Earticle

Not only does play constitute a human function as essential as reflection or work, but also the genesis and development of culture have a ludic character. For this reason, in this essay we will analyze the different existing relationships between the game of go and culture. In ancient China, go was born as a game. Its goal was on itself. It was played because its recreational element made the people who practiced it enjoy. Over time, it became a highly appreciated cultural tradition. Along with calligraphy, painting and music, go became one of the four arts that any intellectual had to master. Cultivate is a word that means many things such as: promote, incubate, educate, encourage, practice. profess, etc. We can say that cultivating is that journey that each human being must end up taking to get to know themselves. There are numerous paths to make this trip. Each way of cultivating oneself through a book, a movie, music, etc. it challenges us and confronts us with ourselves. In short, it activates this journey that takes us towards what one is and the game of go is no exception Go is an abstract game. When you look at a board full of black and white stones you are faced with something that seems really chaotic. However, due to its nature, one can approach go in different ways: through its ancient history, as an artistic form, its philosophical foundation or through its sociological and scientific aspect. For this reason, learning environments can approach the task of teaching and learning the game of go in a multifaceted and holistic manner, thus facilitating diverse entry points according to the interests of each individual. In this essay, we will explore the game of go through the various spheres of culture to obtain key elements that will help us better understand it and that will be useful for teaching it.

The classic game of board game has some interesting variants, such as freestyle chess (Chess960) which has a high profile international tournament. There are also many variations of Go, some modifying the rules of the game and some modifying the style of the board. In most cases, the rules of these variants are more complex than the rules of Go itself, but there are exceptions. A variant form of Go presented here is known as “8-liberty Go”, discovered by the author in 2015, which differs from classic Go by the addition of diagonal lines within all squares of the board, a change that unexpectedly streamlines the rules even further, eliminating the need for artificial rules for Ko fight and maintaining the complexity of Go itself. The fact that the standard Go board did not evolve into this style is perhaps just a historical accident, judging from ancient documents. Through the practice of several tournaments, such as the European Go Congress, and research by offline players, some of the basic techniques of the 8-liberty Go board (connecting, cut, basic life and death, shape, tesuji, etc.) have been preliminarily explored. In this paper, we will introduce the theoretical foundations, the rules and characteristics, and the introductory techniques of this variant of Go, and demonstrate the role and structure of“intuition, calculation, and judgment” in Go technology in a comparison with standard Go technology.

Artificial Intelligence (AI) is now routinely used by Go players to review their games. Analyzing individual mistakes helps players identify their weaknesses. However, deriving generalizable insights requires a broader analysis of mistake patterns. In this study, 100,682 AI-scored amateur and professional Go games are studied to investigate mistake patterns. Three different ranks are examined, ranging from low-level amateurs to top professionals. Various move features such as height, distance to previous move, and adjacent stones are analyzed to gain a deeper understanding of these mistake patterns. The key findings are as follows: (1) a noticeable improvement in opening performance among professional players since 2017; (2) a significant performance gap in the endgame between professionals (who exhibit near-optimal play) and amateurs; and (3) areas for improvement in tactical skills among amateurs, particularly in first-line and sacrificial moves.

Analysing Go games with AIpowered engines is now common practice among amateur and professional Go players. These engines rely on deep artificial neural networks (ANN) and MonteCarlo tree search (MCTS) to evaluate board positions. For a given ANN, the engine’s accuracy increases with the number of positions evaluated during the tree search, or number of visits (often called the number of playouts). Although more playouts lead to more accurate analyses, the relationship between playouts and engine accuracy has not been fully explored. In this study, we analysed thousands of games from amateur and professional players, using different numbers of playouts per move, ranging from 5 to 15,000. A statistical analysis of the results shows that a constant number of playouts per move leads to varying accuracy during the game (high accuracy during the opening and endgame, low accuracy during the middle game). Quantitative guidelines to choose the number of playouts depending on one’s rank are derived. Looking at AI prediction of human win rate, the playouts needed to reach 99% accuracy are 140 for 1d, 235 for 5d, and 1,500 for 9d Fox players and 5,500 for a 9p professional player.