Sydney Go Journal Issue Date - September 2006
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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 & -
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 .......................................................................................................... -
Hebsacker-Verlag, PDF-Katalog 03/2017
Hebsacker-Verlag, PDF-Katalog 03/2017 Der Hebsacker Verlag ist eine Gesellschaft bürgerlichen Rechts (GbR). 2002 wurde sie von den beiden heutigen Inhabern Steffi Hebsacker und Tobias Berben mit dem Ziel gegründet, die Verbreitung des Go-Spiels in Deutschland zu fördern. Der Anstoss zur Gründung eines Spiele- und Buchverlags ergab sich aus der Tatsache, dass der Ravensburger Spieleverlag nach vielen Jahren die Produktion von preisgünstigen Go-Spielen eingestellt hatte. Ersatz war gefragt, um gerade auch Kindern und Jugendlichen ein bezahlbares Go-Spiel anbieten zu können. Steffi Hebsacker entwarf daher ein Go-Spiel aus Papier und Pappe und setzte dessen Produktion mit Unterstützung des Deutschen Go-Bundes e. V. um. Nahezu zeitgleich realisierte Tobias Berben den Neudruck des Go-Buch-Klassikers "Go. Die Mitte des Himmels" von Micheal Koulen, der beim Kölner DuMont Verlag in drei Auflagen erschienen, aber nicht wieder aufgelegt worden war. Zusammen gründeten dann beide als Rahmen für ihre Projekte den gemeinsamen Verlag. Relativ bald wurde klar, dass für den Verlag eine eigene Website sowie ein Webshop eingerichtet werden muss. Im Frühjahr 2003 folgte der Ausbau des Webshops auf ein umfassendes Angebot an Go-Spielmaterial und -Büchern, 2004 die Umstellung auf die heute verwendete, leistungsfähige Shop-Software. Bis heute folgte eine Vielzahl unterschiedlicher Projekte und ein kontinuierlicher Ausbau des Webshops, dessen Angobot nun auch andere Denk- und Strategiespiele umfasst. Neben dem Verlag und dem Shop ist die redaktionelle sowie technische Betreuung der Deutschen Go-Zeitung eine unserer zentralen Aktivitäten. Zudem veranstalten wir Seminare und Turniere und sponsern einen Jugendpreis, eine Bundesligamannschaft sowie zwei Websites. Dieser Katalog umfasst alle derzeit bei uns erhältlichen Artikel. -
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. -
Every Team Deserves a Second Chance: an Interactive 9X9 Go Experience (Demonstration)
Every Team Deserves a Second Chance: An Interactive 9x9 Go Experience (Demonstration) Leandro Soriano Marcolino1, Vaishnavh Nagarajan2, Milind Tambe1 1 University of Southern California, Los Angeles, CA, 90089, USA {sorianom, tambe}@usc.edu 2 Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India [email protected] ABSTRACT team is failing in problem solving, we can increase the allocation We show that without using any domain knowledge, we can predict of resources. Or consider a team playing together a game against an the final performance of a team of voting agents, at any step towards opponent (such as board games, or poker). Different teams might solving a complex problem. This demo allows users to interact with play better against different opponents. Hence, if we notice that a our system, and observe its predictions, while playing 9x9 Go. team is having issues, we could dynamically change it. Under time constraints, however, such prediction must be done quickly. Our approach is based on a prediction model derived from a Categories and Subject Descriptors graphical representation of the problem-solving process, where the I.2.1 [Artificial Intelligence]: Applications and Expert Systems final outcome is modeled as a random variable that is influenced by the subsets of agents that agreed together over the actions taken at Keywords each step towards solving the problem. Hence, our representation has no dependency on the domain. Teamwork; Single and multiagent learning; Social choice In this demo, the user will be able to interact with our system, while playing 9x9 Go games against a team of agents. -
EZGO: an Overview
GO Overview Metaphorical Go Philosophy and Go The Dinosaur’s Hindbrain Ranks & Handicaps The Wolf Pack Go on the Information Superhighway 1 EZ This section (Overview) comes from the book EZ-GO — Oriental Strategy in a Nutshell by Bruce and Sue Wilcox. ISBN 0-9652235-4-X Copyright © 1996 by Bruce Wilcox All Rights Reserved You may photocopy this section only, exactly as is, with this cover page, and distribute it free of charge as a teaching aid. Pages of this book are not to be reproduced otherwise without written permission. This book is out of print in paper but a PDF version has been created. It can be ordered for US$25 or 20 Euro or 15 pounds sterling for CD including shipping & handling. Foreign currency must be in cash (no checks). Paypal also accepted. Send check/cash to Bruce Wilcox, 1169 Laurel Lane, San Luis Obispo, CA 93401 [email protected] 2 GO Metaphorical Go Metaphorical Go he sheriff stepped out onto the dusty street. The sun burned directly overhead, blinding him for a second. Facing the sheriff, the outlaw tensed his hands over his holsters. The outlaw Tbegan to reach for his gun, and, with a loud crack, it was all over. The outlaw, in disbelief, fell to the ground. Bobby Fisher hunched over the chess board, gazing sightlessly at the hand-carved wooden pieces. One could almost see the wheels turning in his head, looking at sequence after sequence, trying to find a way to save his beleaguered king. There! Was that it? His mind reviewed the sequence of moves he had just imagined. -
