论文标题
时间逻辑分类器在环境系统中的强大推理和验证
Robust Inference and Verification of Temporal Logic Classifier-in-the-loop Systems
论文作者
论文摘要
嵌入机器学习模块的自主系统通常依靠深度神经网络来对环境中的不同对象进行分类，或者为系统采取的不同动作或策略。由于深度神经网络的非线性和高维度，自主系统的解释性受到损害。此外，自主系统中的机器学习方法主要是数据密集型，并且缺乏人类自然的常识性知识和推理。在本文中，我们提出了时间逻辑分类器在环境系统中的框架。时间逻辑分类器可以根据环境输出不同的动作以采取自主系统，从而使自主系统的行为可以满足给定的时间逻辑规范。我们的方法是强大的且可证明的，因为我们可以证明自主系统的行为可以在存在（有限的）干扰的情况下满足给定的时间逻辑规范。
Autonomous systems embedded with machine learning modules often rely on deep neural networks for classifying different objects of interest in the environment or different actions or strategies to take for the system. Due to the non-linearity and high-dimensionality of deep neural networks, the interpretability of the autonomous systems is compromised. Besides, the machine learning methods in autonomous systems are mostly data-intensive and lack commonsense knowledge and reasoning that are natural to humans. In this paper, we propose the framework of temporal logic classifier-in-the-loop systems. The temporal logic classifiers can output different actions to take for an autonomous system based on the environment, such that the behavior of the autonomous system can satisfy a given temporal logic specification. Our approach is robust and provably-correct, as we can prove that the behavior of the autonomous system can satisfy a given temporal logic specification in the presence of (bounded) disturbances.
