A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go

TL;DR

This paper presents a GFML-based robot agent that facilitates cooperative learning between humans and machines in the game of Go, leveraging AI predictions and fuzzy logic for improved knowledge and rule base construction.

Contribution

It introduces a novel GFML-based system that integrates multiple AI bots and online game data to enhance cooperative learning in Go for various robots.

Findings

Improved prediction accuracy of the knowledge base.

Enhanced rule base for co-learning system.

Effective application across different robot platforms.

Abstract

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.