DOCSLIB.ORG

Applying Artificial Intelligence and Machine Learning Techniques To

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Applying Artificial Intelligence and Machine Learning Techniques To Applying Artificial Intelligence and Machine Learning Techniques to Create Varying Play Style in Artificial Game Opponents Nicholas John Sephton Doctor of Engineering University of York Computer Science October 2016 Abstract Artificial Intelligence is quickly becoming an integral part of the modern world, employed in almost every modern industry we interact with. Whether it be self-drive cars, integra- tion with our web clients or the creation of actual intelligent companions such as Xiaoice1, artificial intelligence is now an integrated and critical part of our daily existence. The application of artificial intelligence to games has been explored for several decades, with many agents now competing at a high level in strategic games which prove challenging for human players (e.g. Go [127] and Chess [73]). With artificial intelligence now able to produce strong opponents for many games, we are more concerned with the style of play of artificial agents, rather than simply their strength. Our work here focusses on the modification of artificial game opponents to create varied playstyle in complex games. We explore several techniques of modifying Monte Carlo Tree Search in an attempt to create different styles of play, thus changing the experience for human opponents playing against them. We also explore improving artificial agent strength, both by investigating parallelization of MCTS and by using Association Rule Mining to predict opponent’s choices, thus improving our ability to play well against them. 1http://www.msxiaoice.com/ 2 Contents Abstract 2 List of Contents 3 List of Figures 9 Acknowledgements 13 Author’s Declaration 15 1 Introduction 16 1.1 Background . 19 1.2 Research Questions . 21 1.2.1 Can modification of MCTS create artificial agents that affect playstyle? 22 1.2.2 Can we create an artificial agent which displays modified behaviour without compromising play strength? . 23 1.2.3 Can the application of game knowledge improve rule artificial intel- ligence techniques for modelling opponent knowledge? . 23 1.3 Publications . 23 1.4 Thesis Overview . 24 2 Literature Review 25 2.1 Introduction . 25 2.2 Games & Game terminology . 26 2.2.1 Game Terminology . 26 3 CONTENTS 2.2.2 Formal Game Definition . 27 2.2.3 Combinatorial Games . 27 2.2.4 Games of Imperfect Information . 28 2.2.5 Nash Equilibrium . 29 2.2.6 Pure versus Mixed Strategies . 30 2.3 Basic AI techniques & Concepts . 31 2.3.1 Classic AI Methods . 31 2.3.2 Move Pruning . 32 2.4 Monte Carlo Tree Search (MCTS) . 33 2.4.1 Multi-armed bandit . 33 2.4.2 Monte Carlo Tree Search . 35 2.4.3 Upper Confidence Bound applied to Trees (UCT) . 40 2.4.4 Flat Monte Carlo . 43 2.4.5 Counterfactual Regret (CFR) . 43 2.5 MCTS Enhancements & Variations . 44 2.5.1 Perfect Information Monte Carlo (PIMC) . 44 2.5.2 Transposition Tables . 47 2.5.3 Progressive Strategies . 48 2.5.4 Value Approximation Techniques . 49 2.5.5 Information Set MCTS . 51 2.5.6 Genetic Programming in MCTS . 51 2.5.7 Parallelization of MCTS . 52 2.6 Entertaining Play Experiences . 55 2.6.1 Making more human-like moves . 55 2.6.2 Entertaining Play . 56 2.6.3 Human-like play . 57 2.6.4 The importance of challenge to an entertaining experience . 58 2.7 Opponent Modelling . 59 2.8 Data Mining Techniques . 60 2.8.1 Data Preparation . 60 4 CONTENTS 2.8.2 Association Rule Mining . 61 2.8.3 The a priori Algorithm . 61 2.8.4 Data Mining for Games . 62 2.9 Summary . 63 3 Game Domains and Experimentation Software 64 3.1 Game Domains . 64 3.1.1 Lords of War . 64 3.1.2 Android: Netrunner . 66 3.2 Experimentation Hardware & Software . 67 3.2.1 MCTSTree . 68 3.2.2 Move & Game State interface . 68 3.2.3 Memory Management . 69 3.2.4 Engine Profiling . 69 3.3 Summary . 71 4 Parallelization of Information Set MCTS 73 4.1 Experimental Methodology . 75 4.1.1 Root Parallelization . 75 4.1.2 Tree Parallelization . 76 4.1.3 Leaf Parallelization . 77 4.1.4 Iteration Budget Experimentation . 78 4.1.5 Win Percentage Experimentation . 81 4.1.6 Experimentation Environment . 82 4.2 Results . 82 4.2.1 Iteration Budget Results . 82 4.2.2 Win Percentage Results . 85 4.3 Summary & Discussion . 87 4.3.1 Contributions . 90 5 CONTENTS 5 Modifying MCTS to Create Different Play Styles 92 5.1 Move Pruning . 