Chapter 6 Two-Player Games

Total Page:16

File Type:pdf, Size:1020Kb

Chapter 6 Two-Player Games Introduction to Using Games in Education: A Guide for Teachers and Parents Chapter 6 Two-Player Games There are many different kinds of two-person games. You may have played a variety of these games such as such as chess, checkers, backgammon, and cribbage. While all of these games are competitive, many people play them mainly for social purposes. A two-person game environment is a situation that facilitates communication and companionship. Two major ideas illustrated in this chapter: 1. Look ahead: learning to consider what your opponent will do as a response to a move that you are planning. 2. Computer as opponent. In essence, this makes a two-player game into a one- player game. In addition, we will continue to explore general-purpose, high-road transferable, problem-solving strategies. Tic-Tac-Toe To begin, we will look at the game of tic-tac-toe (TTT). TTT is a two-player game, with players taking turns. One player is designated as X and the other as O. A turn consists of marking an unused square of a 3x3 grid with one’s mark (an X or an O). The goal is to get three of one’s mark in a file (vertical, horizontal, or diagonal). Traditionally, X is the first player. A sample game is given below. Page 95 Introduction to Using Games in Education: A Guide for Teachers and Parents X X X O X O Before X's O's X's game first first second begins move move move X X X X O X X O X O O O X O X O X O X O X O O's X's O's X wins on second third third X's fourth move move move move Figure 6.1. Example of a Tic-Tac-Toe game. TTT provides a good environment in which to explore how a computer can play a game. You are familiar with the idea of random numbers. For example, if you designate one side of a “true” coin as a 0, and the other side as a 1, then repeating flipping this coin generates a random sequence of 0’s and 1’s. If you repeatedly toss a “true” six-sided die, you will generate a random sequence of integers in the range [1, 6]. In many games, a spinner is used to generate random moves. The process of shuffling a deck of cards is a process of randomizing the locations of the cards in a deck. Randomness is a complex area of study. For example, suppose you throw a pair of “true” dice, and add up the total. You will get an integer in the range [2,12]. However, a sequence of such throws will not produce a random sequence of number in this range. Some numbers, such as 2 and 12, will occur much less frequency than others. The number 7 will occur far more frequently than 2 or 12. The study of this and related types of situations is part of the field of probability. It turns out that random numbers are quite important in many non-game settings. For example, an educational researcher is conducting an experiment in which one set of students receives a certain treatment, and a different set receives a different treatment. To decide which student gets a particular treatment, all of the student names are written on identical small pieces of cardboard and places in a box. The box is then thoroughly shakes and contents thoroughly stirred. Then names are drawn out one at a time, alternately placing the name into the group to receive the first treatment or the group to receive the second treatment. Computer programs have been developed that generate pseudorandom numbers. (See http://en.wikipedia.org/wiki/Pseudorandomness.) Quoting from the Wikipedia: A pseudo-random variable is a variable which is created by a deterministic procedure (often a computer program or subroutine) which (generally) takes random bits as input. The pseudo-random string will typically Page 96 Introduction to Using Games in Education: A Guide for Teachers and Parents be longer than the original random string, but less random (less entropy, in the information theory sense). This can be useful for randomized algorithms. Pseudo-random number generators are widely used in such applications as computer modeling (e.g., Markov chains), statistics, experimental design, etc. Some of them are sufficiently random to be useful in these applications. Many are not, and considerable sophistication is required to correctly determine the difference for any particular purpose. You know that computers can play some games quite well. Perhaps you believe that is a computer has been programmed to play a game, the computer will never lose. However, that is not the case. Let’s use TTT for an example. Suppose that a computer has been programmed to make random moves when playing against a human opponent. When it is the computer’s turn to make a move, it selects one of the legal available moves at random, and makes that move. As you might expect, a human player can often beat such a compute program. Your students may enjoy creating a “by hand” simulation of this situation. Prepare nine small pieces of paper that are numbered 1, 2, … 9, respectively, and place them in a small box. Number the spaces of a TTT board with the nine digits as follows: 1 2 3 4 5 6 7 8 9 Figure 6.2. A TTT board with its squares numbered 1 to 9. Use of Random Moves in a Game Let us suppose, as an example, that X is going to play first and that X’s moves will be randomly generated. You play O against the random mover. Start at step 1. 