PFML-based Semantic BCI Agent for Game of Go Learning and Prediction
This work addresses Go players and students by providing an incremental improvement in online learning environments through human-machine co-learning.
The paper tackles the problem of enhancing Go learning and prediction by developing a semantic BCI agent that uses PSO and FML to optimize parameters between human brain waves and machine game data, achieving favorable performance in a co-learning mechanism.
This paper presents a semantic brain computer interface (BCI) agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go learning and prediction applications. Additionally, we also establish an Open Go Darkforest (OGD) cloud platform with Facebook AI research (FAIR) open source Darkforest and ELF OpenGo AI bots. The Japanese robot Palro will simultaneously predict the move advantage in the board game Go to the Go players for reference or learning. The proposed semantic BCI agent operates efficiently by the human-based BCI data from their brain waves and machine-based game data from the prediction of the OGD cloud platform for optimizing the parameters between humans and machines. Experimental results show that the proposed human and smart machine co-learning mechanism performs favorably. We hope to provide students with a better online learning environment, combining different kinds of handheld devices, robots, or computer equipment, to achieve a desired and intellectual learning goal in the future.