Crowdsourcing-based quick and interactive concept structure building for robots

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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.