Earticle

초록

영어

The endgame stage of Go presents a unique challenge for scientific research. Contrary to previous stages, in the endgame the key to a successful analysis is board decomposition into smaller, independent local positions. Go players typically analyze these positions separately and prioritize moves based on their value. In this paper, I introduce a novel program that automates this decomposition-based analysis for the endgame stage of Go. AlphaZero has revolutionized Computer Go, by applying a generic move-selection mechanism, based on neural network judgments and the MCTS search algorithm. However, it does not specifically address the complexity of endgame in the aforementioned manner. On the other hand, by leveraging the decomposition-based analysis, my program reaches decisions in the endgame with relatively little computation. Additionally, it offers insights for Go practitioners by providing accurate move value evaluations. Notable prior work on automated endgame analysis was done by Martin Müller (1995). His program Explorer checked all possible variations in every undecided position and aggregated the results based on an algorithm inspired by the Combinatorial Game Theory (CGT). However, due to the exponential growth of the number of variations, Explorer’s application was limited to small, tightly bounded local positions. In contrast, my program leverages a neural network to predict optimal local moves, dramatically reducing the number of variations that need to be explored. Provided that the neural network’s predictions are correct, the program can accurately evaluate move values by considering relatively few variations, just like human Go experts do. Thanks to this approach, it is the first program capable of analyzing large, unbounded local positions, which are commonly encountered in real games. The neural network was fine-tuned from a pre-trained AlphaZero reimplementation on the task of optimal local move prediction. Training data was gathered from KataGo self-play games, utilizing KataGo’s network to perform board decomposition.

목차

Abstract
I. Introduction
1. Mathematical approach to endgame
2. AlphaZero mode of operation
3. AI estimating move values
II. Related work
III. Goal of this work
1. Canonical forms vs. temperature theory
2. Forcing moves in light of CGT
3. Chosen approach
IV. Methods
1. Information to be predicted by the network
2. Model architecture
3. Training data construction
4. Data augmentation and sampling
5. Training procedure
6. Calculating temperature
V. Results
1. Comparison with baseline
2. Qualitative analysis
VI. Future work
Conclusion
References

키워드

저자

• Stanisław Frejlak [ University of Warsaw, Poland ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  바둑학연구 [Journal of Go Studies]

• 간기

  반년간

• pISSN

  1738-3730

• 수록기간

  2004~2025

• 십진분류

  KDC 691 DDC 794