93 5.2 Action Selection Mechanisms . 94 5.3 Experimental Methodology . 94 5.3.1 State Evaluation . 94 5.3.2 Single Heuristic Experimentation . 98 5.3.3 Multi-Heuristic Experimentation . 99 5.3.4 State-Extrapolation . 100 5.3.5 Action Selection Mechanism . 100 5.3.6 Complexity Measurements . 102 5.3.7 Online tuning . 104 5.4 Results . 104 5.4.1 Single Heuristic Results . 104 5.4.2 Multi-heuristic Results . 107 5.4.3 State Extrapolation . 108 5.4.4 Max Techniques . 110 5.4.5 Robust Techniques . 111 5.4.6 Further action selection mechanisms . 113 5.4.7 Varying Iterations . 114 5.4.8 Complexity Measure . 116 5.4.9 Online tuning . 118 5.5 Summary & Discussion . 119 5.5.1 Heuristic Move Pruning . 119 5.5.2 Action Selection Mechanisms . 120 5.5.3 Contributions . 121 6 Rule Association Mining for Opponent Modelling in Games 124 6.1 Experimental Methodology . 125 6.1.1 Netrunner Deck Data . 125 6.1.2 Apriori Rule Generation . 126 6 CONTENTS 6.1.3 Apriori Prediction . 127 6.2 Results . 134 6.2.1 Default Apriori (a1)......................... 135 6.2.2 Apriori with duplicates (a2)..................... 136 6.2.3 Apriori with Influence Priority (a3)................. 136 6.2.4 Apriori with Influence Filtering (a4)................. 137 6.2.5 Rule Generation including duplicate cards (a5)........... 137 6.2.6 Prioritising by rulesize (a6)...................... 137 6.2.7 Making confident predictions (a7).................. 138 6.2.8 Varied Size Observation Set . 138 6.2.9 Deck Creation . 141 6.3 Summary & Discussion . 142 6.3.1 Contributions . 142 7 Conclusions & Further Work 144 7.1 Research Contributions . 145 7.1.1 Parallelization of Monte Carlo Tree Search and Information Set Monte Carlo Tree Search . 145 7.1.2 Modification of Monte Carlo Tree Search using Heuristic Hard Prun- ing .................................. 146 7.1.3 Modification of Monte Carlo Tree Search play style using Action Selection Mechanisms . 147 7.1.4 Application of Data Mining Techniques to prediction of opponent decks in card games . 149 7.2 Summary and General Observations . 150 7.3 Future Work . 151 7.3.1 Parallelization of MCTS . 151 7.3.2 Heuristic Pruning . 152 7.3.3 Action Selection Mechanisms . 153 7.3.4 Rule Association Mining . 153 7 CONTENTS Appendices 155 A Experimental Results 156 A.1 Chapter 4 . ..
Load more

Recommended publications
  • “Algoritmos Para Um Jogador Inteligente De Poker” Autor: Vinícius
    Universidade Federal De Santa Catarina Centro Tecnológico Bacharelado em Ciências da Computação “Algoritmos para um jogador inteligente de Poker” Autor: Vinícius Sousa Fazio Florianópolis/SC, 2008 Universidade Federal De Santa Catarina Centro Tecnológico Bacharelado em Ciências da Computação “Algoritmos para um jogador inteligente de Poker” Autor: Vinícius Sousa Fazio Orientador: Mauro Roisenberg Banca: Benjamin Luiz Franklin Banca: João Rosaldo Vollertt Junior Banca: Ricardo Azambuja Silveira Florianópolis/SC, 2008 AGRADECIMENTOS Agradeço a todos que me ajudaram no desenvolvimento deste trabalho, em especial ao professor Mauro Roisenberg e aos colegas de trabalho João Vollertt e Benjamin Luiz Franklin e ao Ricardo Azambuja Silveira pela participação na banca avaliadora. Algoritmos para um jogador inteligente de Poker – Vinícius Sousa Fazio 4 RESUMO Poker é um jogo simples de cartas que envolve aposta, blefe e probabilidade de vencer. O objetivo foi inventar e procurar algoritmos para jogadores artificiais evolutivos e estáticos que jogassem bem poker. O jogador evolutivo criado utiliza aprendizado por reforço, onde o jogo é dividido em uma grande matriz de estados com dados de decisões e recompensas. O jogador toma a decisão que obteve a melhor recompensa média em cada estado. Para comparar a eficiência do jogador, várias disputas com jogadores que tomam decisões baseados em fórmulas simples foram feitas. Diversas disputas foram feitas para comparar a eficiência de cada algoritmo e os resultados estão demonstrados graficamente no trabalho. Palavras-Chave: Aprendizado por Reforço, Inteligência Artificial, Poker; Algoritmos para um jogador inteligente de Poker – Vinícius Sousa Fazio 5 SUMÁRIO 1. Introdução........................................................................................................12 2. Fundamentação Teórica..................................................................................15 2.1. Regras do Poker Texas Hold'em................................................................16 2.1.1.
