1 research outputs found
PFML-based Semantic BCI Agent for Game of Go Learning and Prediction
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