1. To generate X’s move, stir up the pieces of paper in the box and draw one out. Its number will be the space in which X moves. Then one of the following 3 situations occurs: 1a. If this move completes a file with three X’s, X wins and the game ends. 1b. Otherwise, if this is the ninth move in the game, the game ends and is a draw. 1c. Otherwise, go to step 2. 2. You (O) make a move. If this produces a file with three O’s, you win and the game ends. Otherwise, look into the box and remove the slip of paper that contains the number corresponding to the move you just made. Then go to step 1. The set of steps can easily be written as a computer program. The set of steps is an algorithm that generates moves for X and determines who wins or if the game is a draw. It should be evident to you that just because a computer has been programmed to play a game it does not Page 97 Introduction to Using Games in Education: A Guide for Teachers and Parents follow that the computer wins all the time or will always play well. Indeed, the random number player will play poorly. However, it will occasionally best a child who is just learning to play the game. The idea of using random numbers in a computer program adds an unpredictability dimension to what a computer can or cannot do. People often argue about limitations of computers by stating that a computer can only do what it is told to do in a computer program. However, when this “telling the computer” includes making use of random numbers, the programmer or the program user may not be able to accurately predict the results. A TTT Algorithm that Never Loses Next, we will explore a TTT algorithm that can be followed by a person or programmed into a computer, and that plays quite well. This algorithm is specifically designed to produce moves for X, who moves first. Begin by numbering the nine squares on the grid as follows: 2 6 3 7 1 8 4 9 5 Figure 6.3. TTT board numbered to help specify a game-playing algorithm. The first player (X) uses the following 4-part procedure to determine what move to make at each turn: 1. Examine the grid and carry out the following sub-steps: 1a. If there are one or more files that contain 2 X’s and no O’s, play in the one that contains the lowest numbered blank space. Thus, you win the game, and the game ends. 1b. Otherwise, if there is only one blank square remaining, play in it. The game then ends as a draw. 1c. Otherwise, go to step 2. 2. If there is a file containing 2 O’s and no X, play in that file. Otherwise: 3. Consider each possible remaining legal move, from the lowest numbered one to the highest numbered one. For each, see if making that move would result in the creation of two or more distinct files each containing two X’s and no O’s. If (and as soon as) such a possible move is discovered, make it. Otherwise: 4. Move in the lowest numbered unused square. Page 98 Introduction to Using Games in Education: A Guide for Teachers and Parents Through some careful thought, you should be able to convince yourself that X (playing first) never loses. This algorithm that never loses is dependent on X going first, on the board being numbered as shown, and on the “look ahead” feature in step 3. Use of the look-ahead strategy is a key feature in writing a program that plays a good game of chess, checkers, or other somewhat similar games.
Recommended publications
  • Paper and Pencils for Everyone
    (CM^2) Math Circle Lesson: Game Theory of Gomuku and (m,n,k-games) Overview: Learning Objectives/Goals: to expose students to (m,n,k-games) and learn the general history of the games through out Asian cultures. SWBAT… play variations of m,n,k-games of varying degrees of difficulty and complexity as well as identify various strategies of play for each of the variations as identified by pattern recognition through experience. Materials: Paper and pencils for everyone Vocabulary: Game – we will create a working definition for this…. Objective – the goal or point of the game, how to win Win – to do (achieve) what a certain game requires, beat an opponent Diplomacy – working with other players in a game Luck/Chance – using dice or cards or something else “random” Strategy – techniques for winning a game Agenda: Check in (10-15min.) Warm-up (10-15min.) Lesson and game (30-45min) Wrap-up and chill time (10min) Lesson: Warm up questions: Ask these questions after warm up to the youth in small groups. They may discuss the answers in the groups and report back to you as the instructor. Write down the answers to these questions and compile a working definition. Try to lead the youth so that they do not name a specific game but keep in mind various games that they know and use specific attributes of them to make generalizations. · What is a game? · Are there different types of games? · What make something a game and something else not a game? · What is a board game? · How is it different from other types of games? · Do you always know what your opponent (other player) is doing during the game, can they be sneaky? · Do all of games have the same qualities as the games definition that we just made? Why or why not? Game history: The earliest known board games are thought of to be either ‘Go’ from China (which we are about to learn a variation of), or Senet and Mehen from Egypt (a country in Africa) or Mancala.
