Combinatorial Game Theory: an Introduction to Tree Topplers
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Alpha-Beta Pruning
Alpha-Beta Pruning Carl Felstiner May 9, 2019 Abstract This paper serves as an introduction to the ways computers are built to play games. We implement the basic minimax algorithm and expand on it by finding ways to reduce the portion of the game tree that must be generated to find the best move. We tested our algorithms on ordinary Tic-Tac-Toe, Hex, and 3-D Tic-Tac-Toe. With our algorithms, we were able to find the best opening move in Tic-Tac-Toe by only generating 0.34% of the nodes in the game tree. We also explored some mathematical features of Hex and provided proofs of them. 1 Introduction Building computers to play board games has been a focus for mathematicians ever since computers were invented. The first computer to beat a human opponent in chess was built in 1956, and towards the late 1960s, computers were already beating chess players of low-medium skill.[1] Now, it is generally recognized that computers can beat even the most accomplished grandmaster. Computers build a tree of different possibilities for the game and then work backwards to find the move that will give the computer the best outcome. Al- though computers can evaluate board positions very quickly, in a game like chess where there are over 10120 possible board configurations it is impossible for a computer to search through the entire tree. The challenge for the computer is then to find ways to avoid searching in certain parts of the tree. Humans also look ahead a certain number of moves when playing a game, but experienced players already know of certain theories and strategies that tell them which parts of the tree to look. -
Algorithmic Combinatorial Game Theory∗
Playing Games with Algorithms: Algorithmic Combinatorial Game Theory∗ Erik D. Demaine† Robert A. Hearn‡ Abstract Combinatorial games lead to several interesting, clean problems in algorithms and complexity theory, many of which remain open. The purpose of this paper is to provide an overview of the area to encourage further research. In particular, we begin with general background in Combinatorial Game Theory, which analyzes ideal play in perfect-information games, and Constraint Logic, which provides a framework for showing hardness. Then we survey results about the complexity of determining ideal play in these games, and the related problems of solving puzzles, in terms of both polynomial-time algorithms and computational intractability results. Our review of background and survey of algorithmic results are by no means complete, but should serve as a useful primer. 1 Introduction Many classic games are known to be computationally intractable (assuming P 6= NP): one-player puzzles are often NP-complete (as in Minesweeper) or PSPACE-complete (as in Rush Hour), and two-player games are often PSPACE-complete (as in Othello) or EXPTIME-complete (as in Check- ers, Chess, and Go). Surprisingly, many seemingly simple puzzles and games are also hard. Other results are positive, proving that some games can be played optimally in polynomial time. In some cases, particularly with one-player puzzles, the computationally tractable games are still interesting for humans to play. We begin by reviewing some basics of Combinatorial Game Theory in Section 2, which gives tools for designing algorithms, followed by reviewing the relatively new theory of Constraint Logic in Section 3, which gives tools for proving hardness. -
COMPSCI 575/MATH 513 Combinatorics and Graph Theory
COMPSCI 575/MATH 513 Combinatorics and Graph Theory Lecture #34: Partisan Games (from Conway, On Numbers and Games and Berlekamp, Conway, and Guy, Winning Ways) David Mix Barrington 9 December 2016 Partisan Games • Conway's Game Theory • Hackenbush and Domineering • Four Types of Games and an Order • Some Games are Numbers • Values of Numbers • Single-Stalk Hackenbush • Some Domineering Examples Conway’s Game Theory • J. H. Conway introduced his combinatorial game theory in his 1976 book On Numbers and Games or ONAG. Researchers in the area are sometimes called onagers. • Another resource is the book Winning Ways by Berlekamp, Conway, and Guy. Conway’s Game Theory • Games, like everything else in the theory, are defined recursively. A game consists of a set of left options, each a game, and a set of right options, each a game. • The base of the recursion is the zero game, with no options for either player. • Last time we saw non-partisan games, where each player had the same options from each position. Today we look at partisan games. Hackenbush • Hackenbush is a game where the position is a diagram with red and blue edges, connected in at least one place to the “ground”. • A move is to delete an edge, a blue one for Left and a red one for Right. • Edges disconnected from the ground disappear. As usual, a player who cannot move loses. Hackenbush • From this first position, Right is going to win, because Left cannot prevent him from killing both the ground supports. It doesn’t matter who moves first. -
