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Gobang is one of the most ancient abstract strategy games for two players. The game is traditionally played on a board with black and white stones, where players take turns placing a colored stone on an empty intersection. The winner is the first player to form an unbroken chain of five stones, either horizontally, vertically, or diagonally. Although the rules of Gobang seem pretty straightforward, the game tree complexity is enormous since the board state is more intuitive than in other games. In this paper, we will implement an algorithm with faster and more efficient play than existing ones that will solve the Gobang game using artificial intelligence (AI) methods. The program will begin learning from scratch, then use selfplay to produce training data, and eventually steadily build up its strength. The present work first focuses on the implementation of the supervised learning algorithm in the identification procedure in order to identify the position of the current fallen piece. This will be achieved by utilizing image processing and a residual neural network. Then a Gobang game procedure will be implemented using a game search algorithm, in which the state of the game is judged by means of a human-set function. After that, the function of judging the game state in the above game search algorithm will be changed to an artificial neural network (ANN) model since it is convenient to train a model with a small dataset. Finally, the reinforcement learning algorithm will be applied to learn the artificial neural network model so that the playing level of the Gobang game program can be continuously improved. [ABSTRACT FROM AUTHOR]

Research Summary: Problem formulation is central to recombinant invention, and problem complexity particularly shapes the process and outcome of knowledge recombination. However, research on the antecedents of problem complexity remains limited. This study examines how breakthrough inventions may serve as an important antecedent of the complexity of problems formulated by individual inventors. We propose that a breakthrough invention may facilitate inventors' appreciation of novel knowledge couplings and improve their overall comprehension of knowledge interdependence for problem formulation, thus increasing problem complexity. We further argue that inventors' prior search breadth and experimentation strengthen the above effect. By exploiting the unexpected victory of AlphaGo and tracking the questions posted on StackOverflow.com by developers interested in deep learning, we find empirical evidence that supports our hypotheses. Managerial Summary: Because formulating complex problems may result in novel and valuable inventions, it is helpful to understand what drives inventors to formulate problems of higher complexity. In this study, we propose that breakthrough inventions can serve as an important antecedent of problem complexity. In the context of deep learning, we find that developers formulate problems of higher complexity after the unexpected victory of AlphaGo, a widely acknowledged inventive breakthrough. Further, the above effect is more pronounced when developers have broad search experience in the past or engage frequently in experimentation. The insights of this study are not only relevant to managerial practices within the dynamic field of deep learning but also generalizable to broader technological contexts with breakthrough inventions. [ABSTRACT FROM AUTHOR]

The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.

The article offers information on the 1st World AI Go Open, 2017 CITIC Securities Cup held in Ordos City, Inner Mongolia, China, during August 16–18, 2017. Topics discussed include information on the borard game, Go championship using artificial intelligence; the championship was hosted by the CITIC Securities; and the information on the use of the technology in games.

The game of Block Go was invented in 2009 as a simplified version of Go. Block Go is played on a 13 × 13 Go board, and each player has nine blocks. This game is suitable for beginners and is popular in most children Go institutes in Taiwan. In this paper, we apply Deep Convolutional Neural Network and Monte Carlo tree search to develop a Block Go program. As a case study, we describe the program named ILEP, which won the Block Go tournament in the Computer Olympiad 2017. [ABSTRACT FROM AUTHOR]

This lecture is about go, an ancient oriental game of strategy. It is divided in three parts: on the first part, a broad perspective of the world of go is given; the second part deals with the basic rules of the game, and on the final part some analogies are made on go and business. [ABSTRACT FROM AUTHOR]

Introduction Dedication and training to a profession results in a certain level of expertise. This expertise, like any other skill obtained in our lifetime, is encoded in the brain and may be reflected in our brain's connectome. This property can be observed by mapping resting state connectivity. In this study, we examine the differences in resting state functional connectivity in four major networks between professional 'Baduk' (Go) players and normal subjects. Methods Resting state fMRI scans were acquired for professional 'Baduk' (Go) players and normal controls. Major resting state networks were identified using independent component analysis and compared between the two groups. Networks which were compared include the default mode network, the left and right fronto-parietal network, and the salience network. Results We found that normal subjects showed increased connectivity within certain areas of each target network. Professional players, however, showed higher connectivity to regions outside the traditional regions of each given network. Close examination of these regions revealed that regions shown to have higher connectivity in professional players have been revealed to be relevant in expertise for board games. Conclusion The findings in this study suggest that continuous training results in greater integration between regions and networks, which are necessary for high-level performance. The differences observed in our study between normal controls and professional players also shed light on the difference in brain connectivity which can arise through lifestyle and specialization in a specific field. [ABSTRACT FROM AUTHOR]