    [Show full text]
  • Bibliography of Traditional Board Games
    Bibliography of Traditional Board Games Damian Walker Introduction The object of creating this document was to been very selective, and will include only those provide an easy source of reference for my fu- passing mentions of a game which give us use- ture projects, allowing me to find information ful information not available in the substan- about various traditional board games in the tial accounts (for example, if they are proof of books, papers and periodicals I have access an earlier or later existence of a game than is to. The project began once I had finished mentioned elsewhere). The Traditional Board Game Series of leaflets, The use of this document by myself and published on my web site. Therefore those others has been complicated by the facts that leaflets will not necessarily benefit from infor- a name may have attached itself to more than mation in many of the sources below. one game, and that a game might be known Given the amount of effort this document by more than one name. I have dealt with has taken me, and would take someone else to this by including every name known to my replicate, I have tidied up the presentation a sources, using one name as a \primary name" little, included this introduction and an expla- (for instance, nine mens morris), listing its nation of the \families" of board games I have other names there under the AKA heading, used for classification. and having entries for each synonym refer the My sources are all in English and include a reader to the main entry.
    [Show full text]
  • Ai12-General-Game-Playing-Pre-Handout
    Introduction GDL GGP Alpha Zero Conclusion References Artificial Intelligence 12. General Game Playing One AI to Play All Games and Win Them All Jana Koehler Alvaro´ Torralba Summer Term 2019 Thanks to Dr. Peter Kissmann for slide sources Koehler and Torralba Artificial Intelligence Chapter 12: GGP 1/53 Introduction GDL GGP Alpha Zero Conclusion References Agenda 1 Introduction 2 The Game Description Language (GDL) 3 Playing General Games 4 Learning Evaluation Functions: Alpha Zero 5 Conclusion Koehler and Torralba Artificial Intelligence Chapter 12: GGP 2/53 Introduction GDL GGP Alpha Zero Conclusion References Deep Blue Versus Garry Kasparov (1997) Koehler and Torralba Artificial Intelligence Chapter 12: GGP 4/53 Introduction GDL GGP Alpha Zero Conclusion References Games That Deep Blue Can Play 1 Chess Koehler and Torralba Artificial Intelligence Chapter 12: GGP 5/53 Introduction GDL GGP Alpha Zero Conclusion References Chinook Versus Marion Tinsley (1992) Koehler and Torralba Artificial Intelligence Chapter 12: GGP 6/53 Introduction GDL GGP Alpha Zero Conclusion References Games That Chinook Can Play 1 Checkers Koehler and Torralba Artificial Intelligence Chapter 12: GGP 7/53 Introduction GDL GGP Alpha Zero Conclusion References Games That a General Game Player Can Play 1 Chess 2 Checkers 3 Chinese Checkers 4 Connect Four 5 Tic-Tac-Toe 6 ... Koehler and Torralba Artificial Intelligence Chapter 12: GGP 8/53 Introduction GDL GGP Alpha Zero Conclusion References Games That a General Game Player Can Play (Ctd.) 5 ... 18 Zhadu 6 Othello 19 Pancakes 7 Nine Men's Morris 20 Quarto 8 15-Puzzle 21 Knight's Tour 9 Nim 22 n-Queens 10 Sudoku 23 Blob Wars 11 Pentago 24 Bomberman (simplified) 12 Blocker 25 Catch a Mouse 13 Breakthrough 26 Chomp 14 Lights Out 27 Gomoku 15 Amazons 28 Hex 16 Knightazons 29 Cubicup 17 Blocksworld 30 ..
