Objectives

Learning has become a very popular approach for cybernetics systems. This topic has always been considered a research in the Computational Intelligence (CI) area. With the recent success of AlphaGo, there has been a lot of interest among students and professionals to apply machine learning to gaming and in particular to the game of Go. Several conferences have held competitions human versus computer programs or computer programs against each other. While computer programs are already better than humans (even high level professinals), machine learning still offers interesting prospects, both from the fundamental points of view (1) to even further the limits of game playing (having programs playing against each other), (2) to better understand machine intelligence and compare it to human intelligence, and from the practical point of view of enhancing the human playing experience by coaching professinals to play better or training beginners. Hence, in 2018 and 2019, we proposed a summer school on "Computational Intelligence for Human and Robot Co-learning" in Taiwan. In addition, we also set up an AI-FML International Academy (https://sites.google.com/asap.nutn.edu.tw/ai-fml-international-academy/home) in 2020, including the IEEE CIS members from Japan, Canada, Taiwan, Italy, Spain and UK. The objectives of organizing the Computational Intelligence for Human and Robot Co-learning summer school in Japan and Taiwan in 2020 are to gather more students from senior high schools, undergraduate colleges, graduate schools, and even post-graduate that eaher to learn some ideas from the CI area.