Appendix II: 2011 Super Computer Go: Shih
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W2go4e-Book.Pdf
American Go Association The AGA is dedicated to promotion of the game of go in America. It works to encourage people to learn more about and enjoy this remarkable game and to strengthen the U.S. go playing community. The AGA: • Publishes the American Go e-Journal, free to everyone with Legal Note: The Way To Go is a copyrighted work. special weekly editions for members Permission is granted to make complete copies for • Publishes the American Go Journal Yearbook – free to members personal use. Copies may be distributed freely to • Sanctions and promotes AGA-rated tournaments others either in print or electronic form, provided • Maintains a nationwide rating system no fee is charged for distribution and all copies contain • Organizes the annual U.S. Go Congress and Championship this copyright notice. • Organizes the summer U.S. Go Camp for children • Organizes the annual U.S. Youth Go Championship • Manages U.S. participation in international go events Information on these services and much more is available at the AGA’s website at www.usgo.org. E R I C M A N A American Go Association G Box 397 Old Chelsea Station F O O N U I O New York, NY 10113 N D A T http://www.usgo.org American Go Foundation The American Go Foundation is a 501(c)(3) charitable organiza- tion devoted to the promotion of go in the United States. With our help thousands of youth have learned go from hundreds of teachers. Cover print: Two Immortals and the Woodcutter Our outreach includes go related educational and cultural activities A watercolor by Seikan. -
Openbsd Gaming Resource
OPENBSD GAMING RESOURCE A continually updated resource for playing video games on OpenBSD. Mr. Satterly Updated August 7, 2021 P11U17A3B8 III Title: OpenBSD Gaming Resource Author: Mr. Satterly Publisher: Mr. Satterly Date: Updated August 7, 2021 Copyright: Creative Commons Zero 1.0 Universal Email: [email protected] Website: https://MrSatterly.com/ Contents 1 Introduction1 2 Ways to play the games2 2.1 Base system........................ 2 2.2 Ports/Editors........................ 3 2.3 Ports/Emulators...................... 3 Arcade emulation..................... 4 Computer emulation................... 4 Game console emulation................. 4 Operating system emulation .............. 7 2.4 Ports/Games........................ 8 Game engines....................... 8 Interactive fiction..................... 9 2.5 Ports/Math......................... 10 2.6 Ports/Net.......................... 10 2.7 Ports/Shells ........................ 12 2.8 Ports/WWW ........................ 12 3 Notable games 14 3.1 Free games ........................ 14 A-I.............................. 14 J-R.............................. 22 S-Z.............................. 26 3.2 Non-free games...................... 31 4 Getting the games 33 4.1 Games............................ 33 5 Former ways to play games 37 6 What next? 38 Appendices 39 A Clones, models, and variants 39 Index 51 IV 1 Introduction I use this document to help organize my thoughts, files, and links on how to play games on OpenBSD. It helps me to remember what I have gone through while finding new games. The biggest reason to read or at least skim this document is because how can you search for something you do not know exists? I will show you ways to play games, what free and non-free games are available, and give links to help you get started on downloading them. -
Table of Contents 129
Table of Contents 129 TABLE OF CONTENTS Table of Contents ......................................................................................................................................................129 Science and Checkers (H.J. van den Herik) .............................................................................................................129 Searching Solitaire in Real Time (R. Bjarnason, P. Tadepalli, and A. Fern)........................................................ 131 An Efficient Approach to Solve Mastermind Optimally (L-T. Huang, S-T. Chen, S-Ch. Huang, and S.-S. Lin) ...................................................................................................................................... 143 Note: ................................................................................................................................................................. 150 Gentlemen, Stop your Engines! (G. McC. Haworth).......................................................................... 150 Information for Contributors............................................................................................................................. 157 News, Information, Tournaments, and Reports: ......................................................................................................158 The 12th Computer Olympiad (Continued) (H.J. van den Herik, M.H.M. Winands, and J. Hellemons).158 DAM 2.2 Wins Draughts Tournament (T. Tillemans) ........................................................................158 -
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CSC321 Lecture 23: Go
