VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems. We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components. VERIFAI particularly seeks to address challenges with applying formal methods to perception and ML components, including those based on neural networks, and to model and analyze system behavior in the presence of environment uncertainty. We describe the initial version of VERIFAI which centers on simulation guided by formal models and specifications. Several use cases are illustrated with examples, including temporal-logic falsification, model-based systematic fuzz testing, parameter synthesis, counterexample analysis, and data set augmentation.
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References in zbMATH (referenced in 2 articles )
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- Zhao, Hengjun; Zeng, Xia; Chen, Taolue; Liu, Zhiming; Woodcock, Jim: Learning safe neural network controllers with barrier certificates (2021)
- Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson: NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems (2020) arXiv