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Weiqi, Baduk): a Beautiful Game
Go (Weiqi, Baduk): A Beautiful Game Description Go is a strategy board game originating from China more than two thousand years ago. Though its rules are simple, the game is intractably complex; a top professional can easily defeat the best supercomputers today. It was considered one of the four essential skills of a Chinese scholar. The game spread to Korea and Japan, has flourished in East Asia, and is becoming more well-known in the West. Today, Go is enjoyed by more than 40 million players in the world. Objectives Students will learn the rules of go and become able to play at an intermediate level. They will also understand the game in a historical and/or mathematical context. We hope (but do not require) that students will be able to play at a single-digit kyu level (1k-9k). Students will also learn something significant from one of the following (or both): ◦ The culture and history of go, including its origins, historical figure, and presence in East Asia and the western world. ◦ Some mathematical results of go, including a treatment of go using combinatorial game theory. This will offer enough flexibility for students of all majors. Prerequisites None. We expect all students to be able to understand the course material. The course is designed to suit students of all majors. This course is not designed for dan-level players. We will not allow dan-level players to take this course. Course Format This course will be taught by Joshua Wu in both lecture and interactive formats. We hope to emphasize student participation. -
The Way to Go Is a Copyrighted Work
E R I C M A N A G F O O N How to U I O N D A T www.usgo.org www.agfgo.org play the Asian game of Go The Way to by Karl Baker American Go Association Box 397 Old Chelsea Station New York, NY 10113 GO Legal Note: The Way To Go is a copyrighted work. Permission is granted to make complete copies for personal use. Copies may be distributed freely to others either in print or electronic form, provided no fee is charged for distribution and all copies contain this copyright notice. The Way to Go by Karl Baker American Go Association Box 397 Old Chelsea Station New York, NY 10113 http://www.usgo.org Cover print: Two Immortals and the Woodcutter A watercolor by Seikan. Date unknown. How to play A scene from the Ranka tale: Immortals playing go as the the ancient/modern woodcutter looks on. From Japanese Prints and the World of Go by William Pinckard at: Asian Game of Go http://www.kiseido.com/printss/cover.htm Dedicated to Ann All in good time there will come a climax which will lift one to the heights, but first a foundation must be laid, INSPIRED BY HUNDREDS broad, deep and solid... OF BAFFLED STUDENTS Winfred Ernest Garrison © Copyright 1986, 2008 Preface American Go Association The game of GO is the essence of simplicity and the ultimate in complexity all at the same time. It is taught earnestly at military officer training schools in the Orient, as an exercise in military strategy. -
Move Prediction in the Game of Go
Move Prediction in the Game of Go A Thesis presented by Brett Alexander Harrison to Computer Science in partial fulfillment of the honors requirements for the degree of Bachelor of Arts Harvard College Cambridge, Massachusetts April 1, 2010 Abstract As a direct result of artificial intelligence research, computers are now expert players in a variety of popular games, including Checkers, Chess, Othello (Reversi), and Backgammon. Yet one game continues to elude the efforts of computer scientists: Go, also known as Igo in Japan, Weiqi in China, and Baduk in Korea. Due in part to the strategic complexity of Go and the sheer number of moves available to each player, most typical game-playing algorithms have failed to be effective with Go. Even state-of-the-art computer Go programs are weaker than high-ranking amateurs. Thus Go provides the perfect framework for devel- oping and testing new ideas in a variety of fields in computer science, including algorithms, computational game theory, and machine learning. In this thesis, we explore the problem of move prediction in the game of Go. The move prediction problem asks how we can build a program which trains on Go games in order to predict the moves of professional and high-ranking amateur players in other Go games. An accurate move predictor could serve as a powerful component of Go-playing programs, since it can be used to reduce the branching factor in game tree search and can be used as an effective move ordering heuristic. Our first main contribution to this field is the creation of a novel move prediction system, based on a naive Bayes model, which builds upon the work of several previous move prediction systems. -
