FML-based Prediction Agent and Its Application to Game of Go

TL;DR

This paper introduces a robotic prediction agent for the game of Go that integrates a darkforest engine with fuzzy markup language assessment and decision support engines, enabling effective game situation reporting to human players.

Contribution

It presents a novel integration of FML-based assessment and decision engines with a Go engine and robot partner for real-time game prediction and reporting.

Findings

The agent accurately predicts winning possibilities.

The system effectively reports game situations to humans.

Experimental results confirm the system's effectiveness.

Abstract

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.