arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.09181cs.SEcs.AIcs.LGstat.ML

Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges

MIT-IBM Watson AI Lab(MIT-IBM沃森人工智能实验室);IBM Research(IBM研究院);Massachusetts Institute of Technology(麻省理工学院)

Nathan Fulton, Nathan Hunt, Nghia Hoang, Subhro Das

英文摘要:

Autonomous systems -- such as self-driving cars, autonomous drones, and automated trains -- must come with strong safety guarantees. Over the past decade, techniques based on formal methods have enjoyed some success in providing strong correctness guarantees for large software systems including operating system kernels, cryptographic protocols, and control software for drones. These successes suggest it might be possible to ensure the safety of autonomous systems by constructing formal, computer-checked correctness proofs. This paper identifies three assumptions underlying existing formal verification techniques, explains how each of these assumptions limits the applicability of verification in autonomous systems, and summarizes preliminary work toward improving the strength of evidence provided by formal verification.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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