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Score Distribution Analysis, Artificial Intelligence, and Player Modeling for Quantitative Game Design

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Score Distribution Analysis, Artificial Intelligence, and Player Modeling for Quantitative Game Design SCORE DISTRIBUTION ANALYSIS, ARTIFICIAL INTELLIGENCE, AND PLAYER MODELING FOR QUANTITATIVE GAME DESIGN DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY (Computer Science) at the NEW YORK UNIVERSITY TANDON SCHOOL OF ENGINEERING by Aaron Isaksen May 2017 SCORE DISTRIBUTION ANALYSIS, ARTIFICIAL INTELLIGENCE, AND PLAYER MODELING FOR QUANTITATIVE GAME DESIGN DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY (Computer Science) at the NEW YORK UNIVERSITY TANDON SCHOOL OF ENGINEERING by Aaron Isaksen May 2017 Approved: Department Chair Signature Date University ID: N18319753 Net ID: ai758 ii Approved by the Guidance Committee: Major: Computer Science Andy Nealen Assistant Professor of Computer Science New York University, Tandon School of Engineering Date Julian Togelius Associate Professor of Computer Science New York University, Tandon School of Engineering Date Frank Lantz Full Arts Professor and Director New York University, NYU Game Center Date Michael Mateas Professor of Computational Media and Director University of California at Santa Cruz Center for Games and Playable Media Date Leonard McMillan Associate Professor of Computer Science University of North Carolina at Chapel Hill Date iii Microfilm/Publishing Microfilm or copies of this dissertation may be obtained from: UMI Dissertation Publishing ProQuest CSA 789 E. Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106-1346 iv Vita Aaron Mark Isaksen Education Ph.D. in Computer Science Jan. 2014 - May 2017 New York University, Tandon School of Engineering, Game Innovation Lab - Pearl Brownstein Doctoral Research Award, 2017 - Outstanding Performance on Ph.D. Qualifying Exam, D. Rosenthal, MD Award, 2015 - Best paper in Artificial Intelligence and Game Technology, FDG 2015 M.S. in Electrical Engineering and Computer Science Sept. 1998 - Jan. 2001 Massachussets Institute of Technology, Laboratory for Computer Science - NSF Graduate Research Fellowship B.S. in Electrical Engineering and Computer Science Sept. 1994 - May 1998 University of California at Berkeley - Eta Kappa Nu (HKN) Honor Society Research and Funding The research in this thesis was performed at the NYU Game Innovation Lab from January 2014 through May 2017. Funding for tuition, research assistantships, and travel for the work and to present accepted conference papers was provided by Prof. Andy Nealen, Prof. Julian Togelius, the Game Innovation Lab, and via a CSE department teaching assistantship. Professional History Aaron Isaksen has worked in the game development, computer vision, and consumer electronics industry. In 2003, he founded AppAbove, Inc., maker of the first mobile phone Spanish-English phrasebook, and co-founded AppAbove Games, creator of more than 10 mobile games. Aaron’s game Chip Chain has been downloaded over 1 million times and received an Editor’s Choice award from Google. Aaron’s experience in artificial intelligence includes being lead researcher and architect of a patented computer vision based car detection system. He has also contributed his expertise as Founding Partner of indie game funding pioneer Indie Fund; Chairman of IndieBox; Advisor for the game crowd-equity company Fig and medical gaming startup Neuromotion; and NSF-SBIR Principle Investigator for Mental Canvas. v Acknowledgments First and foremost, I would like to thank my PhD advisor Prof. Andy Nealen for his support, friendship, funding, and for making this process so fruitful and enjoyable. Secondly, I would like to thank Prof. Julian Togelius, who although is not formally my co-advisor, has provided essential funding, research ideas, and mentorship. This thesis would not be possible without my co-authors on the works contained within: Prof. Andy Nealen, Prof. Julian Togelius, Prof. Steven J Brams, Prof. Adam Finkelstein, Dr. Christoffer Holmgård, Mehmet Ismail, Drew Wallace, and Dan Gopstein. Thank you Qualification Committee members Prof. Katherine Isbister, Prof. Andy Nealen, and Prof. Julian Togelius; Dissertation Commit- tee members Prof. Andy Nealen, Prof. Julian Togelius, Prof. Frank Lantz, Prof. Leonard McMillan, and Prof. Michael Mateas; the NYU Game Center; and all my fellow students at the NYU Game Innovation Lab. I would also like to thank: Prof. Frank Lantz, Scott Lee, Ahmed Khalifa, Dr. Alex Jaffe, Fernando de Mesentier Silva, Rishabh Jain, Adam Summerville, Sam Snodgrass, Matthew Guzdial, and Prof. Amy K. Hoover for co-authoring other related AI+games papers; Prof. Amy K. Hoover, Prof. Rafael Bidarra, Prof. Nathan Sturtevant, Prof. Jichen Zhu, and Prof. Julian Togelius for co-organizing the PCG2016 and WNAIG2017 work- shops; Prof. Lisa Hellerstein, Prof. Jose Ulerio, Prof. Cameron Browne, Prof. Mark J. Nelson, Prof. Mike Cook, Prof. Gillian Smith, Prof. Antonios Liapis, Prof. Adam M. Smith, Prof. Clara Fernandez, Prof. Eric Zimmerman, Prof. Bennett Foddy, Prof. Tamy Boubekeur, Prof. Daphna Harel, Dr. Alex Zook, Kaho Abe, Chris DiMauro, Chrystanyaa Brown, Dylan