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GENERAL VIDEO GAME ARTIFICIAL INTELLIGENCE MORGAN & CLAYPOOL Series ISSN: 2573-6485 ISSN: Series Synthesis LIÉBANA • ET AL ET • LIÉBANA Series ISSN: 2573-6485 Synthesis Lectures on Games and Computational Intelligence Series Editor: Daniel Ashlock, University of Guelph General Video Game Artificial Intelligence General Video Diego Pérez Liébana, Queen Mary University of London Simon M. Lucas, Queen Mary University of London GENERAL VIDEO GAME ARTIFICIAL INTELLIGENCE ARTIFICIAL VIDEO GAME GENERAL Raluca D. Gaina, Queen Mary University of London Julian Togelius, New York University Ahmed Khalifa, New York University Game Artificial Jialin Liu, Southern University of Science and Technology Research on general video game playing aims at designing agents or content generators that can perform well in multiple video games, possibly without knowing the game in advance and with little to no specific domain knowledge. The general video game AI framework and competition propose a Intelligence challenge in which researchers can test their favorite AI methods with a potentially infinite number of games created using the Video Game Description Language. The open-source framework has been used since 2014 for running a challenge. Competitors around the globe submit their best approaches that aim to generalize well across games. Additionally, the framework has been used in AI modules by many higher-education institutions as assignments, or as proposed projects for final year (undergraduate and Master’s) students and Ph.D. candidates. Diego Pérez Liébana The present book, written by the developers and organizers of the framework, presents the most interesting highlights of the research performed by the authors during these years in this domain. It Simon M. Lucas showcases work on methods to play the games, generators of content, and video game optimization. It also outlines potential further work in an area that offers multiple research directions for the future. Raluca D. Gaina ABOUT SYNTHESIS Julian Togelius This volume is a printed version of a work that appears in theSynthesis Digital Library of Engineering and Computer Science. Synthesis lectures Ahmed Khalifa provide concise original presentations of important research and development topics, published quickly in digital and print formats. For Jialin Liu more information, visit our website: http://store.morganclaypool.com CLAYPOOL & MORGAN Synthesis Lectures on Games and Computational Intelligence store.morganclaypool.com Series Editor: Daniel Ashlock, University of Guelph General Video Game Artificial Intelligence Synthesis Lectures on Games and Computational Intelligence Editor Daniel Ashlock, University of Guelph Synthesis Lectures on Games & Computational Intelligence is an innovative resource consisting of 75-150 page books on topics pertaining to digital games, including game playing and game solving algorithms; game design techniques; artificial and computational intelligence techniques for game design, play, and analysis; classical game theory in a digital environment, and automatic content generation for games. The scope includes the topics relevant to conferences like IEEE-CIG, AAAI-AIIDE, DIGRA, and FDG conferences as well as the games special sessions of the WCCI and GECCO conferences. General Video Game Artificial Intelligence Diego Pérez Liébana, Simon M. Lucas, Raluca D. Gaina, Julian Togelius, Ahmed Khalifa, and Jialin Liu 2019 On the Study of Human Cooperation via Computer Simulation: Why Existing Computer Models Fail to Tell Us Much of Anything Garrison W. Greenwood 2019 Exploring Representation in Evolutionary Level Design Daniel Ashlock 2018 On the Design of Game-Playing Agents Eun-Youn Kim and Daniel Ashlock 2017 Game Theory: A Classical Introduction, Mathematical Games, and the Tournament Andrew McEachern 2017 Copyright © 2020 by Morgan & Claypool All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. General Video Game Artificial Intelligence Diego Pérez Liébana, Simon M. Lucas, Raluca D. Gaina, Julian Togelius, Ahmed Khalifa, and Jialin Liu www.morganclaypool.com ISBN: 9781681736440 paperback ISBN: 9781681736457 ebook ISBN: 9781681736464 hardcover DOI 10.2200/S00944ED1V01Y201908GCI005 A Publication in the Morgan & Claypool Publishers series SYNTHESIS LECTURES ON GAMES AND COMPUTATIONAL INTELLIGENCE Lecture #5 Series Editor: Daniel Ashlock, University of Guelph Series ISSN Print 2573-6485 Electronic 2573-6493 General Video Game Artificial Intelligence Diego Pérez Liébana, Simon M. Lucas, and