Verifying AI systems by extracting automata via learning

确保人工智能程序按照预期的方式运行，已经变得至关重要。然而，无论从理论上还是从实践的角度来看，验证人工智能系统都是一项挑战。该项目旨在研究如何从人工智能系统(如神经网络)中提取数学模型(如自动机)来捕获它们的行为。该项目将专注于为加权自动机等自动机模型开发学习算法，并将其应用于从递归神经网络(或其他人工智能系统)中提取模型，捕捉它们的一些行为，以确保安全属性。

Ensuring AI programs behave the way they are expected to, has become of utmost importance. However verifying AI systems is a challenge, both from a theoretical and from a practical point of view. The project aims at investigating how to extract mathematical models (such as automata) from AI systems (such as neural network) to capture their behaviour. The project will focus on developing learning algorithms for models of automata such as weighted automata and on applying them to extract models from recurrent neural networks - or other AI systems - capturing some of their behaviour to guarantee safety properties.