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AUTOMATED ITERATIVE GAME DESIGN a Dissertation Presented AUTOMATED ITERATIVE GAME DESIGN A Dissertation Presented to The Academic Faculty By Alexander Zook In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Interactive Computing Georgia Institute of Technology December 2016 Copyright c Alexander Zook 2016 AUTOMATED ITERATIVE GAME DESIGN Approved by: Dr. Mark Riedl, Advisor School of Interactive Computing Georgia Institute of Technology Dr. Ashok Goel School of Interactive Computing Dr. Brian Magerko, Co-Advisor Georgia Institute of Technology School of Literature, Media, and Communication Dr. Michael Mateas Georgia Institute of Technology Computational Media University of California, Santa Cruz Dr. Charles Isbell School of Interactive Computing Dr. Jeff Orkin Georgia Institute of Technology Giant Otter Technologies Date Approved: October 17, 2016 A delayed game is eventually good, but a rushed game is forever bad. Shigeru Miyamoto To my family, friends, and sometime rivals. ACKNOWLEDGEMENTS Acknowledgments are always a challenge: it is impossible to recognize all the people who have directly or indirectly influenced your work. Obviously this work would not be possible without Mark’s generosity in providing me opportunities to do the work I am passionate about, patience to tolerate my tangents, and intellect to constantly challenge me to better refine the ideas that have driven my research. Boyang ‘Albert’ Li and Brian O’Neill have both been fantastic mentors throughout my early graduate career, guiding me through the bureaucracy, posing challenging thought questions, and being wonderful collaborators. And Kristin Siu and Matthew Guzdial were wonderful support in completing my work, coding up Cardonomicon, and drawing up (multiple) walls of Pokemon. Any academic career is not limited to just those at your home institution, and there are more people than I can track to thank for their wisdom and insight. Mike Cook has been a constant partner in crime, starting from our first days presenting procedural game genera- tion work with ANGELINA and GAMEFORGE at CIG in 2011. Between helping organize the Experimental AI in Games workshop, to driving me to organize the AI Game Jam, to joining me (with Gillian Smith and Tommy Thompson, too!) in the Plus Four to Science AI and Games podcast, I could not ask for better non-academic academic collaboration. Adam Smith pried me from the grasp of genetic algorithms School of Thought (sorry Julian!) to think harder about how to represent games and consider where computation can fit in the game design process as a whole. Ian Horswill served as a role model for re-evaluating how AI can work in games; his constant support helped keep EXAG going and showed me the potential for genuine change in the ways we approach research in games. Without filling pages, there is quite a list of co-conspirators I would be remiss to not thank: • Tommy Thompson and Gillian Smith for helping with Plus Four to Science and gen- eral mentorship • Eric Fruchter for showing me the potential of bullethell games for PCG and building v the platform and ideas behind automated iteration. Wish I could share where those have gone. • Stephen Lee-Urban and Michael Drinkwater for helping realize GAMETAILOR. Michael especially for the long nights on POKEPOCALYPSE (RIP). • Ken Hartsook and Sauvik Das: bringing GAMEFORGE to life not only started my academic career, but the experience of showing Grace Hopper attendees that RPGs could break story tropes opened my eyes to the range of possibilities for PCG. • Antonios Liapis for making EXAG keep happening, staying on time, and fostering such a fantastic community with Mike and I! • Stephano Allesina for my first publication and shift from neuroscience into compu- tational work (and eventually games). What an odd transition that’s been. My work has also been heavily influenced by my experiences (and ongoing support) from my partners in the games industry. Randy Ramelb has been there since the beginning, hammering out some of my bad academic habits to give me a sharper and clearer mind for how game development works. Craig Fryar set me on the road to working on ‘real’ games by pulling me in as ‘the math guy’ for my first (and second) internships—the experiences at Bioware Austin on Star Wars: The Old Republic showed me what it means to analyze games to support game development. Jeremy Ballenger ‘the Challenger’ drove my growth as a strategic thinker during my time with him at Bioware Austin and (along with Craig) has been a wonderful