Going-Insurrection-Read.Pdf
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Lesson Plans for Go in Schools
Go Lesson Plans 1 Lesson Plans for Go in Schools By Gordon E. Castanza, Ed. D. October 19, 2011 Published By Rittenberg Consulting Group 7806 108th St. NW Gig Harbor, WA 98332 253-853-4831 ©Gordon E. Castanza, Ed. D. 10/19/11 DRAFT Go Lesson Plans 2 Table of Contents Acknowledgements ......................................................................................................................... 4 Purpose/Rationale ........................................................................................................................... 5 Lesson Plan One ............................................................................................................................. 7 Basic Ideas .................................................................................................................................. 7 Introduction ............................................................................................................................... 11 The Puzzle ................................................................................................................................. 13 Surround to Capture .................................................................................................................. 14 First Capture Go ........................................................................................................................ 16 Lesson Plan Two ........................................................................................................................... 19 Units & -
FUEGO—An Open-Source Framework for Board Games and Go Engine Based on Monte Carlo Tree Search Markus Enzenberger, Martin Müller, Broderick Arneson, and Richard Segal
IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, VOL. 2, NO. 4, DECEMBER 2010 259 FUEGO—An Open-Source Framework for Board Games and Go Engine Based on Monte Carlo Tree Search Markus Enzenberger, Martin Müller, Broderick Arneson, and Richard Segal Abstract—FUEGO is both an open-source software framework with new algorithms that would not be possible otherwise be- and a state-of-the-art program that plays the game of Go. The cause of the cost of implementing a complete state-of-the-art framework supports developing game engines for full-information system. Successful previous examples show the importance of two-player board games, and is used successfully in a substantial open-source software: GNU GO [19] provided the first open- number of projects. The FUEGO Go program became the first program to win a game against a top professional player in 9 9 source Go program with a strength approaching that of the best Go. It has won a number of strong tournaments against other classical programs. It has had a huge impact, attracting dozens programs, and is competitive for 19 19 as well. This paper gives of researchers and hundreds of hobbyists. In Chess, programs an overview of the development and current state of the UEGO F such as GNU CHESS,CRAFTY, and FRUIT have popularized in- project. It describes the reusable components of the software framework and specific algorithms used in the Go engine. novative ideas and provided reference implementations. To give one more example, in the field of domain-independent planning, Index Terms—Computer game playing, Computer Go, FUEGO, systems with publicly available source code such as Hoffmann’s man-machine matches, Monte Carlo tree search, open source soft- ware, software frameworks. -
Reinforcement Learning of Local Shape in the Game of Go
Reinforcement Learning of Local Shape in the Game of Go David Silver, Richard Sutton, and Martin Muller¨ Department of Computing Science University of Alberta Edmonton, Canada T6G 2E8 {silver, sutton, mmueller}@cs.ualberta.ca Abstract effective. They are fast to compute; easy to interpret, modify and debug; and they have good convergence properties. We explore an application to the game of Go of Secondly, weights are trained by temporal difference learn- a reinforcement learning approach based on a lin- ing and self-play. The world champion Checkers program ear evaluation function and large numbers of bi- Chinook was hand-tuned by expert players over 5 years. nary features. This strategy has proved effective When weights were trained instead by self-play using a tem- in game playing programs and other reinforcement poral difference learning algorithm, the program equalled learning applications. We apply this strategy to Go the performance of the original version [7]. A similar ap- by creating over a million features based on tem- proach attained master level play in Chess [1]. TD-Gammon plates for small fragments of the board, and then achieved world class Backgammon performance after train- use temporal difference learning and self-play. This ingbyTD(0)andself-play[13]. A program trained by method identifies hundreds of low level shapes with TD(λ) and self-play outperformed an expert, hand-tuned ver- recognisable significance to expert Go players, and sion at the card game Hearts [11]. Experience generated provides quantitive estimates of their values. We by self-play was also used to train the weights of the world analyse the relative contributions to performance of champion Othello and Scrabble programs, using least squares templates of different types and sizes. -
