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The potential implications of AlphaGo defeating a human player have not been fully discussed in the epistemological field. AlphaGo does not play in a nearly exhaustive way with a huge amount of computation, but has reached a higher level of “intuition” and “judgment”, which humans once thought was a difficult area for computer languages to break through. This phenomenon prompts us to rethink the structure of Go knowledge. From the perspective of epistemology, what kind of knowledge of Go is indeed reliable? Based on this fundamental question, this paper attempts to analyze the structure of Go knowledge. The main thought processes in the game of Go can be summarized as “intuition”, “calculation” and “judge-ment”, where rational deduction and empirical induction co-exist. In the past, the simple enumeration of knowledge points became the main focus of Go knowledge learning and teaching, now the nature of these knowledge points are distinguished, particularly the Go knowledge between“quantitative” and “non-quantitative”, moreover, the correlation between knowledge generation and both inherent human cognitive abilities and cognitive limits is presented. This paper analyzes the specific principles of how Go AI surpasses the human level of Go from the perspective of epistemology, provides theoretical support for how human players can leverage AI for new Go knowledge production in the future, and may serve as a bridge between Go and cognitive science research.

There has been a push in recent years to provide better explanations for how AIs make their decisions. Most of this push has come from the ethical concerns that go hand in hand with AIs making decisions that affect humans. Outside of the strictly ethical concerns that have prompted the study of explainable AIs (XAIs), there has been research interest in the mere possibility of creating XAIs in various domains. In general, the more accurate we make our models the harder they are to explain. Go playing AIs like AlphaGo and KataGo provide fantastic examples of this phenomenon. In this paper, I discuss a non-exhaustive list of the leading theories of explanation and what each of these theories would say about the explainability of AIplayed moves of Go. Finally, I consider the possibility of ever explaining AIplayed Go moves in a way that meets the four principles of XAI. I conclude, somewhat pessimistically, that Go is not as imminently explainable as other domains. As such, the probability of having an XAI for Go that meets the four principles is low.

The advent of artificial intelligence (AI) has transformed the landscape of various strategic games, including Go. In 2016, the AIpowered engine AlphaGo defeated one of the world’s strongest players. Since then, Go engines have routinely been used by amateur and professional Go players to analyse their games. In the early stages of AI analysis, Go players relied solely on the AI win rate, the only available indicator. However, the AI win rate does not accurately reflect the win rate of human Go players and might be misleading. Katago, first released in 2019, is the first engine to provide score predictions in addition to win rates. While it is now possible to evaluate board positions with a score, it remains unclear how this score translates into human win rates. In this work, a large database of online and professional games is analysed to extract the win rate of a human player based on their strength and the stage of the game. As expected, the human win rate is significantly lower than the AI win rate, even for 9dan professional players. A general formula is provided to compute the win rate based on player strength and move number. This feature offers new insights into the relative importance of mistakes and can assist players in making improved decisions during games.

Since the democratization of powerful AI engines for the game of Go, it is not uncommon to see a drastic level increase of some players that must be explained with the help of AI. This is considered cheating and forbidden by most organizations. When looking at online beginners and stronger amateur players, we discovered that they can display playing strength below professional level and still confidently win the game, as opposed to professional players. This makes using only AI-likeness metrics not sufficient to detect such players. We propose a method based on the analysis of a player’s performance considering point loss distribution over several games, taking into account only relevant moves of a game. We still use an AI-likeness metric for analyzing individual games where the use of AI may not be consistent. We evaluated our methods on two European go official online leagues, where cheating detection was already performed (for a total of about 150 unique regular players, with levels ranging from 20 kyu to 5 European dan). We show that our system confirmed 5 cases of players previously banned for cheating (out of 6). Our methods do not set out to categorize players between “cheaters” and “not cheaters,” but rather rank them in order of suspicion, for the sake of assisting referees and providing them a way to effectively investigate suspicious players over time.