    [Show full text]
  • Jeanne's Favorite Games
    Jeanne’s Favorite Games Game Maker Cognitive functions/operations For Younger Children Blink Out of the Box games comparing at a high rate of speed Toot and Otto Thinkfun planning, hypothetical thinking, sequencing Barnyard Critters Rio Grande Games logic, hypothetical thinking, non competitive LCR (fun family game) George and Co. LLC spatial sense for left and right Open Sesame Ravensburger memory for spatial relationships Walk the Dogs Simply Fun planning, visual transport The Storybook Game Fundex memory, vocabulary, sequencing, forming relationships Camelot Jr. Smart Games hypothetical thinking, logical evidence, planning, Feed the Kitty Gamewright visual representation, spatial orientation Quick Cups Spin Master visual processing speed, color sequencing Go Ape (great family game) Play Monster identify emotions, reading body language Rat-a-tat-tat (card game) Gamewright memory, numbers, planning, relevant cues Penguins On Ice Smart Games spatial sense, hypothetical thinking, planning Slapzi Carma Games language development, understanding negatives (no or not), relevant cues, processing speed Tenzi (fun for groups) Carma Games matching numbers, systematic search, visual processing speed, tons of fun, buy the additional pack of cards “77 Ways to Play Tenzi” Older Children - Adult Skribble TDC Games visualizing, planning, predicting Take Your Pick Simply Fun point of view, predicting Linkity Simply Fun forming relationships, Squint Junior Out of the Box games visualizing, analyzing-integrating, Khet (a very cool laser game) www.khet.com
    [Show full text]
  • A Scalable Neural Network Architecture for Board Games
    A Scalable Neural Network Architecture for Board Games Tom Schaul, Jurgen¨ Schmidhuber Abstract— This paper proposes to use Multi-dimensional II. BACKGROUND Recurrent Neural Networks (MDRNNs) as a way to overcome one of the key problems in flexible-size board games: scalability. A. Flexible-size board games We show why this architecture is well suited to the domain There is a large variety of board games, many of which and how it can be successfully trained to play those games, even without any domain-specific knowledge. We find that either have flexible board dimensions, or have rules that can performance on small boards correlates well with performance be trivially adjusted to make them flexible. on large ones, and that this property holds for networks trained The most prominent of them is the game of Go, research by either evolution or coevolution. on which has been considering board sizes between the min- imum of 5x5 and the regular 19x19. The rules are simple[5], I. INTRODUCTION but the strategies deriving from them are highly complex. Players alternately place stones onto any of the intersections Games are a particularly interesting domain for studies of the board, with the goal of conquering maximal territory. of machine learning techniques. They form a class of clean A player can capture a single stone or a connected group and elegant environments, usually described by a small set of his opponent’s stones by completely surrounding them of formal rules and clear success criteria, and yet they often with his own stones. A move is not legal if it leads to a involve highly complex strategies.
    [Show full text]
  • A Scalable Neural Network Architecture for Board Games
    A Scalable Neural Network Architecture for Board Games Tom Schaul, Jurgen¨ Schmidhuber Abstract— This paper proposes to use Multi-dimensional II. BACKGROUND Recurrent Neural Networks (MDRNNs) as a way to overcome one of the key problems in flexible-size board games: scalability. A. Flexible-size board games We show why this architecture is well suited to the domain There is a large variety of board games, many of which and how it can be successfully trained to play those games, even without any domain-specific knowledge. We find that either have flexible board dimensions, or have rules that can performance on small boards correlates well with performance be trivially adjusted to make them flexible. on large ones, and that this property holds for networks trained The most prominent of them is the game of Go, research by either evolution or coevolution. on which has been considering board sizes between the min- imum of 5x5 and the regular 19x19. The rules are simple[4], I. INTRODUCTION but the strategies deriving from them are highly complex. Players alternately place stones onto any of the intersections Games are a particularly interesting domain for studies of of the board, with the goal of conquering maximal territory. machine learning techniques. They form a class of clean and A player can capture a single stone or a connected group elegant environments, usually described by a small set of of his opponent’s stones by completely surrounding them formal rules, have very clear success criteria, and yet they with his own stones. A move is not legal if it leads to a often involve highly complex strategies.
    [Show full text]
  • Ultimate Tic-Tac-Toe
    ULTIMATE TIC-TAC-TOE Scott Powell, Alex Merrill Professor: Professor Christman An algorithmic solver for Ultimate Tic-Tac-Toe May 2021 ABSTRACT Ultimate Tic-Tac-Toe is a deterministic game played by two players where each player’s turn has a direct effect on what options their opponent has. Each player’s viable moves are determined by their opponent on the previous turn, so players must decide whether the best move in the short term actually is the best move overall. Ultimate Tic-Tac-Toe relies entirely on strategy and decision-making. There are no random variables such as dice rolls to interfere with each player’s strategy. This is rela- tively rare in the field of board games, which often use chance to determine turns. Be- cause Ultimate Tic-Tac-Toe uses no random elements, it is a great choice for adversarial search algorithms. We may use the deterministic aspect of the game to our advantage by pruning the search trees to only contain moves that result in a good board state for the intelligent agent, and to only consider strong moves from the opponent. This speeds up the efficiency of the algorithm, allowing for an artificial intelligence capable of winning the game without spending extended periods of time evaluating each potential move. We create an intelligent agent capable of playing the game with strong moves using adversarial minimax search. We propose novel heuristics for evaluating the state of the game at any given point, and evaluate them against each other to determine the strongest heuristics. TABLE OF CONTENTS 1 Introduction1 1.1 Problem Statement............................2 1.2 Related Work...............................3 2 Methods6 2.1 Simple Heuristic: Greedy.........................6 2.2 New Heuristic...............................7 2.3 Alpha- Beta- Pruning and Depth Limit.................
