[computer-go] Move Prediction and Strength in Monte-Carlo Go Program

[Rémi Coulom]

Hi,

I found the Master Thesis of Nobuo Araki is available online:
http://ark.qp.land.to/main.pdf

Abstract:
Recently in the Go program, there was a breakthrough by the Monte-Carlo 
method using
a game tree search method called UCT (UCB applied to trees, UCB stands 
for Upper Confidence
Bounds) in combination with the reduction of search space by move 
prediction. By
this method, Go programs easily become stronger than existing programs. 
However, there
are hardly any studies concerning the relationship between the strength 
of a program, and
the accuracy of move prediction, which is integrated into the 
Monte-Carlo method; therefore,
we cannot assume the direction of future research that makes stronger 
programs. In this
study, we developed a move prediction system based on machine learning 
techniques, and
researched the relationship between the accuracy of move prediction, and 
the strength of
Monte-Carlo method. Our move prediction system based on the maximum 
entropy method
attained top level accuracies of those days. Furthermore, it became 
clear that even when
the move prediction accuracy goes higher, the programs do not always 
become stronger. We
investigated the reasons behind this result. Additionally, we have 
attempted to create a Go
player by enforcing move prediction, but the result was not beyond 
satisfactory. We will also
describe the reasons behind this result.

Rémi