Blue-Red Hackenbush
Basic rules Two players: Blue and Red. Perfect information. Players move alternately. First player unable to move loses. The game must terminate. Mathematical Games – p. 1 Outcomes (assuming perfect play) Blue wins (whoever moves first): G > 0 Red wins (whoever moves first): G < 0 Mover loses: G = 0 Mover wins: G0 Mathematical Games – p. 2 Two elegant classes of games number game: always disadvantageous to move (so never G0) impartial game: same moves always available to each player Mathematical Games – p. 3 Blue-Red Hackenbush ground prototypical number game: Blue-Red Hackenbush: A player removes one edge of his or her color. Any edges not connected to the ground are also removed. First person unable to move loses. Mathematical Games – p. 4 An example Mathematical Games – p. 5 A Hackenbush sum Let G be a Blue-Red Hackenbush position (or any game). Recall: Blue wins: G > 0 Red wins: G < 0 Mover loses: G = 0 G H G + H Mathematical Games – p. 6 A Hackenbush value value (to Blue): 3 1 3 −2 −3 sum: 2 (Blue is two moves ahead), G> 0 3 2 −2 −2 −1 sum: 0 (mover loses), G= 0 Mathematical Games – p. 7 1/2 value = ? G clearly >0: Blue wins mover loses! x + x - 1 = 0, so x = 1/2 Blue is 1/2 move ahead in G. Mathematical Games – p. 8 Another position What about ? Mathematical Games – p. 9 Another position What about ? Clearly G< 0. Mathematical Games – p. 9 −13/8 8x + 13 = 0 (mover loses!) x = -13/8 Mathematical Games – p. -
Combinatorial Game Theory, Well-Tempered Scoring Games, and a Knot Game
Combinatorial Game Theory, Well-Tempered Scoring Games, and a Knot Game Will Johnson June 9, 2011 Contents 1 To Knot or Not to Knot 4 1.1 Some facts from Knot Theory . 7 1.2 Sums of Knots . 18 I Combinatorial Game Theory 23 2 Introduction 24 2.1 Combinatorial Game Theory in general . 24 2.1.1 Bibliography . 27 2.2 Additive CGT specifically . 28 2.3 Counting moves in Hackenbush . 34 3 Games 40 3.1 Nonstandard Definitions . 40 3.2 The conventional formalism . 45 3.3 Relations on Games . 54 3.4 Simplifying Games . 62 3.5 Some Examples . 66 4 Surreal Numbers 68 4.1 Surreal Numbers . 68 4.2 Short Surreal Numbers . 70 4.3 Numbers and Hackenbush . 77 4.4 The interplay of numbers and non-numbers . 79 4.5 Mean Value . 83 1 5 Games near 0 85 5.1 Infinitesimal and all-small games . 85 5.2 Nimbers and Sprague-Grundy Theory . 90 6 Norton Multiplication and Overheating 97 6.1 Even, Odd, and Well-Tempered Games . 97 6.2 Norton Multiplication . 105 6.3 Even and Odd revisited . 114 7 Bending the Rules 119 7.1 Adapting the theory . 119 7.2 Dots-and-Boxes . 121 7.3 Go . 132 7.4 Changing the theory . 139 7.5 Highlights from Winning Ways Part 2 . 144 7.5.1 Unions of partizan games . 144 7.5.2 Loopy games . 145 7.5.3 Mis`eregames . 146 7.6 Mis`ereIndistinguishability Quotients . 147 7.7 Indistinguishability in General . 148 II Well-tempered Scoring Games 155 8 Introduction 156 8.1 Boolean games . -
Combinatorial Game Theory
Combinatorial Game Theory Aaron N. Siegel Graduate Studies MR1EXLIQEXMGW Volume 146 %QIVMGER1EXLIQEXMGEP7SGMIX] Combinatorial Game Theory https://doi.org/10.1090//gsm/146 Combinatorial Game Theory Aaron N. Siegel Graduate Studies in Mathematics Volume 146 American Mathematical Society Providence, Rhode Island EDITORIAL COMMITTEE David Cox (Chair) Daniel S. Freed Rafe Mazzeo Gigliola Staffilani 2010 Mathematics Subject Classification. Primary 91A46. For additional information and updates on this book, visit www.ams.org/bookpages/gsm-146 Library of Congress Cataloging-in-Publication Data Siegel, Aaron N., 1977– Combinatorial game theory / Aaron N. Siegel. pages cm. — (Graduate studies in mathematics ; volume 146) Includes bibliographical references and index. ISBN 978-0-8218-5190-6 (alk. paper) 1. Game theory. 2. Combinatorial analysis. I. Title. QA269.S5735 2013 519.3—dc23 2012043675 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 a chapter 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. Requests for such permission should be addressed to the Acquisitions Department, American Mathematical Society, 201 Charles Street, Providence, Rhode Island 02904-2294 USA. Requests can also be made by e-mail to [email protected]. c 2013 by the American Mathematical Society. All rights reserved. The American Mathematical Society retains all rights except those granted to the United States Government. -
Game Tree Search
