AUTOMATED RPG MAP SYNTHESIS USING EVOLUTIONARY OPTIMIZATION ALGORITHMS ALESTER LEONGPUH

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1 AUTOMATED RPG MAP SYNTHESIS USING EVOLUTIONARY OPTIMIZATION ALGORITHMS ALESTER LEONGPUH FACULTY OF COMPUTING AND INFORMATICS UNIVERSITI MALAYSIA SABAH 2015

2 AUTOMATED RPG MAP SYNTHESIS USING EVOLUTIONARY OPTIMIZATION ALGORITHMS ALESTER LEONGPUH THESIS SUBMITTED IN PARTIAL FULFILLMENT FOR THE DEGREE OF BACHELOR OF COMPUTER SCIENCE FACULTY OF COMPUTING AND INFORMATICS UNIVERSITI MALAYSIA SABAH

3 DECLARATION I hereby declare that this thesis, submitted to Universiti Malaysia Sabah as partial fulfillment of the requirements for the degree of Bachelor of Computer Science with honours (Software Engineering), has not been submitted to any other university for any degree. I also certify that the work described herein is entirely my own, except for quotations and summaries sources of which have been duly acknowledged. This thesis may be made available within the university library and may be photocopied or loaned to other libraries for the purposes of consultation. 1 July 2015 Alester Leongpuh CERTIFIED BY ASSOCIATE PROFESSOR DR JASON TEO TZE WI SUPERVISOR ii

4 ACKNOWLEDGEMENT Foremost, I would like to express my sincere gratitude to my supervisor Associate Professor Dr. Jason Teo Tze Wi for the continuous support of my Bachelor study and research, for his patience, motivation, enthusiasm and immense knowledge. His guidance has helped me in all the time of research and writing of this thesis. I could not have imagined having a better supervisor for my bachelor study. Besides my supervisor, I would also like to thank to my examiner, Dr. Chin Kim On for his insightful comments and question regarding this study. Last but not least, I would like to thank my family for their support in term of money and mentally throughout this whole project. iii

5 ABSTRACT Role-Playing Games (RPG) genre has emerged over the last ten years to become one of the most popular and widely played genres of computer game. At the moment, RPG maps are usually hard coded and can be easily memorized over time resulting in lack of replay ability for RPG players. Moreover, such maps are very expensive and time consuming for game designers to code and develop for games companies. An automatically generated map would not only reduce the development costs for the games but at the same time allow different maps, levels or dungeons to be generated at run-time which gives the players a new playing experience each time the game is played, thereby significantly improving the RPG game experience and replay value. Procedural Content Generation (PCG) is the process of automating the construction of media types for use in game development and other creative fields which allows real-time generation of game content instead of relying on pre-generated ones. The implementation of a procedural generation method for 2D isometric maps in the FLARE game is reported in this paper. This work uses a technique known as Cellular Automata (CA) that allows random generation of cave-like dungeons to be created automatically in the FLARE game. The Depth First Search (DFS) algorithm is used to detect and navigate the playability of the automatically generated dungeon areas in the map. An evolutionary optimization algorithm in the form of a Genetic Algorithm (GA) is used to tune the parameters of the CA in order to produce and evolve the CA rules to generate playable dungeon areas in the map of the FLARE RPG game. iv