Mastering the Game of Go Without Human Knowledge
Mastering the Game of Go without Human Knowledge David Silver*, Julian Schrittwieser*, Karen Simonyan*, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis. DeepMind, 5 New Street Square, London EC4A 3TW. *These authors contributed equally to this work. A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, su- perhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated posi- tions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self- play. Here, we introduce an algorithm based solely on reinforcement learning, without hu- man data, guidance, or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of tree search, re- sulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo. Much progress towards artificial intelligence has been made using supervised learning sys- tems that are trained to replicate the decisions of human experts 1–4. However, expert data is often expensive, unreliable, or simply unavailable. Even when reliable data is available it may impose a ceiling on the performance of systems trained in this manner 5. -
On Competency of Go Players: a Computational Approach
On Competency of Go Players: A Computational Approach Amr Ahmed Sabry Abdel-Rahman Ghoneim B.Sc. (Computer Science & Information Systems) Helwan University, Cairo - Egypt M.Sc. (Computer Science) Helwan University, Cairo - Egypt A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at the School of Engineering & Information Technology the University of New South Wales at the Australian Defence Force Academy March 2012 ii [This Page is Intentionally Left Blank] iii Abstract Abstract Complex situations are very much context dependent, thus agents – whether human or artificial – need to attain an awareness based on their present situation. An essential part of that awareness is the accurate and effective perception and understanding of the set of knowledge, skills, and characteristics that are needed to allow an agent to perform a specific task with high performance, or what we would like to name, Competency Awareness. The development of this awareness is essential for any further development of an agent’s capabilities. This can be assisted by identifying the limitations in the so far developed expertise and consequently engaging in training processes that add the necessary knowledge to overcome those limitations. However, current approaches of competency and situation awareness rely on manual, lengthy, subjective, and intrusive techniques, rendering those approaches as extremely troublesome and ineffective when it comes to developing computerized agents within complex scenarios. Within the context of computer Go, which is currently a grand challenge to Artificial Intelligence, these issues have led to substantial bottlenecks that need to be addressed in order to achieve further improvements. -
Mastering the Game of Go Without Human Knowledge
ArtICLE doi:10.1038/nature24270 Mastering the game of Go without human knowledge David Silver1*, Julian Schrittwieser1*, Karen Simonyan1*, Ioannis Antonoglou1, Aja Huang1, Arthur Guez1, Thomas Hubert1, Lucas Baker1, Matthew Lai1, Adrian Bolton1, Yutian Chen1, Timothy Lillicrap1, Fan Hui1, Laurent Sifre1, George van den Driessche1, Thore Graepel1 & Demis Hassabis1 A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo. Much progress towards artificial intelligence has been made using trained solely by selfplay reinforcement learning, starting from ran supervised learning systems that are trained to replicate the decisions dom play, without any supervision or use of human data. Second, it of human experts1–4. However, expert data sets are often expensive, uses only the black and white stones from the board as input features. -
Go and Mathematics Leon
Go and Mathematics Leon Lei Around 4,000 years ago in ancient China, a board game developed that would redefine the mathematical study of games. This game is known as 围棋 (Wei Qi), or “Go”. Two players, one playing black and the other white, take turns placing stones on the intersections of a Go board (See Figure 1 and 2). If a stone or group of stones on the board is completely surrounded on all sides, then those stones are “captured” and removed from the game (See Figure 3). At the end of the game, the player with the most territory is the winner. The rules of Go are relatively simple, and anybody could learn them and start playing within 5 minutes. However, there is a deep mathematical complexity in the game that cannot be ignored, and mastering the game requires great dedication and experience, a process that requires a lifetime. Figure 1: An example of a 19x19 Go Board Figure 2: Stones go on intersection points Figure 3: Examples of captured stones (marked with red X’s) Original Diagrams 1 Go is an elegant game of balance and strategy. The game is far less about surrounding and capturing than it is about establishing your own territory in an area of the board and placing stones on key points to gain territory. In fact, it is possible for a whole game to play out in which neither side captures a single stone. Stones placed farther apart from each other have the potential of more territory, but also run a higher risk of being separated and killed. -