CSC384: Introduction to Artificial Intelligence Game Tree Search • Chapter 5.1, 5.2, 5.3, 5.6 cover some of the material we cover here. Section 5.6 has an interesting overview of State-of-the-Art game playing programs. • Section 5.5 extends the ideas to games with uncertainty (We won’t cover that material but it makes for interesting reading). Fahiem Bacchus, CSC384 Introduction to Artificial Intelligence, University of Toronto 1 Generalizing Search Problem • So far: our search problems have assumed agent has complete control of environment • State does not change unless the agent (robot) changes it. • All we need to compute is a single path to a goal state. • Assumption not always reasonable • Stochastic environment (e.g., the weather, traffic accidents). • Other agents whose interests conflict with yours • Search can find a path to a goal state, but the actions might not lead you to the goal as the state can be changed by other agents (nature or other intelligent agents) Fahiem Bacchus, CSC384 Introduction to Artificial Intelligence, University of Toronto 2 Generalizing Search Problem •We need to generalize our view of search to handle state changes that are not in the control of the agent. •One generalization yields game tree search • Agent and some other agents. • The other agents are acting to maximize their profits • this might not have a positive effect on your profits. Fahiem Bacchus, CSC384 Introduction to Artificial Intelligence, University of Toronto 3 General Games •What makes something a game? • There are two (or more) agents making changes to the world (the state) • Each agent has their own interests • e.g., each agent has a different goal; or assigns different costs to different paths/states • Each agent tries to alter the world so as to best benefit itself. -
Math Book from Wikipedia
Math book From Wikipedia PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Mon, 25 Jul 2011 10:39:12 UTC Contents Articles 0.999... 1 1 (number) 20 Portal:Mathematics 24 Signed zero 29 Integer 32 Real number 36 References Article Sources and Contributors 44 Image Sources, Licenses and Contributors 46 Article Licenses License 48 0.999... 1 0.999... In mathematics, the repeating decimal 0.999... (which may also be written as 0.9, , 0.(9), or as 0. followed by any number of 9s in the repeating decimal) denotes a real number that can be shown to be the number one. In other words, the symbols 0.999... and 1 represent the same number. Proofs of this equality have been formulated with varying degrees of mathematical rigour, taking into account preferred development of the real numbers, background assumptions, historical context, and target audience. That certain real numbers can be represented by more than one digit string is not limited to the decimal system. The same phenomenon occurs in all integer bases, and mathematicians have also quantified the ways of writing 1 in non-integer bases. Nor is this phenomenon unique to 1: every nonzero, terminating decimal has a twin with trailing 9s, such as 8.32 and 8.31999... The terminating decimal is simpler and is almost always the preferred representation, contributing to a misconception that it is the only representation. The non-terminating form is more convenient for understanding the decimal expansions of certain fractions and, in base three, for the structure of the ternary Cantor set, a simple fractal. -
Lecture Notes Part 2
Claudia Vogel Mathematics for IBA Winter Term 2009/2010 Outline 5. Game Theory • Introduction & General Techniques • Sequential Move Games • Simultaneous Move Games with Pure Strategies • Combining Sequential & Simultaneous Moves • Simultaneous Move Games with Mixed Strategies • Discussion Claudia Vogel: Game Theory and Applications 2 Motivation: Why study Game Theory? • Games are played in many situation of every days life – Roomates and Families – Professors and Students – Dating • Other fields of application – Politics, Economics, Business – Conflict Resolution – Evolutionary Biology – Sports Claudia Vogel: Game Theory and Applications 3 The beginnings of Game Theory 1944 “Theory of Games and Economic Behavior“ Oskar Morgenstern & John Neumann Claudia Vogel: Game Theory and Applications 4 Decisions vs Games • Decision: a situation in which a person chooses from different alternatives without concerning reactions from others • Game: interaction between mutually aware players – Mutual awareness: The actions of person A affect person B, B knows this and reacts or takes advance actions; A includes this into his decision process Claudia Vogel: Game Theory and Applications 5 Sequential vs Simultaneous Move Games • Sequential Move Game: player move one after the other – Example: chess • Simultaneous Move Game: Players act at the same time and without knowing, what action the other player chose – Example: race to develop a new medicine Claudia Vogel: Game Theory and Applications 6 Conflict in Players‘ Interests • Zero Sum Game: one player’s gain is the other player’s loss – Total available gain: Zero – Complete conflict of players’ interests • Constant Sum Game: The total available gain is not exactly zero, but constant. • Games in trade or other economic activities usually offer benefits for everyone and are not zero-sum. -
