementation. We also summarized our user experience with Dafny, presenting several techniques that can enhance the efficiency of the proof process.
26#
發(fā)表于 2025-3-26 01:29:46 | 只看該作者
Real Arithmetic in?,?d implement them in the .?Proof Manager. The latter generates assertions in .?and directs them to a selected backend (currently the .?SMT solver, which supports the theory of nonlinear real arithmetic). We motivate this functionality with problems arising in safety verification.
27#
發(fā)表于 2025-3-26 05:52:17 | 只看該作者
28#
發(fā)表于 2025-3-26 11:28:49 | 只看該作者
Symmetry-Based Abstraction Algorithm for?Accelerating Symbolic Control Synthesiss layer of abstraction to guide the order by which actions are explored during synthesis over the grid-based abstraction. We demonstrate the potential of our algorithm by synthesizing a reach-avoid controller for a 3-dimensional ship model with translation and rotation symmetries in the special Euclidean group SE(2) (Code is available at: .).
29#
發(fā)表于 2025-3-26 14:10:15 | 只看該作者
Towards Formal Verification of?Neural Networks in?Cyber-Physical Systemsification System theorem prover to model neural networks based on non-linear activation functions and prove the safety bounds of the output under safety-critical conditions. Finally, we show the application of the proposed approach to a model-predictive controller for autonomous driving.
30#
發(fā)表于 2025-3-26 18:39:24 | 只看該作者
Quantitative Input Usage Static Analysis underlying abstract backward analyzer and computes a sound over-approximation of the impact of program inputs, providing valuable insights into how the analyzed program handles them. We implement a proof-of-concept static analyzer to demonstrate potential applications.
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