The Game Is Not Over Yet—Go in the Post-Alphago Era
philosophies Article The Game Is Not over Yet—Go in the Post-AlphaGo Era Attila Egri-Nagy 1,* and Antti Törmänen 2 1 Department of Mathematics and Natural Sciences, Akita International University, Yuwa, Akita City 010-1292, Japan 2 Nihon Ki-In—Japan Go Association, 7-2 Gobancho, Chiyoda City, Tokyo 102-0076, Japan; [email protected] * Correspondence: [email protected] Received: 1 October 2020; Accepted: 10 November 2020; Published: 13 November 2020 Abstract: The game of Go was the last great challenge for artificial intelligence in abstract board games. AlphaGo was the first system to reach supremacy, and subsequent implementations further improved the state of the art. As in chess, the fall of the human world champion did not lead to the end of the game. Now, we have renewed interest in the game due to new questions that emerged in this development. How far are we from perfect play? Can humans catch up? How compressible is Go knowledge? What is the computational complexity of a perfect player? How much energy is really needed to play the game optimally? Here, we investigate these and related questions with respect to the special properties of Go (meaningful draws and extreme combinatorial complexity). Since traditional board games have an important role in human culture, our analysis is relevant in a broader context. What happens in the game world could forecast our relationship with AI entities, their explainability, and usefulness. Keywords: game of Go; artificial intelligence; deep learning; neural networks; combinatorics; complexity 1. Introduction Humans have been playing the game of Go for thousands of years, and will continue to play it for long time, since it remains entertaining and educational. -
Static Analysis of Life and Death in the Game of Go
Information Sciences 121 (1999) 113±134 www.elsevier.com/locate/ins Static analysis of life and death in the game of Go Ken Chen a,*, Zhixing Chen b a Department of Computer Science, University of North Carolina, Charlotte, NC 28213, USA b Department of Chemistry, Zhongshan University, Guangzhou, People's Republic of China Received 18 February 1998; received in revised form 13 December 1998; accepted 20 February 1999 Abstract This paper describes heuristic rules for static life/death analysis for general classes of groups in the game of Go, as implemented in authorsÕ world championship computer Go programs Go Intellect and HandTalk. We shall show that for many life and death problems in Go, the eect of a tree search can be approximated by a static analysis on the groupÕs interior and surroundings. We investigate eye-spaces with or without op- ponentÕs dead stones and try to provide a foundation towards conquering the combi- natorial explosion of Go game tree. Ó 1999 Elsevier Science Inc. All rights reserved. Keywords: Heuristic programming; Approximation algorithms; Machine game playing; Combinatorial game theory 1. Introduction Recent success of the Deep Blue chess program draws vast attention and excitement to machine game playing. Yet the ultimate programming challenge in machine game playing undoubtedly lies with the ancient oriental game of Go. It is well known that Go de®es computer chess type full board search techniques. Go is a two-person perfect information game. Two players alternately place a black and a white stone on some empty intersection of a 19 by 19 grid. Placed * Corresponding author. -
Congress Handbook
Congress Handbook Contents Page 1 AGA President’s Welcome 2 Director’s Welcome 3 Acknowledgments 4 General Program Schedule 7 Professional Players 14 Professional Events 15 Preliminary Pro Schedule 18 Tournaments U.S. Open U.S. Masters Division Women’s Tournament Seniors’ Tournament Self-Paired Tournament 9x9 Tournament Lightning Tournament Die Hard Tournament 13x13 Tournament North American Pair Go Championship Pandanet AGA City League Finals Crazy Go! 23 Rules and Guidelines 24 Youth Activities 26 Organizational Meetings 27 Special Events and Competitions Teachers’ Workshop Autism Workshop Bob High Memorial Song and Poetry Contest Shamisen Performance Japanese Prints Baum Prizes Selfie Treasure Hunt Challenge 29 