This paper extends simulation adjusting that optimizes parameters in a policy function so that the Monte-Carlo method produces correct moves, and gives a firm theoretical basis for simulation adjusting. Preliminary experiments show that improved simulation adjusting is capable of adjusting 28 parameters of the policy function in a Go program. [ABSTRACT FROM AUTHOR]

A reprint of the article "A History of Go-playing Programs" by Jon Diamond, which first published online from the British Go Association is presented. It discusses the history of Go-playing programs which started in the Go program written by Albert Zobrist in 1968 as part of his thesis on pattern recognition, the computer go tournaments which started in 1984 in London, England, and the advancements in Go programs, such as the use of neural networks in 2005 and the performance of the AlphaGo.

Seki positions of the game of Go that are discussed in this paper involve only chains with 2 liberties with the minor generalization of what will be called 'negligible' 1- or 2-stone chains with 1 liberty sitting in a 1-chain eye. There are no kos in the seki and no kos can result from play in the seki. Any move, other than capturing a negligible chain or filling a 1-point eye composed of multiple chains of same color and thus linking these chains, would result in the instant capture of the placed stone, and its attached chain of the same color. Also, the captured chain must be large enough, and of the right shape, such that the captured player then loses, without even the possibility of any type of ko, like 'mannen ko'. Therefore, 'throw-in ko', and sacrifices of 'Nakade' shapes, and positions like 'bent 4 in the corner', are excluded. We call the remaining, simplest, permitted positions Basic Seki. The article starts with describing deformations of seki positions that do not change the essential structure of a seki which is summarized in a so-called Basic Seki Graph. By using such graphs it is shown that all chains which share some liberty, say A, can have in total at most two more liberties other thanA. As a consequence Basic Seki have either a linear or a circular topology. Furthermore, if the position has > 2 empty points, then there can be at most 3 chains which have the same 2 empty points as liberties and, moreover, these chains need to have the same color. These properties allow us to recognise Basic Seki, after 1) verifying that sacrificing any chain leads to an unconditional loss, and then 2) verifying the above properties of Basic Seki. These properties are all local, so that the total effort goes linearly with the number of liberties and chains that are involved if that number gets large. Based on the necessary and sufficient properties of Basic Seki, a numerical encoding of any Basic Seki is possible that allows a straightforward algorithm for formulating all possible topologies of Basic Seki and thus for creating any Basic Seki position. An approximate formula for the number of different topologies of Basic Seki has been derived. Finally, examples of modifications of seki positions are shown that change their Basic Seki Graph. The resulting positions are used to illustrate the numerical encoding of their topology. [ABSTRACT FROM AUTHOR]

This article combines both analytical and evocative autoethnographic study of Go to illuminate Confucian self-transformation and to contribute to Robert Stebbins's conceptions of serious leisure. As an analytical autoethnographic study, this article interweaves the author's personal experiences of playing Go with theoretical discussions. The author argues that whereas Stebbins's analysis of durable benefits from serious leisure is individualist, Confucian self-transformation encourages a relationalist approach and that the latter approach could be a remedy for the shortcomings of the former one. As for the evocative part of this article, the author uses some devices of fictional writing to tell a lifelike story of an unforgettable Go game. This story invites readers to put themselves in the author's situations, to taste the sweet fruit of the author's serious leisure pursuit, and to reflect on their own leisure experiences. [ABSTRACT FROM PUBLISHER]

This paper aims to present a collection of the several implications of AlphaGo's victory against the top professional Go player, Lee Sedol. The implications were studied through three different aspects: (1) technological, (2) social, and (3) economical/political. The study of the technological implications was done through the lens of understanding what makes AlphaGo work, as well as the differences between AlphaGo and Deep Blue. The social implications are viewed through the perspective of the Go community in general, with a discussion on the fears and optimistic outlooks by different professionals. The economic/political aspect is viewed on a global perspective, integrating business and political mindsets as well as an inquiry into how the Chinese think about issues of war and diplomacy. [ABSTRACT FROM AUTHOR]