Curriculum Guide for Go in Schools
Curriculum Guide 1 Curriculum Guide for Go In Schools by Gordon E. Castanza, Ed. D. October 19, 2011 Published By: Rittenberg Consulting Group 7806 108th St. NW Gig Harbor, WA 98332 253-853-4831 © 2005 by Gordon E. Castanza, Ed. D. Curriculum Guide 2 Table of Contents Acknowledgements ......................................................................................................................... 4 Purpose and Rationale..................................................................................................................... 5 About this curriculum guide ................................................................................................... 7 Introduction ..................................................................................................................................... 8 Overview ................................................................................................................................. 9 Building Go Instructor Capacity ........................................................................................... 10 Developing Relationships and Communicating with the Community ................................. 10 Using Resources Effectively ................................................................................................. 11 Conclusion ............................................................................................................................ 11 Major Trends and Issues .......................................................................................................... -
FUEGO – an Open-Source Framework for Board Games and Go Engine Based on Monte-Carlo Tree Search
FUEGO – An Open-source Framework for Board Games and Go Engine Based on Monte-Carlo Tree Search Markus Enzenberger, Martin Muller,¨ Broderick Arneson and Richard Segal Abstract—FUEGO is both an open-source software frame- available source code such as Hoffmann’s FF [20] have had work and a state of the art program that plays the game of a similarly massive impact, and have enabled much followup Go. The framework supports developing game engines for full- research. information two-player board games, and is used successfully in UEGO a substantial number of projects. The FUEGO Go program be- F contains a game-independent, state of the art came the first program to win a game against a top professional implementation of MCTS with many standard enhancements. player in 9×9 Go. It has won a number of strong tournaments It implements a coherent design, consistent with software against other programs, and is competitive for 19 × 19 as well. engineering best practices. Advanced features include a lock- This paper gives an overview of the development and free shared memory architecture, and a flexible and general current state of the FUEGO project. It describes the reusable components of the software framework and specific algorithms plug-in architecture for adding domain-specific knowledge in used in the Go engine. the game tree. The FUEGO framework has been proven in applications to Go, Hex, Havannah and Amazons. I. INTRODUCTION The main innovation of the overall FUEGO framework may lie not in the novelty of any of its specific methods and Research in computing science is driven by the interplay algorithms, but in the fact that for the first time, a state of of theory and practice. -
Go Books Detail
Evanston Go Club Ian Feldman Lending Library A Compendium of Trick Plays Nihon Ki-in In this unique anthology, the reader will find the subject of trick plays in the game of go dealt with in a thorough manner. Practically anything one could wish to know about the subject is examined from multiple perpectives in this remarkable volume. Vital points in common patterns, skillful finesse (tesuji) and ordinary matters of good technique are discussed, as well as the pitfalls that are concealed in seemingly innocuous positions. This is a gem of a handbook that belongs on the bookshelf of every go player. Chapter 1 was written by Ishida Yoshio, former Meijin-Honinbo, who intimates that if "joseki can be said to be the highway, trick plays may be called a back alley. When one masters the alleyways, one is on course to master joseki." Thirty-five model trick plays are presented in this chapter, #204 and exhaustively analyzed in the style of a dictionary. Kageyama Toshiro 7 dan, one of the most popular go writers, examines the subject in Chapter 2 from the standpoint of full board strategy. Chapter 3 is written by Mihori Sho, who collaborated with Sakata Eio to produce Killer of Go. Anecdotes from the history of go, famous sayings by Sun Tzu on the Art of Warfare and contemporary examples of trickery are woven together to produce an entertaining dialogue. The final chapter presents twenty-five problems for the reader to solve, using the knowledge gained in the preceding sections. Do not be surprised to find unexpected booby traps lurking here also. -
