论文标题
衡量机器学习系统的能力并执行可靠性
Measuring Competency of Machine Learning Systems and Enforcing Reliability
论文作者
论文摘要
我们探讨了环境条件对机器学习代理能力的影响，以及实时能力评估如何提高ML代理的可靠性。我们了解了影响ML代理的策略和性能的状况的代表，以确定代理商可以在卷积神经网络的情况下保持操作员期望的行动，该卷积神经网络利用视觉图像有助于避免模拟自动驾驶工具的障碍任务。
We explore the impact of environmental conditions on the competency of machine learning agents and how real-time competency assessments improve the reliability of ML agents. We learn a representation of conditions which impact the strategies and performance of the ML agent enabling determination of actions the agent can make to maintain operator expectations in the case of a convolutional neural network that leverages visual imagery to aid in the obstacle avoidance task of a simulated self-driving vehicle.