    [Show full text]
  • Introduction to DEVS Modeling and Simulation with JAVA: Developing Component-Based Simulation Models
    Introduction to DEVS Modeling and Simulation with JAVA: Developing Component-Based Simulation Models Bernard P. Zeigler Arizona Center for Integrative Modeling and Simulation Electrical & Computer Enginering College of Engineering and Mines University of Arizona Tucson, Arizona, USA Hessam S. Sarjoughian Arizona Center for Integrative Modeling and Simulation Computer Science & Engineering Fulton School of Engineering Arizona State University Tempe, Arizona, USA January 2005 Copyright by the Authors Draft Version 3 List of Chapters Chapter 1..............................................................................................6 INTRODUCTION TO DEVS MODELING & SIMULATION METHODOLOGY........6 Framework for Modeling and Simulation ..............................................6 Brief Review of the DEVS Concepts ..................................................6 Basic Models .................................................................................8 Coupled Models .............................................................................9 Hierarchical Model Construction .....................................................10 The DEVS Formalism.......................................................................10 Classic DEVS System Specification .................................................10 Summary.......................................................................................12 Chapter 2............................................................................................13 WORKING WITH SIMPLE DEVS
    [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]
  • Parina Nirav Shah
    BANGALORE BEIJING CAPE TOWN CHANDIGARH DELHI GOA HYDERABAD JOHANNESBURG KOLKATA LONDON LOS ANGELES MUMBAI NEW YORK SINGAPORE TORONTO VANCOUVER September 15, 2020 To, To, The National Stock Exchange of India Limited BSE Limited Listing Department, Listing Department, Exchange Plaza, Bandra Kurla Complex Phiroze Jeejebhoy Towers, Bandra East, Dalal Street, Mumbai - 400 051 Mumbai - 400 001 Fax Nos.: 26598237/26598238 Fax Nos.: 22723121/2037/2039 Dear Sir/ Madam, Ref.: Scrip code: BSE — 532748/NSE – PFOCUS Sub.: Presentation to the Analyst / Institutional Investor on Un-audited Financial Results for the quarter ended June 30, 2020 Dear Sir / Madam, Please find enclosed the Presentation to the Analyst / Institutional Investor on Un-audited Financial Results for the quarter ended June 30, 2020. Kindly take the above on your record and acknowledge the receipt of the same. Thanking You, FOR PRIME FOCUS LIMITED PARINA SHAH COMPANY SECRETARY & COMPLIANCE OFFICER Encl.: a/a Prime Focus Limited. Corporate Identity Number (CIN): L92100MH1997PLC108981 Prime Focus House, Linking Road, Opposite Citibank, Khar (West), Mumbai 400 052, India. T: +91 22 6715 5000 www.primefocus.com Investor Presentation September 2020 Safe Harbor Certain statements in this document may be forward-looking statements. Such forward- looking statements are subject to certain risks and uncertainties like regulatory changes, local political or economic developments, and many other factors that could cause our actual results to differ materially from those contemplated by the relevant forward-looking statements. Prime Focus will not be in any way responsible for any action taken based on such statements and undertakes no obligation to publicly update these forward-looking statements to reflect subsequent events or circumstances.