1 Introduction
An Investigation of an Evolutionary Approach to the Opening of Go Graham Kendall, Razali Yaakob Philip Hingston School of Computer Science and IT School of Computer and Information Science University of Nottingham, UK Edith Cowan University, WA, Australia {gxk|rby}@cs.nott.ac.uk [email protected] Abstract – The game of Go can be divided into three stages; even told how it had performed in each individual game. the opening, the middle, and the end game. In this paper, The conclusion of Fogel and Chellapilla is that, without evolutionary neural networks, evolved via an evolutionary any expert knowledge, a program can learn to play a game strategy, are used to develop opening game playing strategies at an expert level using a co-evolutionary approach. for the game. Go is typically played on one of three different board sizes, i.e., 9x9, 13x13 and 19x19. A 19x19 board is the Additional information about this work can be found in standard size for tournament play but 9x9 and 13x13 boards [19, 20, 21, 22]. are usually used by less-experienced players or for faster Go is considered more complex than chess or checkers. games. This paper focuses on the opening, using a 13x13 One measure of this complexity is the number of positions board. A feed forward neural network player is played against that can be reached from the starting position which Bouzy a static player (Gondo), for the first 30 moves. Then Gondo and Cazenave estimate to be 10160 [23]. Chess and takes the part of both players to play out the remainder of the checkers are about 1050 and 1017, respectively [23]. -
Eyespace Values in Go
Games of No Chance MSRI Publications Volume 29, 1996 Eyespace Values in Go HOWARD A. LANDMAN Abstract. Most of the application of combinatorial game theory to Go has been focussed on late endgame situations and scoring. However, it is also possible to apply it to any other aspect of the game that involves counting. In particular, life-and-death situations often involve counting eyes. Assuming all surrounding groups are alive, a group that has two or more eyes is alive, and a group that has one eye or less is dead. This naturally raises the question of which game-theoretical values can occur for an eyemaking game. We define games that provide a theoretical framework in which this question can be asked precisely, and then give the known results to date. For the single-group case, eyespace values include 0, R R R R R 1 1 3 1 1, 2, 2 , 1 2 , 4 , 1 4 , 1 ,andseveralko-relatedloopygames,aswell ∗ R 1 as some seki-related values. The 2 eye is well-understood in traditional R R R R 1 3 1 Go theory, and 1 2 only a little less so, but 4 , 1 4 ,and 1 may be new discoveries, even though they occur frequently in actual games.∗ For a battle between two or more opposed groups, the theory gets more complicated. 1. Go 1.1. Rules of Go. Go is played on a square grid with Black and White stones. The players alternate turns placing a stone on an unoccupied intersection. Once placed, a stone does not move, although it may sometimes be captured and removed from the board. -
Active Opening Book Application for Monte-Carlo Tree Search in 19×19 Go
Active Opening Book Application for Monte-Carlo Tree Search in 19×19 Go Hendrik Baier and Mark H. M. Winands Games and AI Group, Department of Knowledge Engineering Maastricht University, Maastricht, The Netherlands Abstract The dominant approach for programs playing the Asian board game of Go is nowadays Monte-Carlo Tree Search (MCTS). However, MCTS does not perform well in the opening phase of the game, as the branching factor is high and consequences of moves can be far delayed. Human knowledge about Go openings is typically captured in joseki, local sequences of moves that are considered optimal for both players. The choice of the correct joseki in a given whole-board position, however, is difficult to formalize. This paper presents an approach to successfully apply global as well as local opening moves, extracted from databases of high-level game records, in the MCTS framework. Instead of blindly playing moves that match local joseki patterns (passive opening book application), knowledge about these moves is integrated into the search algorithm by the techniques of move pruning and move biasing (active opening book application). Thus, the opening book serves to nudge the search into the direction of tried and tested local moves, while the search is able to filter out locally optimal, but globally problematic move choices. In our experiments, active book application outperforms passive book application and plain MCTS in 19×19 Go. 1 Introduction Since the early days of Artificial Intelligence (AI) as a scientific field, games have been used as testbed and benchmark for AI architectures and algorithms. The board game of Go, for example, has stimulated con- siderable research efforts over the last decades, second only to computer chess. -