Going-Insurrection-Read.Pdf
Introduction The game of Go originated in China or Tibet at least 3500 years ago, and in its simplicity and complexity, it remains the greatest strategic game that exists. Part of its interest is that it is quite abstract, just stones on a grid, and so it lends itself well to interpretation. The most obvious analogy for the game is war, but Go is not chess, where the pieces have military names and are lined up facing each other, making the war analogy inescapable. In fact, in many ways, the traditional image of war as opposing nation states advancing on each other is not applicable to Go. The lines are not so clearly drawn, and rather than starting with a full army that gets picked apart, the Go board begins empty and the players create the geography of the game together. Through its simplicity, Go can become a metaphor for thinking about conflict and struggle more generally. In modern North American society, conflict is everywhere, and overwhelmingly it is a one-sided battle constantly waged by the economic and political elites against everyone else. This conflict is visible in the spread of security cameras and other technologies of surveillance; in the growth of prisons and the expansion of police forces; in the ongoing wars of occupation waged by imperialist nations to secure access to resources; in the ongoing colonization carried out against Indigenous Peoples to undercut their resistance and steal their territories; in the threat of being fired or evicted if we aren't subservient enough; in the mass media that teaches us to submit; and in our relationships where we exploit each other, mirroring the systems of domination we were raised to identify with. -
Monte-Carlo Tree Search Using Expert Knowledge: an Application to Computer Go and Human Genetics
Curso 2012/13 CIENCIAS Y TECNOLOGÍAS/23 I.S.B.N.: 978-84-15910-90-9 SANTIAGO BASALDÚA LEMARCHAND Monte-Carlo tree search using expert knowledge: an application to computer go and human genetics Directores J. MARCOS MORENO VEGA CARLOS A. FLORES INFANTE SOPORTES AUDIOVISUALES E INFORMÁTICOS Serie Tesis Doctorales ciencias 23 (Santiago Basaldúa Lemarchand).indd 1 18/02/2014 11:24:43 Universidad de La Laguna Abstract Monte-Carlo Tree Search Using Expert Knowledge: An Application to Computer Go and Human Genetics During the years in which the research described in this PhD dissertation was done, Monte-Carlo Tree Search has become the preeminent algorithm in many AI and computer science fields. This dissertation analyzes how expert knowledge and also online learned knowledge can be used to enhance the search. The work describes two different implementations: as a two player search in computer go and as an optimization method in human genetics. It is established that in large problems MCTS has to be combined with domain specific or online learned knowledge to improve its strength. This work analyzes different successful ideas about how to do it, the resulting findings and their implications, hence improving our insight of MCTS. The main contributions to the field are: an analytical mathematical model improving the understanding of simulations, a problem definition and a framework including code and data to compare algorithms in human genetics and three successful implementations: in the field of 19x19 go openings named M-eval, in the field of learning playouts and in the field of genetic etiology. Also, an open source integer representation of proportions as Win/Loss States (WLS), a negative result in the field of playouts, an unexpected finding of a possible problem in optimization and further insight on the limitations of MCTS are worth mentioning. -
UCT-Enhanced Deep Convolutional Neural Network for Move Recommendation in Go
MQP CDR#GXS 1501 UCT-Enhanced Deep Convolutional Neural Network for Move Recommendation in Go a Major Qualifying Project Report submitted to the faculty of the WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for th e Degree of Bachelor of Science by Sarun Paisarnsrisomsuk Pitchaya Wiratchotisatian April 30, 2015 Professor Gábor N. Sárközy, Major Advisor Abstract Deep convolutional neural networks (CNN) have been proved to be useful in predicting moves of human Go experts [10], [12]. Combining Upper Confidence bounds applied to Trees (UCT) with a large deep CNN creates an even more powerful artificial intelligence method in playing Go [12]. Our project introduced a new feature, board patterns at the end of a game, used as inputs to the network. We implemented a small, single-thread, 1-hidden-layer deep CNN without using GPU, and trained it by supervised learning from a database of human professional games. By adding the new feature, our best model was able to correctly predict the experts’ move in 18% of the positions, compared to 6% without this feature. In practice, even though the board pattern at the end of the game is invisible to Go players until the very end of the game, collecting these statistics during each simulation in UCT can approximate the likelihood of which parts of the board belong to the players. We created another dataset by using UCT to simulate a number of playouts and calculating the average influence value of Black and White positions in those boards at the end of the simulations. We used this dataset as the input to the deep CNN and found that the network was able to correctly predict the experts’ move in 9% of positions. -