    [Show full text]
  • III Abierto Internacional De Deportes Mentales 3Rd International Mind Sports Open (Online) 1St April-2Nd May 2020
    III Abierto Internacional de Deportes Mentales 3rd International Mind Sports Open (Online) 1st April-2nd May 2020 Organizers Sponsor Registrations: [email protected] Tournaments Modern Abstract Strategy 1. Abalone (http://www.abal.online/) 2. Arimaa (http://arimaa.com/arimaa/) 3. Entropy (https://www.mindoku.com/) 4. Hive (https://boardgamearena.com/) 5. Quoridor (https://boardgamearena.com/) N° of players: 32 max (first comes first served based) Time: date and time are flexible, as long as in the window determined by the organizers (see Calendar below) System: group phase (8 groups of 4 players) + Knock-out phase. Rounds: 7 (group phase (3), first round, Quarter Finals, Semifinals, Final) Time control: Real Time – Normal speed (BoardGameArena); 1’ per move (Arimaa); 10’ (Mindoku) Tie-breaks: Total points, tie-breaker match, draw Groups draw: will be determined once reached the capacity Calendar: 4 days per round. Tournament will start the 1st of April or whenever the capacity is reached and pairings are up Match format: every match consists of 5 games (one for each of those reported above). In odd numbered games (Abalone, Entropy, Quoridor) player 1 is the first player. In even numbered game (Arimaa, Hive) player 2 is the first player. The match is won by the player who scores at least 3 points. Prizes: see below Trophy Alfonso X Classical Abstract Strategy 1. Chess (https://boardgamearena.com/) 2. International Draughts (https://boardgamearena.com/) 3. Go 9x9 (https://boardgamearena.com/) 4. Stratego Duel (http://www.stratego.com/en/play/) 5. Xiangqi (https://boardgamearena.com/) N° of players: 32 max (first comes first served based) Time: date and time are flexible, as long as in the window determined by the organizers (see Calendar below) System: group phase (8 groups of 4 players) + Knock-out phase.
    [Show full text]
  • Pokerface: Emotion Based Game-Play Techniques for Computer Poker Players
    University of Kentucky UKnowledge University of Kentucky Master's Theses Graduate School 2004 POKERFACE: EMOTION BASED GAME-PLAY TECHNIQUES FOR COMPUTER POKER PLAYERS Lucas Cockerham University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Cockerham, Lucas, "POKERFACE: EMOTION BASED GAME-PLAY TECHNIQUES FOR COMPUTER POKER PLAYERS" (2004). University of Kentucky Master's Theses. 224. https://uknowledge.uky.edu/gradschool_theses/224 This Thesis is brought to you for free and open access by the Graduate School at UKnowledge. It has been accepted for inclusion in University of Kentucky Master's Theses by an authorized administrator of UKnowledge. For more information, please contact [email protected]. ABSTRACT OF THESIS POKERFACE: EMOTION BASED GAME-PLAY TECHNIQUES FOR COMPUTER POKER PLAYERS Numerous algorithms/methods exist for creating computer poker players. This thesis compares and contrasts them. A set of poker agents for the system PokerFace are then introduced. A survey of the problem of facial expression recognition is included in the hopes it may be used to build a better computer poker player. KEYWORDS: Poker, Neural Networks, Arti¯cial Intelligence, Emotion Recognition, Facial Action Coding System Lucas Cockerham 7/30/2004 POKERFACE: EMOTION BASED GAME-PLAY TECHNIQUES FOR COMPUTER POKER PLAYERS By Lucas Cockerham Dr. Judy Goldsmith Dr. Grzegorz Wasilkowski 7/30/2004 RULES FOR THE USE OF THESES Unpublished theses submitted for the Master's degree and deposited in the University of Kentucky Library are as a rule open for inspection, but are to be used only with due regard to the rights of the authors.
    [Show full text]
  • Towards General Game-Playing Robots: Models, Architecture and Game Controller
    Towards General Game-Playing Robots: Models, Architecture and Game Controller David Rajaratnam and Michael Thielscher The University of New South Wales, Sydney, NSW 2052, Australia fdaver,[email protected] Abstract. General Game Playing aims at AI systems that can understand the rules of new games and learn to play them effectively without human interven- tion. Our paper takes the first step towards general game-playing robots, which extend this capability to AI systems that play games in the real world. We develop a formal model for general games in physical environments and provide a sys- tems architecture that allows the embedding of existing general game players as the “brain” and suitable robotic systems as the “body” of a general game-playing robot. We also report on an initial robot prototype that can understand the rules of arbitrary games and learns to play them in a fixed physical game environment. 1 Introduction General game playing is the attempt to create a new generation of AI systems that can understand the rules of new games and then learn to play these games without human intervention [5]. Unlike specialised systems such as the chess program Deep Blue, a general game player cannot rely on algorithms that have been designed in advance for specific games. Rather, it requires a form of general intelligence that enables the player to autonomously adapt to new and possibly radically different problems. General game- playing systems therefore are a quintessential example of a new generation of software that end users can customise for their own specific tasks and special needs.
