论文标题
具有部分定义性能功能的系统的主动学习可靠性方法
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions
论文作者
论文摘要
在工程设计中，人们经常希望计算系统的性能在不确定性下令人满意的概率。存在使用高斯过程模型的主动学习来解决此问题的最新算法。但是，这些算法不能应用于通常在某些情况下系统性能不确定的自动驾驶汽车域中经常出现的问题。为了解决此问题，我们引入了一个用于系统性能的层次模型，该模型在进行性能之前对不确定的性能进行了分类。这使主动学习高斯过程方法可以应用于系统性能有时不确定的问题，我们通过测试我们的方法论在自主驾驶领域的合成数值示例上测试我们的方法的有效性。
In engineering design, one often wishes to calculate the probability that the performance of a system is satisfactory under uncertainty. State of the art algorithms exist to solve this problem using active learning with Gaussian process models. However, these algorithms cannot be applied to problems which often occur in the autonomous vehicle domain where the performance of a system may be undefined under certain circumstances. To solve this problem, we introduce a hierarchical model for the system performance, where undefined performance is classified before the performance is regressed. This enables active learning Gaussian process methods to be applied to problems where the performance of the system is sometimes undefined, and we demonstrate the effectiveness of our approach by testing our methodology on synthetic numerical examples for the autonomous driving domain.