6 ABSTRAK (AUTOMATED RPG MAP SYNTHESIS USING EVOLUTIONARY OPTIMIZATION ALGORITHMS) Genre permainan Role-Playing Games (RPG) telah muncul lebih sepuluh tahun yang lalu menjadi salah satu genre permainan computer yang paling popular dan meluas dimainkan. Pada masa ini, peta RPG biasanya dihasilkan secara manual dan mudah untuk dihafal dari masa ke masa, mengakibatkan kurang nilai keupayaan ulangan untuk pemain RPG tersebut. Selain itu, peta tersebut adalah sangat mahal dan memakan masa untuk pereka permainan untuk mengkod dan membangunkan permainan untuk syarikat. Peta dijana secara automatik ini bukan sahaja akan mengurangkan kos pembangunan untuk permainan, tetapi pada masa yang sama juga membolehkan peta yang berbeza, tahap atau kurungan bawah tanah yang akan dijana semasa waktu bermain yang mana memberikan pemain pengalaman bermain yang baru setiap kali permainan ini dimainkan dan dengan itu meningkatkan pengalaman permainan RPG dan nilai ulangan. Procedural Content Generation (PCG) adalah proses mengautomasikan pembinaan jenis media untuk digunakan dalam pembangunan permainan dan bidang kreatif lain yang membolehkan generasi masa sebenar pada kandungan permainan dan bukan bergantung kepada kandungan yang telah sedia dijana. Pelaksanaan kaedah generasi prosedur untuk peta 2D isometrik dalam permainan FLARE akan dibincangkan dalam kertas kerja ini. Kerja ini menggunakan teknik yang dikenali sebagai Cellular Automata (CA) yang membolehkan generasi rawak yang menyerupai gua yang akan dibuat secara automatik dalam permainan FLARE itu. Algoritma Depth First Search (DFS) digunakan untuk mengesan dan mengemudi kawasan kurungan bawah tanah yang dijana secara automatik dalam peta. Satu pengoptimuman algoritma evolusi dalam bentuk algoritma genetik (GA) akan digunakan untuk menghasilkan dan mengembangkan peraturan dalam CA untuk menghasilkan peta yang dimainkan dalam peta permainan RPG FLARE. v

7 TABLE OF CONTENTS TITLE PAGE DECLARATION ACKNOWLEDGEMENT ABSTRACT ABSTRAK TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES LIST OF ABBREVIATIONS Page i ii iii iv v vi x xii xiii CHAPTER 1: INTRODUCTION Introduction Problem Background Problem Statement Research Question Objective Scope Organization of Report 4 CHAPTER 2: LITERATURE REVIEW Introduction History of Video Games Game Design Game Graphic Game Sound Game AI 7 vi

8 2.7 Fuzzy Logic Artificial Neural Network Evolutionary Algorithms Genetic Algorithm Evolution Strategies Evolutionary Programming Genetic Programming Cellular Automata Procedural Content Generation Parameter Driven Procedural Content Generation Chunk Based Procedural Content Generation Search Based Procedural Content Generation Automatic Content Generation in Dungeon Games Current Automatic Content Generation in Games Existing Research Critical Summary 34 a. Reason for Selecting GA Open Research Question Conclusion 36 CHAPTER 3: METHODOLOGY Introduction Process Model Planning Phase Analysis Phase Design Phase Coding Phase Testing Phase 40 vii

9 3.2.6 Integration Phase Testing Phase Deploy Phase Software and Hardware Requirements Software Requirement Hardware requirement Game Engine Algorithms Cellular Automata Depth First Search Evolutionary Algorithm Performance Metric 48 CHAPTER 4: SYSTEM ANALYSIS AND DESIGN Introduction System Design of Genetic Algorithm Genetic Algorithm Processes Genetic Algorithm Initialization Genetic Algorithm Evaluation Genetic Algorithm Parent Selection Genetic Algorithm Crossover Genetic Algorithm Mutation Genetic Algorithm Survivor Selection Classes and functions Conclusion 68 viii

10 CHAPTER 5: IMPLEMENTATION Introduction Depth First Search Cellular Automata Genetic Algorithm Experiments Setup Conclusion 78 CHAPTER 6: PRELIMINARY EXPERIMENT Introduction Testing of System Using Evolutionary Algorithm for Experiment One Testing of System Using Evolutionary Algorithm for Experiment Two Testing of System Using Evolutionary Algorithm for Experiment Three Testing of System Using Evolutionary Algorithm for Experiment Four Conclusion 109 CHAPTER 7: CONCLUSION AND FUTURE WORKS Introduction Summary of the Project Limitations Future Works Conclusion 113 REFERENCES 114 ix