CSC321 Lecture 23: Go Roger Grosse Roger Grosse CSC321 Lecture 23: Go 1 / 22 Final Exam Monday, April 24, 7-10pm A-O: NR 25 P-Z: ZZ VLAD Covers all lectures, tutorials, homeworks, and programming assignments 1/3 from the first half, 2/3 from the second half If there's a question on this lecture, it will be easy Emphasis on concepts covered in multiple of the above Similar in format and difficulty to the midterm, but about 3x longer Practice exams will be posted Roger Grosse CSC321 Lecture 23: Go 2 / 22 Overview Most of the problem domains we've discussed so far were natural application areas for deep learning (e.g. vision, language) We know they can be done on a neural architecture (i.e. the human brain) The predictions are inherently ambiguous, so we need to find statistical structure Board games are a classic AI domain which relied heavily on sophisticated search techniques with a little bit of machine learning Full observations, deterministic environment | why would we need uncertainty? This lecture is about AlphaGo, DeepMind's Go playing system which took the world by storm in 2016 by defeating the human Go champion Lee Sedol Roger Grosse CSC321 Lecture 23: Go 3 / 22 Overview Some milestones in computer game playing: 1949 | Claude Shannon proposes the idea of game tree search, explaining how games could be solved algorithmically in principle 1951 | Alan Turing writes a chess program that he executes by hand 1956 | Arthur Samuel writes a program that plays checkers better than he does 1968 | An algorithm defeats human novices at Go 1992 -
Residual Networks for Computer Go Tristan Cazenave
Residual Networks for Computer Go Tristan Cazenave To cite this version: Tristan Cazenave. Residual Networks for Computer Go. IEEE Transactions on Games, Institute of Electrical and Electronics Engineers, 2018, 10 (1), 10.1109/TCIAIG.2017.2681042. hal-02098330 HAL Id: hal-02098330 https://hal.archives-ouvertes.fr/hal-02098330 Submitted on 12 Apr 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. IEEE TCIAIG 1 Residual Networks for Computer Go Tristan Cazenave Universite´ Paris-Dauphine, PSL Research University, CNRS, LAMSADE, 75016 PARIS, FRANCE Deep Learning for the game of Go recently had a tremendous success with the victory of AlphaGo against Lee Sedol in March 2016. We propose to use residual networks so as to improve the training of a policy network for computer Go. Training is faster than with usual convolutional networks and residual networks achieve high accuracy on our test set and a 4 dan level. Index Terms—Deep Learning, Computer Go, Residual Networks. I. INTRODUCTION Input EEP Learning for the game of Go with convolutional D neural networks has been addressed by Clark and Storkey [1]. It has been further improved by using larger networks [2]. -
Achieving Master Level Play in 9X9 Computer Go
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Achieving Master Level Play in 9 9 Computer Go × Sylvain Gelly∗ David Silver† Univ. Paris Sud, LRI, CNRS, INRIA, France University of Alberta, Edmonton, Alberta, Canada Abstract simulated, using self-play, starting from the current position. Each position in the search tree is evaluated by the average The UCT algorithm uses Monte-Carlo simulation to estimate outcome of all simulated games that pass through that po- the value of states in a search tree from the current state. However, the first time a state is encountered, UCT has no sition. The search tree is used to guide simulations along knowledge, and is unable to generalise from previous expe- promising paths. This results in a highly selective search rience. We describe two extensions that address these weak- that is grounded in simulated experience, rather than an ex- nesses. Our first algorithm, heuristic UCT, incorporates prior ternal heuristic. Programs using UCT search have outper- knowledge in the form of a value function. The value function formed all previous Computer Go programs (Coulom 2006; can be learned offline, using a linear combination of a million Gelly et al. 2006). binary features, with weights trained by temporal-difference Monte-Carlo tree search algorithms suffer from two learning. Our second algorithm, UCT–RAVE, forms a rapid sources of inefficiency. First, when a position is encoun- online generalisation based on the value of moves. We ap- tered for the first time, no knowledge is available to guide plied our algorithms to the domain of 9 9 Computer Go, the search. -
Computer Go: from the Beginnings to Alphago Martin Müller, University of Alberta
Computer Go: from the Beginnings to AlphaGo Martin Müller, University of Alberta 2017 Outline of the Talk ✤ Game of Go ✤ Short history - Computer Go from the beginnings to AlphaGo ✤ The science behind AlphaGo ✤ The legacy of AlphaGo The Game of Go Go ✤ Classic two-player board game ✤ Invented in China thousands of years ago ✤ Simple rules, complex strategy ✤ Played by millions ✤ Hundreds of top experts - professional players ✤ Until 2016, computers weaker than humans Go Rules ✤ Start with empty board ✤ Place stone of your own color ✤ Goal: surround empty points or opponent - capture ✤ Win: control more than half the board Final score, 9x9 board ✤ Komi: first player advantage Measuring Go Strength ✤ People in Europe and America use the traditional Japanese ranking system ✤ Kyu (student) and Dan (master) levels ✤ Separate Dan ranks for professional players ✤ Kyu grades go down from 30 (absolute beginner) to 1 (best) ✤ Dan grades go up from 1 (weakest) to about 6 ✤ There is also a numerical (Elo) system, e.g. 2500 = 5 Dan Short History of Computer Go Computer Go History - Beginnings ✤ 1960’s: initial ideas, designs on paper ✤ 1970’s: first serious program - Reitman & Wilcox ✤ Interviews with strong human players ✤ Try to build a model of human decision-making ✤ Level: “advanced beginner”, 15-20 kyu ✤ One game costs thousands of dollars in computer time 1980-89 The Arrival of PC ✤ From 1980: PC (personal computers) arrive ✤ Many people get cheap access to computers ✤ Many start writing Go programs ✤ First competitions, Computer Olympiad, Ing Cup ✤ Level 10-15 kyu 1990-2005: Slow Progress ✤ Slow progress, commercial successes ✤ 1990 Ing Cup in Beijing ✤ 1993 Ing Cup in Chengdu ✤ Top programs Handtalk (Prof. -