In 2016, AlphaGo’s advent transformed the world of Go as AI-powered tools began to surpass the world’s top professional players. The rapid growth in AI’s influence raises questions about the potential replacement of human players. This paper examines recent trends in Go education in light of the AI revolution and its future implications. To investigate these trends, we conducted a survey among Go educators, focusing on three key aspects: (1) the perceived benefits of learning Go, (2) the impact of AI on Go education, and (3) educators’ satisfaction with Go AI tools. Data was collected through online questionnaires in English, Korean, and Chinese. Survey results indicate that Go teachers believe learning Go equips students with valuable skills, including critical thinking, resilience, and persever-ance, fostering character and cognitive development. However, educators’ opinions on AI-based tools in the classroom are mixed. Approximately 41% of respondents have refrained from using AI tools, citing concerns about their suitability for lower-level and younger learners, as well as perceived difficulties in their implementation. Additionally, there are concerns about over-reliance on AI and its limitations in Go education. Conversely, educators who have integrated AI tools report overall satisfaction and optimism for further developments. This study highlights the growing acceptance of AI programs and their positive impact on Go education. While practical demands remain partially unmet, many educators, in general, express satisfaction with the available programs. The findings of this study shed light on areas for potential improvement in AI to further enhance Go education.

The Baduk Promotion Act, which was enacted for the purpose of contributing to the expansion of leisure opportunities for the people, the cultivation of healthy minds, and the globalization of Baduk, legally supported the policy of supporting the promotion of Baduk and the creation of Baduk culture, and prepared a legal basis for it. This has a significant meaning for Baduk community. However, in order for the law to be effective, it must be practiced in real society. Therefore, the purpose of this study is to examine the effectiveness of the Baduk Promotion Act enacted on April 17, 2018 for its legislative purpose. The results of examining the characteristics and limitations of the Baduk Promotion Act are as follows. First, there is an issue regarding the lack of clear definitions within the law for terms such as ‘Baduk instructor,’ ‘Baduk professional player,’ and ‘Baduk organization.’. Second, there is an issue concerning the term ‘special circumstances’ used in defining cooperation with relevant agencies, as it leaves room for various interpretations. Third, the provisions of the Baduk Promotion Act, which stipulate support policies such as funds, are meaningful in that they provide a legal basis for receiving financial support from the state budget. There is a problem that consists of regulations that are too comprehensive and abstract to be taken as a legal basis for materialization. Lastly, there is an issue within the content related to the cultivation and support of Baduk professional players, which may prioritize a sports policy focused on fostering elite athletes and elevating the national stature rather than aligning with the primary objectives set forth by the Baduk Promotion Act. Therefore, to enhance the effectiveness of the Go Promotion Act, it is necessary to enact legal provisions that specify clear criteria and procedures. Through such improvements, it is believed that effective support and development for the promotion of Go can be facilitated.

The purpose of this study is to contribute to the creation of a more equal environment for playing online Go. For this purpose, the results of the online Go server C company’s 7 dan matches and 7 dan and 6 dan matches were used. The matches were divided into sections by rating score difference between the two players and the winning rate and the number of matches were analyzed. The results of analyzing 7 dan’s 269,898 matches and 7 dan and 6 dan’s 107,649 matches are as follows. First, the winning rate on the side with the higher score (H ratio) increased by 10% for every 300 points in the 7 dan matches. As for the number of matches, 70% of the matches were distributed within the first 15 sections (point difference ranged 300), which is within H ratio of 50%, and about 90% of the matches were played within 25 sections (point difference ranged 500), which is within H ratio of 60%. In the 7 dan matches, when the difference was more than 300 points, the even game with 6.5 point komi seemed unequal. Second, in the matches between 7 dan and 6 dan, overall, 10% increase in the H ratio was shown for every 450 points, and one-stone handicap seemed unequal from about 900 points difference. Looking at the distribution of the number of matches, about 80% of the matches were played between the 20th section (point difference ranged 400) and the 71st section (point difference ranged 1420), that is, the H ratio ranged from 50% to 60%. Third, as a countermeasure against this inconsistency, komi subdivision and constant C value adjustment were proposed.

Go is a strategy game of oriental origin that has spread to many countries. With an antiquity of more than 2,500 years, its influence has expanded to Mexico, where it is currently practiced by people of all ages, without distinction of gender. Information on the web and studies indicate that practicing Go can influence the strengthening of cognitive skills of the people who play it. Therefore, the interest in this project is focused on studying the influence of the game Go on the development and strengthening of cognitive skills in people who play it in Mexico City and the metropolitan area, based on the evaluation and comparison of two groups: a group of 22 people who play Go and a group of 22 people who do not. The study was carried out in June 2023. Four cognitive skills were chosen for evaluation: reasoning, creativity, mathematical ability, and emotional intelligence. The evaluations consisted of exercises designated by school levels: primary, secondary, and high school and beyond. The data obtained was compiled into a table to create comparative graphs. The interpretation of the results revealed that the group that played Go obtained higher percentages in the evaluation of cognitive abilities, with mathematical ability showing the most significant improvement.