What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866908056298717184 |
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| author | Bensalem, Saddek Cheng, Chih-Hong Huang, Wei Huang, Xiaowei Wu, Changshun Zhao, Xingyu |
| author_facet | Bensalem, Saddek Cheng, Chih-Hong Huang, Wei Huang, Xiaowei Wu, Changshun Zhao, Xingyu |
| contents | Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges. Among the challenges, it is known that a rigorous, yet practical, way of achieving safety guarantees is one of the most prominent. In this paper, we first discuss the engineering and research challenges associated with the design and verification of such systems. Then, based on the observation that existing works cannot actually achieve provable guarantees, we promote a two-step verification method for the ultimate achievement of provable statistical guarantees. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_11784 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems Bensalem, Saddek Cheng, Chih-Hong Huang, Wei Huang, Xiaowei Wu, Changshun Zhao, Xingyu Machine Learning Artificial Intelligence Software Engineering Systems and Control Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges. Among the challenges, it is known that a rigorous, yet practical, way of achieving safety guarantees is one of the most prominent. In this paper, we first discuss the engineering and research challenges associated with the design and verification of such systems. Then, based on the observation that existing works cannot actually achieve provable guarantees, we promote a two-step verification method for the ultimate achievement of provable statistical guarantees. |
| title | What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems |
| topic | Machine Learning Artificial Intelligence Software Engineering Systems and Control |
| url | https://arxiv.org/abs/2307.11784 |