    [Show full text]
  • Gomoku-Narabe" (五 目 並 べ, "Five Points in a Row"), the Game Is Quite Ancient
    Gomoku (Japanese for "five points") or, as it is sometimes called, "gomoku-narabe" (五 目 並 べ, "five points in a row"), the game is quite ancient. The birthplace of Gomoku is considered to be China, the Yellow River Delta, the exact time of birth is unknown. Scientists name different dates, the oldest of which is the 20th century BC. All this allows us to trace the history of similar games in Japan to about 100 AD. It was probably at this time that the pebbles played on the islands from the mainland. - presumably in 270 AD, with Chinese emigrants, where they became known under different names: Nanju, Itsutsu-ishi (old-time "five stones"), Goban, Goren ("five in a row") and Goseki ("five stones "). In the first book about the Japanese version of the game "five-in-a-row", published in Japan in 1858, the game is called Kakugo (Japanese for "five steps). At the turn of the 17th-18th centuries, it was already played by everyone - from old people to children. Players take turns taking turns. Black is the first to go in Renju. Each move the player places on the board, at the point of intersection of the lines, one stone of his color. The winner is the one who can be the first to build a continuous row of five stones of the same color - horizontally, vertically or diagonally. A number of fouls - illegal moves - have been determined for the player who plays with black. He cannot build "forks" 3x3 and 4x4 and a row of 6 or more stones, as well as any forks with a multiplicity of more than two.
    [Show full text]
  • Knots, Molecules, and the Universe: an Introduction to Topology
    KNOTS, MOLECULES, AND THE UNIVERSE: AN INTRODUCTION TO TOPOLOGY AMERICAN MATHEMATICAL SOCIETY https://doi.org/10.1090//mbk/096 KNOTS, MOLECULES, AND THE UNIVERSE: AN INTRODUCTION TO TOPOLOGY ERICA FLAPAN with Maia Averett David Clark Lew Ludwig Lance Bryant Vesta Coufal Cornelia Van Cott Shea Burns Elizabeth Denne Leonard Van Wyk Jason Callahan Berit Givens Robin Wilson Jorge Calvo McKenzie Lamb Helen Wong Marion Moore Campisi Emille Davie Lawrence Andrea Young AMERICAN MATHEMATICAL SOCIETY 2010 Mathematics Subject Classification. Primary 57M25, 57M15, 92C40, 92E10, 92D20, 94C15. For additional information and updates on this book, visit www.ams.org/bookpages/mbk-96 Library of Congress Cataloging-in-Publication Data Flapan, Erica, 1956– Knots, molecules, and the universe : an introduction to topology / Erica Flapan ; with Maia Averett [and seventeen others]. pages cm Includes index. ISBN 978-1-4704-2535-7 (alk. paper) 1. Topology—Textbooks. 2. Algebraic topology—Textbooks. 3. Knot theory—Textbooks. 4. Geometry—Textbooks. 5. Molecular biology—Textbooks. I. Averett, Maia. II. Title. QA611.F45 2015 514—dc23 2015031576 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy select pages for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. Republication, systematic copying, or multiple reproduction of any material in this publication is permitted only under license from the American Mathematical Society. Permissions to reuse portions of AMS publication content are handled by Copyright Clearance Center’s RightsLink service.
