FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

TL;DR

This paper introduces an FML-based dynamic assessment agent and a human-machine cooperative system for Go, integrating decision-making, learning, and real-time evaluation to enhance gameplay analysis and cooperation.

Contribution

It presents a novel FML-based framework for Go that combines assessment, decision-making, and cooperative features, validated through extensive experiments with professional-level games.

Findings

Effective performance of FDAA in Go applications

Successful validation with professional-level game data

Enhanced human-machine cooperation in Go

Abstract

In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.