    [Show full text]
  • ANALYSIS and IMPLEMENTATION of the GAME ARIMAA Christ-Jan
    ANALYSIS AND IMPLEMENTATION OF THE GAME ARIMAA Christ-Jan Cox Master Thesis MICC-IKAT 06-05 THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE OF KNOWLEDGE ENGINEERING IN THE FACULTY OF GENERAL SCIENCES OF THE UNIVERSITEIT MAASTRICHT Thesis committee: prof. dr. H.J. van den Herik dr. ir. J.W.H.M. Uiterwijk dr. ir. P.H.M. Spronck dr. ir. H.H.L.M. Donkers Universiteit Maastricht Maastricht ICT Competence Centre Institute for Knowledge and Agent Technology Maastricht, The Netherlands March 2006 II Preface The title of my M.Sc. thesis is Analysis and Implementation of the Game Arimaa. The research was performed at the research institute IKAT (Institute for Knowledge and Agent Technology). The subject of research is the implementation of AI techniques for the rising game Arimaa, a two-player zero-sum board game with perfect information. Arimaa is a challenging game, too complex to be solved with present means. It was developed with the aim of creating a game in which humans can excel, but which is too difficult for computers. I wish to thank various people for helping me to bring this thesis to a good end and to support me during the actual research. First of all, I want to thank my supervisors, Prof. dr. H.J. van den Herik for reading my thesis and commenting on its readability, and my daily advisor dr. ir. J.W.H.M. Uiterwijk, without whom this thesis would not have been reached the current level and the “accompanying” depth. The other committee members are also recognised for their time and effort in reading this thesis.
    [Show full text]
  • Inventaire Des Jeux Combinatoires Abstraits
    INVENTAIRE DES JEUX COMBINATOIRES ABSTRAITS Ici vous trouverez une liste incomplète des jeux combinatoires abstraits qui sera en perpétuelle évolution. Ils sont classés par ordre alphabétique. Vous avez accès aux règles en cliquant sur leurs noms, si celles-ci sont disponibles. Elles sont parfois en anglais faute de les avoir trouvées en français. Si un jeu vous intéresse, j'ai ajouté une colonne « JOUER EN LIGNE » où le lien vous redirigera vers le site qui me semble le plus fréquenté pour y jouer contre d'autres joueurs. J'ai remarqué 7 catégories de ces jeux selon leur but du jeu respectif. Elles sont décrites ci-dessous et sont précisées pour chaque jeu. Ces catégories sont directement inspirées du livre « Le livre des jeux de pions » de Michel Boutin. Si vous avez des remarques à me faire sur des jeux que j'aurai oubliés ou une mauvaise classification de certains, contactez-moi via mon blog (http://www.papatilleul.fr/). La définition des jeux combinatoires abstraits est disponible ICI. Si certains ne répondent pas à cette définition, merci de me prévenir également. LES CATÉGORIES CAPTURE : Le but du jeu est de capturer ou de bloquer un ou plusieurs pions en particulier. Cette catégorie se caractérise souvent par une hiérarchie entre les pièces. Chacune d'elle a une force et une valeur matérielle propre, résultantes de leur capacité de déplacement. ELIMINATION : Le but est de capturer tous les pions de son adversaire ou un certain nombre. Parfois, il faut capturer ses propres pions. BLOCAGE : Il faut bloquer son adversaire. Autrement dit, si un joueur n'a plus de coup possible à son tour, il perd la partie.
    [Show full text]
  • Artificial General Intelligence in Games: Where Play Meets Design and User Experience
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by The IT University of Copenhagen's Repository ISSN 2186-7437 NII Shonan Meeting Report No.130 Artificial General Intelligence in Games: Where Play Meets Design and User Experience Ruck Thawonmas Julian Togelius Georgios N. Yannakakis March 18{21, 2019 National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan Artificial General Intelligence in Games: Where Play Meets Design and User Experience Organizers: Ruck Thawonmas (Ritsumeikan University, Japan) Julian Togelius (New York University, USA) Georgios N. Yannakakis (University of Malta, Malta) March 18{21, 2019 Plain English summary (lay summary): Arguably the grand goal of artificial intelligence (AI) research is to pro- duce machines that can solve multiple problems, not just one. Until recently, almost all research projects in the game AI field, however, have been very specific in that they focus on one particular way in which intelligence can be applied to video games. Most published work describes a particu- lar method|or a comparison of two or more methods|for performing a single task in a single game. If an AI approach is only tested on a single task for a single game, how can we argue that such a practice advances the scientific study of AI? And how can we argue that it is a useful method for a game designer or developer, who is likely working on a completely different game than the method was tested on? This Shonan meeting aims to discuss three aspects on how to generalize AI in games: how to play any games, how to model any game players, and how to generate any games, plus their potential applications.
