Intelligent agent for real-world applications on robotic edutainment and humanized co-learning

摘要

Dynamic assessment with an intelligent agent can differentiate the capabilities and proficiency of students. It can therefore be advocated as an interactive approach to conduct assessments on students in learning systems. Facebook AI Research proposed ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. They also developed Darkforest, which displays the competence and skills of high-level amateur Go players. To enable these open-source AI bots to assist humans at different levels in learning Go, this paper proposes an intelligent agent for real-world applications in robotic edutainment and humanized co-learning. To achieve this, we successfully constructed an OpenGo Darkforest (OGD) cloud platform using these AI bots and further combined the brain computer interface with the OGD cloud platform to observe the relationship between the brainwaves and win rates of human Go players. The intelligent agent also converted human brainwaves into physiological indices and reflected these in the robot to express human feelings or emotions in real-time. For future educational applications, this paper also presents intelligent robot teachers learning together with students in Taiwan and Japan. More than 200 students have been co-learning with intelligent robot teachers in Tainan, Kaohsiung, Taipei, and Tokyo from 2018 to 2019. The learning performance and feedback from students and teachers has been extremely positive, especially from remedial students.