Raluca D. Gaina Queen Mary University of London Julian Togelius and Ahmed Khalifa New York University Jialin Liu Southern University of Science and Technology SYNTHESIS LECTURES ON GAMES AND COMPUTATIONAL INTELLIGENCE #5 M &C Morgan & cLaypool publishers ABSTRACT Research on general video game playing aims at designing agents or content generators that can perform well in multiple video games, possibly without knowing the game in advance and with little to no specific domain knowledge. The general video game AI framework and competition propose a challenge in which researchers can test their favorite AI methods with a potentially in- finite number of games created using the Video Game Description Language. The open-source framework has been used since 2014 for running a challenge. Competitors around the globe submit their best approaches that aim to generalize well across games. Additionally, the frame- work has been used in AI modules by many higher-education institutions as assignments, or as proposed projects for final year (undergraduate and Master’s) students and Ph.D. candidates. The present book, written by the developers and organizers of the framework, presents the most interesting highlights of the research performed by the authors during these years in this domain. It showcases work on methods to play the games, generators of content, and video game optimization. It also outlines potential further work in an area that offers multiple research directions for the future. KEYWORDS computational intelligence, artificial intelligence, video games, general video game playing, GVGAI, video game description language, reinforcement learning, Monte Carlo tree search, rolling horizon evolutionary algorithms, procedural content gen- eration vii Contents Preface ........................................................... xi Acknowledgments ................................................ xiii 1 Introduction ....................................................... 1 Diego Pérez Liébana 1.1 A Historical View: From Chess to GVGAI . 3 1.2 GVGAI in Education . 7 1.3 GVGAI and the Games Industry . 9 2 VGDL and the GVGAI Framework ................................... 13 Diego Pérez Liébana 2.1 Introduction . 13 2.2 The Video Game Description Language . 14 2.2.1 Continuous Physics . 19 2.2.2 2-Player Games . 20 2.2.3 The Future of VGDL . 22 2.3 The General Video Game AI Framework . 22 2.3.1 API for 2-Player Games . 24 2.3.2 GVGAI as a Multi-Track Challenge . 25 2.4 The GVGAI Competition . 26 2.5 Exercises . 28 3 Planning in GVGAI ................................................ 31 Diego Pérez Liébana and Raluca D. Gaina 3.1 Introduction . 31 3.2 Monte Carlo Tree Search . 32 3.3 Knowledge-Based Fast Evolutionary MCTS . 36 3.3.1 Fast Evolution in MCTS . 36 3.3.2 Learning Domain Knowledge . 37 3.3.3 Experimental Work . 39 viii 3.4 Multi-Objective MCTS for GVGAI . 40 3.4.1 Multi-Objective Optimization . 41 3.4.2 Multi-Objective MCTS . 42 3.4.3 Experimental Work . 44 3.5 Rolling Horizon Evolutionary Algorithms . 49 3.5.1 Vanilla RHEA . 49 3.5.2 RHEA Enhancements . 52 3.6 Exercises . 58 4 Frontiers of GVGAI Planning ....................................... 61 Diego Pérez Liébana and Raluca D. Gaina 4.1 Introduction . 61 4.2 State of the Art in GVGAI Planning . 62 4.2.1 OLETS (adrienctx) . 62 4.2.2 ToVo2 . 62 4.2.3 YOLOBOT . 64 4.3 Current Problems in GVGAI Planning . 65 4.4 General Win Prediction in GVGAI . 67 4.4.1 Game Playing Agents and Features . 67 4.4.2 Predictive Models . 71 4.5 Exercises . 77 5 Learning in GVGAI ................................................ 79 Jialin Liu 5.1 Challenges of Learning in GVGAI . 79 5.2 Framework . 80 5.2.1 GVGAI Learning Framework . 81 5.2.2 GVGAI Learning Environment . 81 5.2.3 GVGAI Gym Environment . 83 5.2.4 Comparing to Other Learning Frameworks . 84 5.3 GVGAI Learning Competitions . 85 5.3.1 Competition Using the GVGAI Learning Environment . 85 5.3.2 Competition Using the GVGAI Gym . 88 5.4 Competition Entries . 90 5.4.1 Random Agent (Sample Agents) . 91 5.4.2 DRL Algorithms (Sample Agents) . 91 ix 5.4.3 Multi-Armed Bandit Algorithm . 91 5.4.4 Sarsa . 91 5.4.5 Q-Learning . 92 5.4.6 Tree Search Methods . 92 5.4.7 Other Learning Agents . 92 5.4.8 Discussion . 93 5.5 Summary . 94 5.6 Exercises . 94 6 Procedural Content Generation in GVGAI............................. 97 Ahmed Khalifa and Julian Togelius 6.1 Level Generation in GVGAI. 98 6.1.1 Sample Generators . 100 6.1.2 Competition and Other Generators . 104 6.1.3 Discussion . ..
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