ongoing mentor to help me navigate my experiences in the games industry. Finishing my thesis and seeing some of my work brought to bear in live games would not be possible without the support of my Blizzard family. Chaitanya ‘Chaitown’ Chandra Chemudugunta first made the dream of working at Blizzard a reality through my summer internships, and has since given me the support and freedom to drive game design features vi and changes I never imagined possible. His support has shaped me not only as a data sci- entist, but also grown my opportunities to lead teams, build products, and turn whistful ideas about the potential of AI and machine learning in games into reality. Of course, those efforts were not alone. Yuan Cheng has somehow managed to drill the ability to stop and carefully examine an analysis into me, imparting skills in thinking critically and analyti- cally about projects. Dylan Wang set me on the path to actually working on matchmaking systems that would eventually culminate in me presenting Blizzard’s worldclass efforts to many audiences, converting a topic I had pitched as important for game AI in 2013 into reality by 2016. And Xiaoyang ‘XY’ Yang gave me the opportunity to deploy the first live machine-learning system touching millions of players I was involved with. And then there’s my fellow Blizzterns. Shawn Baskin helped do the impossible: see my work realized in the game I have spent the most hours of my life in (World of Warcraft). Not only that, but then see that work recognized as a key factor driving design and business decisions and me signing boxes of the game for the public and Blizzard employees. Mabel Lin fostered the major avenues of my matchmaking efforts in Heroes of the Storm, giving me the chance to shape the redesign of the ranked play system and building the relationships that would support public dissemination of our work. Finally, there is our department director, Ken Drake: you can stop using ‘Mr.’ Zook—somehow you got me to make my thesis deadline happen. Lastly, it goes without saying that the final push to realize my thesis would not be possible without Haram. I would likely have starved to death, not booked my defense flight (or hotel), nor stayed on task were it not for her support in the final year of writing up my thesis. And I cannot wait to add more weird citations to my CV when we start co-authoring in public policy and management. vii TABLE OF CONTENTS Acknowledgments . v List of Tables . xv List of Figures . xvii Chapter 1: Introduction . 1 1.1 Iterative Game Design . 3 1.1.1 Game Generation . 5 1.1.2 Playtesting . 7 1.1.3 Game Evaluation . 9 1.1.4 Iteration . 10 1.2 Thesis Statement . 11 Chapter 2: Background . 13 2.1 Game Generation . 13 2.1.1 General Game Playing . 14 2.1.2 Game Authoring Support . 16 2.1.3 Automated Game Generation . 18 2.2 Behavior Sampling . 21 viii 2.2.1 Exhaustive Behavior Sampling . 22 2.2.2 Planning . 23 2.2.3 Planning in Games . 25 2.2.4 Simulation-based Behavior Sampling . 25 2.2.5 MCTS . 28 2.3 Analytics . 29 2.4 Iteration . 30 2.5 Creativity . 34 2.5.1 Creativity Research . 34 2.5.2 Design Research . 36 2.5.3 Computational Creativity . 37 Chapter 3: Game Generation . 41 3.1 Introduction . 41 3.2 Game Representation . 44 3.2.1 State Model . 45 3.2.2 Mechanic Model . 48 3.3 Game Generation . 51 3.3.1 Design Requirements . 52 3.3.2 Playability Requirements . 54 3.3.3 Implementation . 55 3.4 Examples . 56 3.4.1 Role-Playing Game . 57 ix 3.4.2 Platformer . 58 3.4.3 Combined Game . 61 3.5 Extending AI Design . 61 3.5.1 Mechanic Adaptation . 62 3.5.2 Cost-Benefit Balancing . 64 3.5.3 Multi-agent Games . 66 3.5.4 Multi-instance Progressions . 67 3.5.5 Control Generation . 68 3.6 Playable Game . 69 3.7 Limitations and Future Work . 72 3.7.1 Generating State Models . 72 3.7.2 Generating Design Goals . 73 3.7.3 Linking Semantics to Mechanics . 74 3.7.4 Adversarial Games . 78 3.7.5 Other Game Domains . 79 3.8 Potential Impact . 79 3.8.1 Game Designers . 79 3.8.2 Players . 80 3.9 Summary . 81 Chapter 4: Action Sampling . 83 4.1 Introduction . 83 4.2 Behavior Sampling . 84 x 4.3 MCTS Background . 89 4.4 Skill-based Design Metrics . 91 4.4.1 Summaries . 92 4.4.2 Atoms . 93 4.4.3 Chains . 94 4.4.4 Action Spaces . 95 4.5 Metric Application Case Studies . 96 4.5.1 Agent Design . 96 4.5.2 Experiment Design . 97 4.5.3 Scrabble . 99 4.5.4 Cardonomicon . 101 4.6 Results . 102 4.6.1 Scrabble Metrics . 103 4.6.2 Cardonomicon Metrics . 106 4.7 Limitations and Future Work . 110 4.7.1 Design Space Generalization . ..
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