Walnut Creek CDROM Spring 1995 Catalog 1-800-786-9907 • 1-510-674-0821 Fax the Best of Walnut Creek CDROM Is Yours Free*
Walnut Creek CDROM Spring 1995 Catalog 1-800-786-9907 • 1-510-674-0821 Fax The Best of Walnut Creek CDROM is yours Free*. The • You’ll also get fonts, fractals, Best of Walnut Creek CDROM music, clipart, and more. 600 lets you explore in-depth what MegaBytes in total! Walnut Creek CDROM has to offer. • Boot images from our Unix for PC discs so you will With samples from all of our know if your hardware will products, you’ll be able to see boot Slackware Linux or what our CDROM’s will do for FreeBSD you, before you buy. This CDROM contains: • The Walnut Creek CDROM digital catalog - photos and • Index listings of all the descriptions of our all titles programs, photos, and files on all Walnut Creek CDROM If you act now, we’ll include titles $5.00 good toward the purchase of all Walnut Creek CDROM • The best from each disc titles. If you’re only going to including Hobbes OS/2, own one CDROM, this should CICA MS Windows, Simtel be it! March, 1995. MSDOS, Giga Games, Internet Info, Teacher 2000, Call, write, fax, or email your Ultra Mac-Games and Ultra order to us today! Mac-Utilities * The disc is without cost, but the regular shipping charge still applies. • You get applications, games, utilities, photos, gifs, documents, ray-tracings, and animations 2 CALL NOW! 1-800-786-9907 Phone: +1-510-674-0783 • Fax: +1-510-674-0821 • Email: [email protected] • WWW: http://WWW.cdrom.com/ (Alphabetical Index on page 39.) Hi, Sampler - (Best of Walnut Creek) 2 This is Jack and I’ve got another great batch of CICA for Windows 4 Music Workshop 5 CDROM’s for you. -
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. -
GHDL Documentation Release 1.0-Dev
GHDL Documentation Release 1.0-dev Tristan Gingold and contributors Aug 30, 2020 Introduction 1 What is VHDL? 3 2 What is GHDL? 5 3 Who uses GHDL? 7 4 Contributing 9 4.1 Reporting bugs............................................9 4.2 Requesting enhancements...................................... 10 4.3 Improving the documentation.................................... 10 4.4 Fork, modify and pull-request.................................... 11 4.5 Related interesting projects..................................... 11 5 Copyrights | Licenses 13 5.1 GNU GPLv2............................................. 13 5.2 CC-BY-SA.............................................. 14 5.3 List of Contributors......................................... 14 I Getting GHDL 15 6 Releases and sources 17 6.1 Using package managers....................................... 17 6.2 Downloading pre-built packages................................... 17 6.3 Downloading Source Files...................................... 18 7 Building GHDL from Sources 21 7.1 Directory structure.......................................... 22 7.2 mcode backend............................................ 23 7.3 LLVM backend............................................ 23 7.4 GCC backend............................................. 24 8 Precompile Vendor Primitives 27 8.1 Supported Vendors Libraries..................................... 27 8.2 Supported Simulation and Verification Libraries.......................... 28 8.3 Script Configuration......................................... 28 8.4 Compiling on Linux........................................ -
Mohex 2.0: a Pattern-Based MCTS Hex Player
MoHex 2.0: a pattern-based MCTS Hex player Shih-Chieh Huang1,2, Broderick Arneson2, Ryan B. Hayward2, Martin M¨uller2, and Jakub Pawlewicz3 1 DeepMind Technologies 2 Computing Science, University of Alberta 3 Institute of Informatics, University of Warsaw Abstract. In recent years the Monte Carlo tree search revolution has spread from computer Go to many areas, including computer Hex. MCTS Hex players now outperform traditional knowledge-based alpha-beta search players, and the reigning Computer Olympiad Hex gold medallist is the MCTS player MoHex. In this paper we show how to strengthen Mo- Hex, and observe that — as in computer Go — using learned patterns in priors and replacing a hand-crafted simulation policy with a softmax pol- icy that uses learned patterns can significantly increase playing strength. The result is MoHex 2.0, about 250 Elo stronger than MoHex on the 11×11 board, and 300 Elo stronger on 13×13. 1 Introduction In the 1940s Piet Hein [22] and independently John Nash [26–28] invented Hex, the classic two-player alternate-turn connection game. The game is easy to im- plement — in the 1950s Claude Shannon and E.F. Moore built an analogue Hex player based on electrical circuits [29] — but difficult to master, and has often been used as a testbed for artificial intelligence research. Around 2006 Monte Carlo tree search appeared in Go Go [11] and soon spread to other domains. The four newest Olympiad Hex competitors — MoHex from 2008 [4], Yopt from 2009 [3], MIMHex from 2010 [5], Panoramex from 2011 [20] — all use MCTS.