    [Show full text]
  • Musichess. Music, Dance & Mind Sports
    Bridge (WBF/IMSA) - 1999 Olympic Comitee (ARISF) Chess (FIDE/IMSA) - 1999 ? IMSA Mind sport definition: "game of skill where the Organizations SportAccord competition is based on a particular type of ? the intellectual ability as opposed to physical exercise." (Wikipedia) World Mind Sports Games "Any of various competitive games based Events Mind Sports Olympiad on intellectual capability, such as chess or ? bridge" (Collins English Dictionary) International Open Mind Sports International organization: MusiChess Events Mind Sports Grand Prix International Mind Sports Association (IMSA): Decathlon Mind Sports Bridge, Chess, Draughts (Checkers), Go, Mahjong, Xiangqi Las Ligas de la Ñ - 2019 International events: MusiChess Mind Sports Apolo y las Musas - 2019 World Mind Sports Games (WMSG) Las Ligas de la Ñ Internacional Mind Sports Olympiad (MSO) www.musichess.com Mind Games / Brain Games Chess (FIDE) Abstract Strategy Go (IGF) Stratego (ISF) Carcassonne Ancient Abstract Strategy: Adugo, Eurogames Catan Backgammon, Birrguu Matya, Chaturanga, Monopoly Checkers, Chinese Checkers, Dou Shou Qi, Bridge (WBF) Fanorona, Janggi, Mahjong, Makruk, Card games Magic: The Gathering Mancala games, Mengamenga, Mu Torere, Poker / Match Poker Nine Men's Morris, Petteia, Puluc, Royal (IFP/IFMP) Game of Ur, Senet, Shatranj, Shogi, Tafl Dungeons & Dragons games, Xiangqi, Yote Role-playing games The Lord of the Rings Modern Abstract Strategy: Abalone, Warhammer Arimaa, Blokus, Boku, Coalition Chess, Colour Chess, Djambi (Maquiavelli's Chess), Rubik's Cube (WCA)
    [Show full text]
  • A User's Guide to the Apocalypse
    A User's Guide to the Apocalypse [a tale of survival, identity, and personal growth] [powered by Chuubo's Marvelous Wish-Granting Engine] [Elaine “OJ” Wang] This is a preview version dating from March 28, 2017. The newest version of this document is always available from http://orngjce223.net/chuubo/A%20User%27s%20Guide %20to%20the%20Apocalypse%20unfinished.pdf. Follow https://eternity-braid.tumblr.com/ for update notices and related content. Credits/Copyright: Written by: Elaine “OJ” Wang, with some additions and excerpts from: • Ops: the original version of Chat Conventions (page ???). • mellonbread: Welcome to the Future (page ???), The Life of the Mind (page ???), and Corpseparty (page ???), as well as quotes from wagglanGimmicks, publicFunctionary, corbinaOpaleye, and orangutanFingernails. • godsgifttogrinds: The Game Must Go On (page ???). • eternalfarnham: The Azurites (page ???). Editing and layout: I dream of making someone else do it. Based on Replay Value AU of Homestuck, which was contributed to by many people, the ones whom I remember best being Alana, Bobbin, Cobb, Dove, Impern, Ishtadaal, Keleviel, Mnem, Muss, Ops, Oven, Rave, The Black Watch, Viridian, Whilim, and Zuki. Any omissions here are my own damn fault. In turn, Replay Value AU itself was based upon Sburb Glitch FAQ written by godsgifttogrinds, which in turn was based upon Homestuck by Andrew Hussie. This is a supplement for the Chuubo's Marvelous Wish-Granting Engine system, which was written by Jenna Katerin Moran. The game mechanics belong to her and are used with permission. Previous versions of this content have appeared on eternity-braid.tumblr.com, rvdrabbles.tumblr.com, and archiveofourown.org.
    [Show full text]