A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go
This is an incremental improvement for Go AI systems, potentially benefiting robotics and educational platforms.
The paper tackles the problem of improving prediction accuracy in Go game AI by developing a cooperative learning system using genetic fuzzy markup language (GFML) with robots like Palro and Pepper, achieving enhanced knowledge and rule bases based on Darkforest and OpenGo AI bots with various simulation numbers.
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.