CONNECT6 I-Chen Wu1, Dei-Yen Huang1, and Hsiu-Chen Chang1
234 ICGA Journal December 2005 NOTES CONNECT6 1 1 1 I-Chen Wu , Dei-Yen Huang , and Hsiu-Chen Chang Hsinchu, Taiwan ABSTRACT This note introduces the game Connect6, a member of the family of the k-in-a-row games, and investigates some related issues. We analyze the fairness of Connect6 and show that Connect6 is potentially fair. Then we describe other characteristics of Connect6, e.g., the high game-tree and state-space complexities. Thereafter we present some threat-based winning strategies for Connect6 players or programs. Finally, the note describes the current developments of Connect6 and lists five new challenges. 1. INTRODUCTION Traditionally, the game k-in-a-row is defined as follows. Two players, henceforth represented as B and W, alternately place one stone, black and white respectively, on one empty square2 of an m × n board; B is assumed to play first. The player who first obtains k consecutive stones (horizontally, vertically or diagonally) of his own colour wins the game. Recently, Wu and Huang (2005) presented a new family of k-in-a-row games, Connect(m,n,k,p,q), which are analogous to the traditional k-in-a-row games, except that B places q stones initially and then both W and B alternately place p stones subsequently. The additional parameter q is a key that significantly influences the fairness. The games in the family are also referred to as Connect games. For simplicity, Connect(k,p,q) denotes the games Connect(∞,∞,k,p,q), played on infinite boards. A well-known and popular Connect game is five-in-a-row, also called Go-Moku. -
Part 4: Game Theory II Sequential Games
Part 4: Game Theory II Sequential Games Games in Extensive Form, Backward Induction, Subgame Perfect Equilibrium, Commitment June 2016 Games in Extensive Form, Backward Induction, SubgamePart 4: Perfect Game Equilibrium, Theory IISequential Commitment Games () June 2016 1 / 17 Introduction Games in Extensive Form, Backward Induction, SubgamePart 4: Perfect Game Equilibrium, Theory IISequential Commitment Games () June 2016 2 / 17 Sequential Games games in matrix (normal) form can only represent situations where people move simultaneously ! sequential nature of decision making is suppressed ! concept of ‘time’ plays no role but many situations involve player choosing actions sequentially (over time), rather than simultaneously ) need games in extensive form = sequential games example Harry Local Latte Starbucks Local Latte 1; 2 0; 0 Sally Starbucks 0; 0 2; 1 Battle of the Sexes (BS) Games in Extensive Form, Backward Induction, SubgamePart 4: Perfect Game Equilibrium, Theory IISequential Commitment Games () June 2016 3 / 17 Battle of the Sexes Reconsidered suppose Sally moves first (and leaves Harry a text-message where he can find her) Harry moves second (after reading Sally’s message) ) extensive form game (game tree): game still has two Nash equilibria: (LL,LL) and (SB,SB) but (LL,LL) is no longer plausible... Games in Extensive Form, Backward Induction, SubgamePart 4: Perfect Game Equilibrium, Theory IISequential Commitment Games () June 2016 4 / 17 Sequential Games a sequential game involves: a list of players for each player, a set -
Alpha-Beta Pruning
CMSC 474, Introduction to Game Theory Game-tree Search and Pruning Algorithms Mohammad T. Hajiaghayi University of Maryland Finite perfect-information zero-sum games Finite: finitely many agents, actions, states, histories Perfect information: Every agent knows • all of the players’ utility functions • all of the players’ actions and what they do • the history and current state No simultaneous actions – agents move one-at-a-time Constant sum (or zero-sum): Constant k such that regardless of how the game ends, • Σi=1,…,n ui = k For every such game, there’s an equivalent game in which k = 0 Examples Deterministic: chess, checkers go, gomoku reversi (othello) tic-tac-toe, qubic, connect-four mancala (awari, kalah) 9 men’s morris (merelles, morels, mill) Stochastic: backgammon, monopoly, yahtzee, parcheesi, roulette, craps For now, we’ll consider just the deterministic games Outline A brief history of work on this topic Restatement of the Minimax Theorem Game trees The minimax algorithm α-β pruning Resource limits, approximate evaluation Most of this isn’t in the game-theory book For further information, look at the following Russell & Norvig’s Artificial Intelligence: A Modern Approach • There are 3 editions of this book • In the 2nd edition, it’s Chapter 6 Brief History 1846 (Babbage) designed machine to play tic-tac-toe 1928 (von Neumann) minimax theorem 1944 (von Neumann & Morgenstern) backward induction 1950 (Shannon) minimax algorithm (finite-horizon search) 1951 (Turing) program (on paper) for playing chess 1952–7