McKenzie, Jessica Lam, Gwynna Forgham-Thrift, Susana Garcia, and Eve Henderson for providing external guidance, support, and advice throughout the PhD process; Kyle C. Fox, Trevin Hiebert, Max McDonnell, and Dong Nguyen for help on the Flappy Bird project; Kevin Cancienne, Adam Saltsman, Prof. Frank Lantz, Zach Gage, Ron Carmel, and Steve Swink for providing critical support; and the many Anonymous reviewers for paper edits and suggestions. From my past adventures in academia, thank you Prof. Carlo Sequin, Prof. Brian Barsky, and Prof. Dan Garcia from UC Berkeley for inspiring me to do graduate work; Prof. Leonard McMillan, Prof. Steven J. Gortler, Prof. Julie Dorsey, the late Prof. Seth Teller; Dr. Bob Sumner, Dr. Rob Jagnow, and Dr. Justin Legakis for their support and inspiration during my Master’s research at MIT; and to Simon Ferrari and Prof. Andy Nealen for reminding me that I wanted to complete a PhD in games. vi Dedication This thesis is dedicated to my wife, Christina, who unconditionally supported my decision to return to graduate school to finish the PhD that I first started almost 20 years ago. Thank you Stina for your love and support – I know that this was a long process but I could not have done it without your unwavering confidence in me. I also dedicate this work to Trixie, who always welcomed me home after a long day of research and reminded me to take a break, at least to feed her or take her for a walk. Finally, I dedicate this to Rose: we had dreamed that you would be here to see me achieve my doctorate, but sadly it was not meant to be. I love all three of you. Additional Thanks Thank you to my family: Joanne, Len, Debbie, Dan, Arlen, Rick, Jon, Jan, Kat, Coco, Ghongee, aunts, uncles, cousins, nieces and nephews for their love, encouragement, and support and to my late grandparents Micael, Freda, Bill, and Vera for teaching me the values of hard work and integrity. Thank you to John Bizzarro, my business partner for many years, who helped cultivate my game design skills during the years we made games together. Thanks to Bob Caspe for his critical advice and mentorship over the years, and to Rick Kraemer for giving me my first job and helping me develop my work ethic. Thank you to the wonderful people I’ve worked with in the indie game industry, especially James Morgan III from IndieBox, my partners Kellee Santiago, Ron Carmel, Jonathan Blow, Nathan Vella, Kyle Gabler, and Matthew Wegner at Indie Fund, and all the talented game designers we worked with that taught me how to make a great game. Thank you to Jeff, Liam, David, and Dan for all the video games we played together as kids, and to some of my childhood game designer heroes: Marc Blank and Dave Lebling (Zork I, Enchanter), Steve Meretzky (Planetfall), Bryan Eggers (13 Ghosts), Kotaro Hayashida (Alex Kidd in Miracle World), Brian Fargo (Wasteland), and Richard Garriott (Ultima V). Finally – Thank you Mom and Dad for getting us our first computer in 1982 and thereby starting me off on this path, and to my brother Dan for showing me what amazing things I might be able to do with it. vii ABSTRACT SCORE DISTRIBUTION ANALYSIS, ARTIFICIAL INTELLIGENCE, AND PLAYER MODELING FOR QUANTITATIVE GAME DESIGN by Aaron Isaksen Advisor: Prof. Dr.-Ing. Andy Nealen Submitted in partial fulfillment of the Requirements for the Degree of Doctor of Philosophy (Computer Science) May 2017 We use score distribution data analysis, artificial intelligence, and player modeling to better understand games and game design using quantitative techniques, for studying the characteristics of games, and to explore the space of possible games. Score distributions model the probability a score will be achieved by a player; we model and visualize such probabilities using survival analysis, mean score, closeness, high score analysis, and other metrics. We apply artificial intelligence techniques to quantitative game design using tree search, genetic programming, optimization, procedural content generation, and Q-value modeling, focusing on general solutions. We analyze score data collected from human game play, in addition to simulated game play for scalable, repeatable, and controlled data experiments. We employ novel player modeling techniques to more accurately simulate human motor skill, timing accuracy, aiming dexterity, strategic thinking, inequity aversion, and learning effects. Quantitative analysis of simulated and real score data is used to explore various characteristics of games, including human-playable heuristics, game difficulty, score inequity, game balance, length of playtime, randomness, luck, skill, dexterity, and strategy required. We also explore game space to find new game variants using Monte Carlo simulation, computational creativity, sampling, mathematical modeling, and evolutionary search. This thesis contributes to the state of the art in several areas, including the first time that survival analysis has been applied to automated game design, the first quantitative calculation of difficulty curves, proving the probability of setting new high scores, and measuring the interaction of strategy and dexterity in games. viii Table of Contents 1 Introduction 1 1.1 Motivation and Overview .
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