A GFML-based Robot Agent for Human and Machine Cooperative Learning on
  Game of Go
  This paper applies a genetic algorithm and fuzzy markup language to construct
a human and smart machine cooperative learning system on game of Go. The
genetic fuzzy markup language (GFML)-based Robot Agent can work on various
kinds of robots, including Palro, Pepper, and TMUs robots. We use the
parameters of FAIR open source Darkforest and OpenGo AI bots to construct the
knowledge base of Open Go Darkforest (OGD) cloud platform for student learning
on the Internet. In addition, we adopt the data from AlphaGo Master sixty
online games as the training data to construct the knowledge base and rule base
of the co-learning system. First, the Darkforest predicts the win rate based on
various simulation numbers and matching rates for each game on OGD platform,
then the win rate of OpenGo is as the final desired output. The experimental
results show that the proposed approach can improve knowledge base and rule
base of the prediction ability based on Darkforest and OpenGo AI bot with
various simulation numbers.
