Crowdsourcing-based quick and interactive concept structure building for robots

本研究旨在通过机器人自主实现对真实环境的视觉场景解释功能，特别是在真实环境事件识别的概念结构构建问题中高效优化概念聚类。我们通过互联网使用众包作为机器学习的教学信息源。我们的目标是使用众包方法设计这种机器学习的一部分，并与使用其他通用学习方法构建的概念结构相比，更有效地构建真实环境事件解释的概念结构。本研究的成果是对上述系统进行设计和构建，并在构建的系统基础上进行实验。

This study aims to achieve the visual-scene interpretation function for a real environment by a robot autonomously, particularly to efficiently optimize conceptual clustering in a concept structure building problem for real environmental event recognition. We used the crowdsource through the internet as a teaching information source of machine learning. We aim to design a part of this machine learning using the crowdsourcing approach and construct the conceptual structure of real environmental event interpretation more efficiently compared with that constructed using other general learning approaches. The achievement of this study is to design and construct the above-mentioned system, and conduct the experiment based on the constructed system.