The article analyzes liberty races (semeai) in the game of Go. A rigorous treatment of positions with approach moves, but no ko revealed a class of positions not described in the literature before. The complete presented classification is simpler and more compact than the one used so far. This was achieved through a classification by the types of liberties and the order they are to be filled instead of a classification by group status. A principal difference to semeai without approach moves is that in the presence of approach moves and shared liberties and in the absence of eyes, two different strategies are possible because the un-orthodox filling of shared liberties instead of outer liberties may be a better strategy. [ABSTRACT FROM AUTHOR]

This paper offers an ethnomethodological exploration of fun in Go (the ancient board game), the timely delivery of a 'Monkey Jump' (a particular move in Go), and its lingering relevance to science studies (where Go has provided an early analogy for laboratory work). In Go terms, the paper makes a 'pincer' move: on the one hand, it explores the analytic potential of 'fun' for ethnographic purposes and, on the other hand, it questions its manifest abandonment in some quarters of science studies. In particular, the paper challenges their 'curious seriousness' (Garfinkel in Réseaux Hors Sér 8(1):69-78, ) whenever grand ontological claims are mixed up with suspended empirical inquiry. That said, the latter criticism does not take the form of a scholarly exercise in conceptual clarification, but remains part and parcel of the author's ethnography of playing amateur Go, including his dealing with and delivery of a Monkey Jump and reading of Go literature and replaying of professional games (as most amateurs do). The key point of the paper, then, is to demonstrate the heuristic interest of adopting a practitioner's stance, not only for understanding a technical domain such as Go in its own terms (Livingston in Ethnographies of reason, Ashgate, Aldershot, ), but also for launching a phenomenological critique of analytic discretion in science studies. Therefore, the second part of the paper re-examines, from an amateur Go player's stance, Latour and Woolgar's Go analogy in and for Laboratory Life (, )-an early exemplar of science studies' ontological bent. [ABSTRACT FROM AUTHOR]

The game of Go is a board game with a long history that is much more complex than chess. The uncertainties of this game will be higher when the board size gets bigger. For evaluating the human performance on Go games, one human could be advanced to a higher rank based on the number of winning games via a formal human against human competition. However, a human Go player's performance could be influenced by factors such as the on-the-spot environment, as well as physical and mental situations of the day, which causes difficulty and uncertainty in certificating the human's rank. Thanks to a sample of one player's games, evaluating his/her strength by classical models such as the Bradley–Terry model is possible. However, due to inhomogeneous game conditions and limited access to archives of games, such estimates can be imprecise. In addition, classical rankings (1 Dan, 2 Dan, …) are integers, which lead to a rather imprecise estimate of the opponent's strengths. Therefore, we propose to use a sample of games played against a computer to estimate the human's strength. In order to increase the precision, the strength of the computer is adapted from one move to the next by increasing or decreasing the computational power based on the current situation and the result of games. The human can decide some specific conditions, such as komi and board size. In this paper, we use type-2 fuzzy sets (T2FSs) with parameters optimized by a genetic algorithm for estimating the rank in a stable manner, independently of board size. More precisely, an adaptive Monte Carlo tree search (MCTS) estimates the number of simulations, corresponding to the strength of its opponents. Next, the T2FS-based adaptive linguistic assessment system infers the human performance and presents the results using the linguistic description. The experimental results show that the proposed approach is feasible for application to the adaptive linguistic assessment on a human Go player's performance. [ABSTRACT FROM AUTHOR]

The first codecentric Go Challenge took place in Fall 2014. A top German amateur and a top Go bot played a "best of 5" match on the 19x19 board, without any handicap stones. The final score was a 3:1-victory for the human. [ABSTRACT FROM AUTHOR]

Information about TAAI 2013 Computer Game Tournaments held at the 2013 Technologies and Applications of Artificial Intelligence (TAAI) conference in National Chengchi University, Taipei, Taiwan on December 7, 2013. Topics discussed include participation of computer programs from Netherlands, France, and Taiwan; games of the tournaments including Go 9x9, Chinese chess and Mahjong; and winners of the tournaments with the program SHIGA winning the Chinese chess tournament.