    [Show full text]
  • 3.4 Temporal Difference Learning 6 3.5 Ways of Learning 7 3.6 Features 8 4
    Training a Back-Propagation Network with Temporal Difference Learning and a database for the board game Pente Valentijn Muijrers 3275183 [email protected] Supervisor: Gerard Vreeswijk 7,5 ECTS 22 januari 2011 Abstract This paper will give a view on how to make a back-propagation network which can play Pente by learning from database games with temporal difference learning. The aspects of temporal difference learning and neural networks are described. The method of how a neural network can learn to play the game Pente is also explained. An experiment is described and analyzed and shows how the neural network has learned the game of Pente and in what kind of behavior the learning strategy resulted. The best performance after training on one single game was reached with a learning rate of 0.001. After learning 300000 games from the database the network did not perform very well. A third experiment with 4000 self-play games was also conducted and showed that the network placed the game more to the middle of the board than to the sides which is some form of intelligence. First this paper will give a view of the aspects of temporal difference learning in neural networks and which choices in this project were made during testing. Then the game of Pente will be explained and the way of how this game can be learned by a neural network will be justified. At the end of this paper the experiments of this project will be explained and the results will be evaluated. Then the conclusions will be discussed and analyzed.
    [Show full text]
  • Evolution Gaming Memes
    Tzolk'in Mystic Vale Kingdom Builder *Variable Setup* CodeNames (Card Building) *Variable Setup* King of Tokyo Tiananmen (Worker Placement) Dixit (Word Game) (Press Your Luck) (Area Control) Hanabi Secret Hitler *Shifting Alliances* *Cyclic vs. Radial Connection* (Win by Points) (Secret Information) (Uses Moderator) 2016 (Area Points) (Co-Operative) (Blind Moderator) (Press Your Luck) (Educational Game - History) 2012 (Perception) (Secret Information) Caylus (Connectivity Points) (Secret Information) (Secret Alliances) (Multiple Paths to Victory) (Asymmetric Player Powers) 2010 (Deduction Win Condition) (Win by Points) (Asymmetric Player Powers) 2011 (Worker Placement) Star Realms PatchWork (Pure Strategy) 2016 2013 Dvonn 2012 2016 (1D Linear Board) (Deck Building) (Cover the Board) 2014 (Shrinking Board) (Win by Points) (Card Synergy) 2014 2005 (~Hexagonal Grid) 2014 (Minimal Rules) Calculus (Neutral Pieces) (Continuous Board) Thurn and Taxis (Strategic Perception) (2D Board) (Area Control) WYPS Power Grid Prime Climb (Stackable Pieces - Top Piece Controls) (Edge to Edge Connection) Carcassonne (Catch-Up Mechanic) (Area Points) Lost Cities (Word Game) (Educational Game - Mathematics) 2001 (Minimal Rules) (Worker Placement) (Bidding) Dominion *Star (Connectivity Points) (Suits) (Hexagonal Grid) (Random Strategic Movement) 1997 (Revealed Board) (Map) *Deck Building* (2D Board) (Win by Points) (Win by Points) (Edge to Edge Connection) The Resistance / Avalon Coup (1D Linear Board) 2000 2004 (Card Synergy) (Minimalist Rules) 2006 1999 2009
    [Show full text]
  • Convolutional and Recurrent Neural Network for Gomoku
    Convolutional and Recurrent Neural Network for Gomoku Rongxiao Zhang Stanford University Stanford, CA [email protected] unoccupied intersections on the board in attempt to Abstract conquer the most territory. The player with black stones places first but need to capture a greater area than the This paper proposes to use neural networks to solve a player with white stones in order to win. A single stone or simplified version of Gomoku. More specifically, a group of connected stones can be captured when it is convolutional neural network and multi-dimensional completely surrounded by its opponents’ stones. The only recurrent network are trained separately using high-level illegal move is when it leads to previously seen position to human games. While the convolutional neural network was avoid cycling. The game ends when both players pass. able to learn specific features for Gomoku without prior Winning is determined by counting territory captured by knowledge, the human game data set proved to be each player. insufficient for a robust performance. The One interesting and important observation is Go’s multi-dimensional recurrent network also suffered from the symmetry. It has not only the straightforward axes of small database even though it was able to occasionally symmetry, but also an approximate translational occasionally expert level moves. invariance. Conducting experiments with Go can be very difficult due to the need for distinguishing dead group from alive 1. Introduction ones and handling pass moves. Many machine-learning approaches have used simplified versions of Go as a test Games are an interesting domain for artificial bed for ideas.
    [Show full text]