Sydney Go Journal
Sydney Go Journal Issue Date – July 2007 (Happy Birthday to us – 1 year old! – well done team!) ACT Championships Saturday and Sunday July 28, 29. Venue will be the School of Arts, ANU Campus. Contact: Jason Wright [email protected] 0438464535 for details. Special thanks to Devon Bailey, Tony Oxenham and Geoffrey Gray for proof‐reading this edition and correcting my mistakes. Contributions, comments and suggestions for the SGJ to: [email protected] Contents News & Reviews 3 Eland Cup Quarter Finals 6 Fuseki Problems 20 WAGC Tournament 23 NSW Tournament 34 Fuseki Answers 42 The Sydney Go Club Meets Friday nights from 5.00pm at Philas House, 17 Brisbane St, Surry Hills Entrance fee ‐ $5 per head; Concession $3; Children free ‐ includes tea and coffee. For further information from Robert [email protected] For weekly articles and commented games subscribe to GoAma See page 5 News and Reviews for details. Want to know what's going on in Australian Go? Visit: http://www.australiango.asn.au/ Web Site review News Below is a list of the best educational Go Web sites. They have a mixture of content, some free, some paid. SGJ to be published bimonthly This section will be a feature of future editions and I For the past year we have been able to produce a will add more sites as they are identified. journal one a month. Due to work and other commitments we have decided to publish the journal every two months. The next journal will be in September and all ‘odd numbered’ months from now on. -
Every Team Deserves a Second Chance: an Interactive 9X9 Go Experience (Demonstration)
Every Team Deserves a Second Chance: An Interactive 9x9 Go Experience (Demonstration) Leandro Soriano Marcolino1, Vaishnavh Nagarajan2, Milind Tambe1 1 University of Southern California, Los Angeles, CA, 90089, USA {sorianom, tambe}@usc.edu 2 Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India [email protected] ABSTRACT team is failing in problem solving, we can increase the allocation We show that without using any domain knowledge, we can predict of resources. Or consider a team playing together a game against an the final performance of a team of voting agents, at any step towards opponent (such as board games, or poker). Different teams might solving a complex problem. This demo allows users to interact with play better against different opponents. Hence, if we notice that a our system, and observe its predictions, while playing 9x9 Go. team is having issues, we could dynamically change it. Under time constraints, however, such prediction must be done quickly. Our approach is based on a prediction model derived from a Categories and Subject Descriptors graphical representation of the problem-solving process, where the I.2.1 [Artificial Intelligence]: Applications and Expert Systems final outcome is modeled as a random variable that is influenced by the subsets of agents that agreed together over the actions taken at Keywords each step towards solving the problem. Hence, our representation has no dependency on the domain. Teamwork; Single and multiagent learning; Social choice In this demo, the user will be able to interact with our system, while playing 9x9 Go games against a team of agents. -
River Mountain Go Volume 2: 20K to 8K
River Mountain Go Volume 2: 20k to 8k By Oliver Richman v 0.4.8 (October 7th, 2006) 2 Contents 0.1 Acknowledgements ......................... 6 0.2 Introduction ............................. 7 0.2.1 Japanese Technical Terms ................. 7 0.3 What you should Already Know .................. 7 1 Go Proverbs 9 1.1 My thoughts about this opening .................. 17 2 Hiraki 19 2.1 Hiraki on the sides ......................... 19 2.2 Hiraki from a corner enclosure ................... 22 3 Sente 25 3.1 Tasting sente ............................ 25 3.2 Everything you ever wanted to know about Sente ......... 26 3.3 Sente in the Opening ........................ 27 3.4 Sente in the Middle Game ..................... 32 3.5 Conclusions about Sente ...................... 33 3.6 Honte ................................ 34 3.7 Sente Sequences ........................... 36 3.8 Making effective use of Sente .................... 37 3 4 CONTENTS 4 The Magic of Moyos 39 4.1 Black should make an extension from his corner enclosure .... 40 4.2 Black should make a shimari in the upper right corner ....... 41 4.3 A True Moyo Move ......................... 42 4.4 Black should fix his weak group first ................ 43 5 Joseki 45 5.1 Good and Bad Advice ........................ 46 5.2 Common Star Point Josekis ..................... 46 5.3 Star Point Pincer Josekis and the 3-3 Invasion ........... 48 5.4 And now for something completely different ............ 49 5.5 Final thoughts on the star point ................... 50 5.6 Komoku Josekis every 10 kyu should know ............ 51 6 Your First Go Lesson 53 6.1 Clear your mind ........................... 53 6.2 Nirensei is your friend ....................... 53 6.3 The Three Qualities ........................