Toward verified artificial intelligence

Author:

Seshia Sanjit A.1,Sadigh Dorsa2,Sastry S. Shankar1

Affiliation:

1. University of California, Berkeley, CA

2. Stanford University, CA

Abstract

Making AI more trustworthy with a formal methods-based approach to AI system verification and validation.

Funder

NSF

Defense Advanced Research Projects Agency

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference40 articles.

1. Censi , A. , Slutsky , K. , Wongpiromsarn , T. , Yershov , D. , Pendleton , S. , Fu , J. , and Frazzoli , E . Liability, ethics, and culture-aware behavior specification using rulebooks . In 2019 Intern. Conf. on Robotics and Automation (ICRA), IEEE, 8536--8542 . Censi, A., Slutsky, K., Wongpiromsarn, T., Yershov, D., Pendleton, S., Fu, J., and Frazzoli, E. Liability, ethics, and culture-aware behavior specification using rulebooks. In 2019 Intern. Conf. on Robotics and Automation (ICRA), IEEE, 8536--8542.

2. Cohen , J.M. , Rosenfeld , E. , and Kolter , J.Z . Certified adversarial robustness via randomized smoothing . In Proceedings of the 36th Intern. Conf. on Machine Learning 97 ( 2019 ), 1310--1320. Cohen, J.M., Rosenfeld, E., and Kolter, J.Z. Certified adversarial robustness via randomized smoothing. In Proceedings of the 36th Intern. Conf. on Machine Learning 97 (2019), 1310--1320.

3. SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems

4. Rise of concerns about AI

5. Compositional falsification of cyber-physical systems with machine learning components. In Proceedings of the NASA Formal Methods Conf. (Lecture Notes;Dreossi T.;Computer Science),2017

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