The Official AGA Tournament Guide
The Official AGA Tournament Guide by Ken Koester Copyright © The American Go Association 2006 Table of Contents Part I: The Tasks Introduction........................................................................................................................................... v Chapter 1. Planning.............................................................................................................................1 Theme and System ................................................................................................................1 Budgeting, or, The Tournament on $15 and $20 a Day .................................................... 6 In Need of Assistance ...........................................................................................................8 Timetables ...............................................................................................................................9 Chapter 2. Preparation .....................................................................................................................11 Time and Place .....................................................................................................................11 Getting the Word Out ..........................................................................................................12 Food for Thought .................................................................................................................15 The Prize Is Right .................................................................................................................16 -
By Arend Bayer, Daniel Bump, Evan Berggren Daniel, David Denholm
Documentation for the GNU Go Project Edition 3.8 July, 2003 By Arend Bayer, Daniel Bump, Evan Berggren Daniel, David Denholm, Jerome Dumonteil, Gunnar Farneb¨ack, Paul Pogonyshev, Thomas Traber, Tanguy Urvoy, Inge Wallin GNU Go 3.8 Copyright c 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007 and 2008 Free Software Foundation, Inc. This is Edition 3.8 of The GNU Go Project documentation, for the 3.8 version of the GNU Go program. Published by the Free Software Foundation Inc 51 Franklin Street, Fifth Floor Boston, MA 02110-1301 USA Tel: 617-542-5942 Permission is granted to make and distribute verbatim or modified copies of this manual is given provided that the terms of the GNU Free Documentation License (see Section A.5 [GFDL], page 218, version 1.3 or any later version) are respected. Permission is granted to make and distribute verbatim or modified copies of the program GNU Go is given provided the terms of the GNU General Public License (see Section A.1 [GPL], page 207, version 3 or any later version) are respected. i Table of Contents ::::::::::::::::::::::::::::::::::::::::::::::::::::::: 1 1 Introduction::::::::::::::::::::::::::::::::::::: 2 1.1 About GNU Go and this Manual ::::::::::::::::::::::::::::::: 2 1.2 Copyrights ::::::::::::::::::::::::::::::::::::::::::::::::::::: 2 1.3 Authors :::::::::::::::::::::::::::::::::::::::::::::::::::::::: 3 1.4 Thanks :::::::::::::::::::::::::::::::::::::::::::::::::::::::: 3 1.5 Development ::::::::::::::::::::::::::::::::::::::::::::::::::: 3 2 Installation :::::::::::::::::::::::::::::::::::::: -
Sydney Go Journal Issue Date - September 2006
Author – David Mitchell on behalf of The Sydney Go Club Sydney Go Journal Issue Date - September 2006 This journal was created with the assistance of the following software (in alphabetic order): Go Game Assistant http://www.go- assistant.com/english/ Used for game and reference diagrams, cataloguing and cross reference. Cost US$29.00 GoDrago http://www.godrago.net/en.htm Used for printing full game scores with a clear font and not missing Ko or captured stones. Freeware MoyoGo http://www.moyogo.com/ Used for game analysis, joseki, fuseki and example games. Cost US$62.00 Why three applications… simple really, none of them deliver everything, so I use the strengths of each (or perhaps it’s because I am old, grumpy and don’t like fighting with software). Bottom line – if you think you want Go software, first determine what you want, and look for the package that fits your need. © Copyright 2006 – David Mitchell – reuse of material for non-profit or personal use permitted. Author – David Mitchell on behalf of The Sydney Go Club Contents Tesuji Page 3 Tesuji Problems Page 6 Pro Games Page 9 Commented Game Page 13 Ben Bildstein vs Carl Jiang Tesuji Answers Page 29 Japanese Go Terms Page 31 Copyright Page 33 The Sydney Go Club Meets Friday nights at :- At Philas House 17 Brisbane St Surry Hills From 5.00pm Entrance fee - $3 per head includes tea and coffee. For further information from Robert [email protected] © Copyright 2006 – David Mitchell – reuse of material for non-profit or personal use permitted.