AGA Code of Conduct 30 Wednesday Events 31 Setting Ing Clocks 32 Memorial Union Floor Plan 33 Map of Congress Sites 34 Congress Event Rooms 34 Important Phone Numbers 35 Prior Congresses AGA President’s Welcome Dear Go Players, Welcome to the 35th Annual U.S. Go Congress. This year we come to a city at the center of some of the most fertile, peaceful farmland anywhere in the world. Madison has made its name as a beautiful place to live and to visit, a cultural center, and an academic hub. We want to acknowledge as well as pay respect to the fact that before it was Madison, it was part of the land of the Ho Chunk nation’s Winnebago territory. As the Congress website notes, the city is surrounded by five lakes, but it is the number four that seems more to the point to me. When we play Go, we site our geography in the four corners of the board. -
Structural Representation of the Game of Go, Master's Thesis
STRUCTURAL REPRESENTATION OF THE GAME OF GO by James Ian Scrimger B.Sc., Mount Allison University, 2004 A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Computer Science in the Graduate Academic Unit of Computer Science Supervisor: Lev Goldfarb, Ph.D., Computer Science Examining Board: Weichang Du, Ph.D., Computer Science, Chair Michael Fleming, Ph.D., Computer Science Yevgen Biletskiy, Ph.D., Electrical and Computer Engineering This thesis is accepted by the Dean of Graduate Studies THE UNIVERSITY OF NEW BRUNSWICK September, 2007 c James Ian Scrimger, 2007 Abstract Go, a popular game of strategy, is not amenable to the traditional AI techniques for game playing. Because of the game’s hierarchical and temporal nature, the use of structural representation is a logical approach to try. This thesis presents a preliminary representation of the game of Go in the structural language of the Evolving Transformation System (ETS). An ETS class hierarchy designed to capture progressively complex aspects of Go positions is presented. Individual Go stones are represented as elements of a few low-level classes, these classes are the constituent pieces of higher-level classes corre- sponding to blocks of stones, which are in turn assembled into classes corresponding to the evolution of larger shapes. The presented class hierarchy could form the basis of the first entirely class-based Go- player, meaning that such a program’s move selection would be directed by generative class descriptions. ii Table of Contents Abstract ii Table of Contents vii List of Tables viii List of Figures xv 1 Introduction 1 1.1 Motivation and background . -
A Machine Learning Playing Go Utilizing Neural Networks to Generate an Artificially Intelligent Go Program Without Tree Search
Technische Universität München Faculty of Mathematics: M15 A Machine Learning playing Go Utilizing neural networks to generate an artificially intelligent Go program without tree search Project Report by Benedikt Mairhörmann, Stefan Peidli, Faruk Toy, Patrick Wilson Supervisor: Prof. Dr. Massimo Fornasier Project Lead: Dr. Ricardo Acevedo Cabra Advisor: M.Sc. Bernhard Werner Submission Date: February 14, 2018 Contents 1 Project Goal and Organization 1 2 The Game of Go 2 2.1 Go from East to West ......................... 2 2.2 Basic Rules of Go ............................ 3 2.3 Computer Go .............................. 5 3 Data and Implementation 7 3.1 Data Preprocessing ........................... 7 3.2 Implementation of the Game Go .................... 7 3.3 Implementation of the Neural Network ................ 9 3.4 Training Data .............................. 9 3.5 Workflow Management ......................... 10 4 Neural Network 11 4.1 Introduction to Neural Networks ................... 11 4.2 Design of the Neural Network ..................... 14 4.2.1 The Input ............................ 14 4.2.2 Activation Functions ...................... 15 4.2.3 Error Functions ......................... 16 4.2.4 Initializing Weights ....................... 17 4.3 Finding Good Learning Parameters .................. 19 4.3.1 Batches ............................. 19 4.3.2 Stochastic Gradient Descent and Learning Rate ....... 20 4.3.3 Regularization and Momentum Term ............. 21 4.4 Architecture of the Neural Network .................. 22 4.4.1 Layers .............................. 22 4.4.2 Filters .............................. 22 5 Results 24 List of Figures 26 Bibliography 27 1 Project Goal and Organization The initial goal of this project was to develop a Go playing computer program for the small 9 9 Go board using supervised learning techniques. × Here the small board was chosen because the computational cost and the amount of training data needed to develop a reasonably good Go engine was estimated too high for the 19 19 board.