Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : June 06, 2021 - June 08, 2021
In services using statistical machine learning, safety quality management is important to reduce accidents and economic losses due to misjudgment and miscontrol by Artificial Intelligence (AI). However, research and development has just begun, and the management method has not yet been established. Moreover, the target of the guidelines published to date for quality management of AI-based systems are limited to supervised learning. In this paper, I develop AI safety and quality management methods for autonomous mobile robots that makes decisions and controls using deep reinforcement learning, which is not covered by the AI quality management guidelines. These methods focus on the quality characteristics and record quality assessment as evidence for analysis, understanding, and quantitative quality explanation. As a result, they enable understanding and sharing the achieved quality among stakeholders, and give concrete explanations to society. It is also useful for clarifying ordering conditions, identifying problems, and presenting added value.
