# Imitating Human Go Players via Vision Transformer
## 1 Introduction  
Developing algorithms for Go programs capable of human-level performance has long been considered an intractable challenge. This difficulty stems from the immense search space and the complex evaluation functions involved, where a single move can drastically influence the game's outcome. Traditional hand-crafted evaluation functions and rules have consistently fallen short of achieving even amateur-level gameplay. Consequently, the predominant approach over the decades has been to leverage deep learning models trained on expert gameplay records. These models, typically trained under a supervised learning paradigm, treat board positions as inputs and use the corresponding moves made by human experts as labels. The groundbreaking success of AlphaGo has brought convolutional neural network (CNN)-based methods [1] to the forefront of Go-related tasks. By employing CNNs, AlphaGo not only defeated professional Go players but also demonstrated the viability of deep learning in mastering this intricate game. The successes of AlphaGo [2] and AlphaGo Zero [3] have encouraged Go players to use AI-powered software as training tools. For instance, Figure 1 illustrates a fifth-line shoulder hit played by [PERSON] against professional ninth-dan player [PERSON]. This move was described by commentators as \"an unthinkable move\" and \"a mistake\", as it defied conventional human Go intuition. Similarly, Figure 2 highlights another \"mistake\" by [PERSON]: selecting move A over move B, despite the latter being clearly superior from a human perspective, as it maximizes territory. This divergence arises because AlphaGo calculates win rates through a combination of neural networks and Monte Carlo Tree Search, which leads to a \"thinking process\" entirely different from human reasoning [4]. Other examples of non-human-like moves, such as slack moves played with no strategic intention other than prolonging the game, furtheremphasize the knowledge gap between humans and AI. Such moves contradict human intuition and experience, rendering them incomprehensible for players seeking to learn from the model. The success of existing Go AI systems [2; 3; 5] lies in their use of self-play, where they play against themselves to maximize their win rate. This approach, however, leads to AI-generated moves that may not resemble human-like strategies, making them less suitable as a training tool. If players cannot understand the reasoning behind AI-generated moves, they are unlikely to replicate such strategies in human-versus-human competitions. Therefore, our study aims to develop a model capable of both formulating professional-level strategies and emulating human gameplay. This would allow Go players to engage with the AI as if they were playing against professional human opponents. Additionally, our model can serve as a post-game analysis tool to help players identify and correct mistakes made during the game. Figure 1: Game 2 between AlphaGo and [PERSON]. AlphaGo played move 37 (marked by the red square) and gained a significant advantage from this move. Figure 2: A “mistake” made by AlphaGo. This is game 1 between AlphaGo and [PERSON]. After [PERSON] played move 161 (marked by the red square), AlphaGo answered with move A, which is a “clear” mistake from a human perspective. Move B is obviously a better move than A. While CNNs have delivered remarkable results in Go, they also exhibit notable limitations. Certain Go patterns, such as ladders and group life-and-death scenarios, involve long-range dependencies that require CNNs to be exceptionally deep. This increases network complexity and introduces challenges such as gradient vanishing or explosion. Recent advances in Vision Transformers (ViTs) [6] suggest that Transformers can effectively handle computer vision tasks, often outperforming CNNs across multiple domains. The multi-head self-attention mechanism in ViTs is particularly adept at capturing long-range dependencies, making it well suited for addressing challenges in Go-related tasks.