The ability to group perceptual objects into functionally relevant categories is vital to our comprehension of the world. Such categorisation aids in how we search for objects in familiar scenes and how we identify an object and its likely uses despite never having seen that specific object before. The systems that mediate this process are only now coming to be understood through considerable research efforts combining neurological, psychological and behavioural studies. What is much less well understood are the differences between the categories, how they are formed and how they are used by experts and non-experts in a complex task that can take decades to master. In a quite different direction to previous studies, this work infers the different categorical structures that might be used by amateurs and professionals in the oriental game of Go. This is achieved by using a newly developed combination of artificial neural networks (Self-organising Maps) and perceptual inference to show that categories of strategic scenes can be learned while playing games using a model of 'conditional perceptual learning'. Applying this technique to two databases of games, one of amateurs and one of professionals, shows that a structural hierarchy of scene information develops that can be readily incorporated into traditional psychological models of decisions and readily implemented in computational systems. The results are discussed in terms of the heuristics and biases literature, emphasising where the significant similarities and differences lie between this work and previous work. [ABSTRACT FROM AUTHOR]

In my book Philosophers and Thespians: Thinking Performance (Stanford University Press, 2010) I analysed four encounters between philosophers and theatre people – the thespians in the title – as a point of departure for presenting a typology based on the exploration of the complex border landscapes between the discursive practices of philosophy and performance/theatre. In this article, I take a closer look at one of these encounters, exploring the narrative strategies of Bertolt Brecht and Walter Benjamin, as well as Franz Kafka, suggesting that they can be analysed on the basis of the games of chess and go. The differences between the rules of these two board games serve as the basis for a dramaturgical analysis where different aspects of performance/theatre and philosophy (practice and theory) are brought together. This hermeneutic ‘approach’ also reflects the basic pedagogic situation of studying and researching theatre and performance as well as many of the ethical concerns raised by our involvement with these artistic practices, along with those of philosophy. [ABSTRACT FROM PUBLISHER]

Abstract: In this study, we tackled the reduction of computational complexity by pruning the igo game tree using the potential model based on the knowledge expression of igo. The potential model considers go stones as potentials. Specific potential distributions on the go board result from each arrangement of the stones on the go board. Pruning using the potential model categorizes the legal moves into effective and ineffective moves in accordance with the threshold of the potential. In this experiment, 4 kinds of pruning strategies were evaluated. The best pruning strategy resulted in an 18% reduction of the computational complexity, and the proper combination of two pruning methods resulted in a 23% reduction of the computational complexity. In this research we have successfully demonstrated pruning using the potential model for reducing computational complexity of the go game. [Copyright &y& Elsevier]

In this paper we survey most of the games that, throughout history, can be classified as mathematical games. This paper is based on a talk given at the conference 'Numeracy: historical, philosophical, and educational perspectives' at St Anne's College, Oxford, December 2009. [ABSTRACT FROM AUTHOR]

ificial intelligence (AI) research is fast approaching, or perhaps has already reached, a bottleneck whereby further advancement towards practical human-like reasoning in complex tasks needs further quantified input from large studies of human decision-making. Previous studies in psychology, for example, often rely on relatively small cohorts and very specific tasks. These studies have strongly influenced some of the core notions in AI research such as the reinforcement learning and the exploration versus exploitation paradigms. With the goal of contributing to this direction in AI developments we present our findings on the evolution towards world-class decision-making across large cohorts of subjects in the formidable game of Go. Some of these findings directly support previous work on how experts develop their skills but we also report on several previously unknown aspects of the development of expertise that suggests new avenues for AI research to explore. In particular, at the level of play that has so far eluded current AI systems for Go, we are able to quantify the lack of 'predictability' of experts and how this changes with their level of skill. [ABSTRACT FROM AUTHOR]

The article focuses on the potential use of artificial intelligence (AI) to improve organizational data analysis, maximization of healthcare data systems and production. It mentions the use of AI initially from the game between Korean Go player Lee Sedol against AlphaGo, a computer program developed by technology company Google to play Go boardgame.

This article reviews the historic dynamics of drug control and the alternating cycle of availability and prohibition that has characterized the United States since the mid-eighteenth century. It evaluates the seemingly irreconcilable differences separating prohibitionist and decriminalization advocates, and analogizes the situation to the board game Go and the cyclic entrapment known as a ko. While recognizing that the likelihood of resolving these fundamentally oppositional viewpoints is not great, it offers that some potential pathways exist, through shifting the definition of drug abuse into a public health perspective, and adapting some of the control regimen currently employed in the regulation of alcohol. [ABSTRACT FROM AUTHOR]