11 LIST OF FIGURES Page Figure 2.1: Taxonomy of Research 5 Figure 2.2: Screenshot from MicroProse s Civilization II 17 Figure 2.3: Screenshot of Randomly Generated Dungeon from Diablo 18 Figure 3.1: Iterative Development Model 38 Figure 3.2: Example of 5x5 Matrix Map 44 Figure 3.3: Example of Cell in a 2D Matrix 44 Figure 3.4: Chromosome Representation in GA 45 Figure 3.5: Example of One Point Crossover 47 Figure 3.6: Example of Mutation 47 Figure 4.1: Example of Possible Output of Chromosome 52 Figure 4.2: Flow Chart Design of GA 54 Figure 4.3: Flow Chart Design of Create Initial Population for GA 55 Figure 4.4: Flow Chart Design of Chromosome Evaluation 56 Figure 4.5: Flow Chart Design of Parent Selection 57 Figure 4.6: Flow Chart Design of Crossover 58 Figure 4.7: Flow Chart Design of Mutation 59 Figure 4.8: Flow Chart Design of Survivor Selection 60 Figure 4.9: Code Snippet for Class cellularmap 63 Figure 4.10: Code Snippet for Class cellularmap 64 Figure 4.11: Code Snippet for Class Chromosome 65 Figure 4.12: Code Snippet for Class Population 66 Figure 4.13: Code Snippet for Class GA 67 Figure 5.1: Code Snippet of Depth First Search Function 69 Figure 5.2: Code Snippet of issafe Function 70 Figure 5.3: Code Snippet of countarea Function 70 Figure 5.4: Code Snippet for CellularMap Initialization Process 71 Figure 5.5: The Code Snippet for randomfill Function 71 Figure 5.6: Code Snippet for evolvemap Function 72 Figure 5.7: Code Snippet for placewalllogic Function 73 Figure 5.8: Code Snippet for Initialization of Population in GA 73 Figure 5.9: Code Snippet for Parent Selection Process 74 x

12 Figure 5.10: Code Snippet for Parent Selection Process 74 Figure 5.11: Code Snippet for Crossover Process 75 Figure 5.12: Code Snippet for Mutation Process, Mutation Rate of 1% 76 Figure 5.13: Code Snippet for Survival Selection 77 Figure 6.1: Screenshot of Procedurally Build Map (Static Maze like Map) 81 Figure 6.2: Screenshot of Random Map 81 Figure 6.3: Screenshot of Current System Map 82 Figure 6.4: Experiment One Graph of Rating over User 83 Figure 6.5: Average Rating for Experiment One 84 Figure 6.6: Screenshot of Poor Map Based on User Rating 87 Figure 6.7: Screenshot of Good Map Based on User Rating 88 Figure 6.8: Experiment Two Graph of Rating over User (1 st to 4 th test) 90 Figure 6.9: Experiment Two Graph of Rating over User (5 th to 8 th test) 90 Figure 6.10: Average Rating For Experiment Two 91 Figure 6.11: Total User Based on Type of Gamers for Experiment Two 91 Figure 6.12: Average Rating by User Based on Type of Gamer for Experiment Two 92 Figure 6.13: Screenshot of Poor Map Based on User Rating 94 Figure 6.14: Screenshot of Good Map Based on User Rating 95 Figure 6.15: Experiment Three Graph of Rating over User 98 Figure 6.16: Average Rating For Experiment Three 98 Figure 6.17: Total User Based on Type of Gamers 99 Figure 6.18: Average Rating by User Based on Type of Gamer for Experiment Three 99 Figure 6.19: Screenshot of Poor Map Based on User Rating 102 Figure 6.20: Screenshot of Good Map Based on User Rating 103 Figure 6.21: Experiment Four Graph of Rating Over User 106 Figure 6.22: Average Rating of Experiment Four 106 Figure 6.23: Total User Based on Type of Gamers for Experiment Four 107 Figure 6.24: Average Rating by User Based on Type of Gamer for Experiment Four 107 xi

13 LIST OF TABLES Page Table 2.1: Summary of Method Types in Genetic Algorithm 10 Table 2.2: Simple Genetic Algorithm Representation 10 Table 2.3: Evolution Strategies Representation 11 Table 2.4: Evolution Programming Representation 12 Table 2.5: Genetic Programming Representation 13 Table 2.6: Summary of Existing Research 19 Table 3.1: Summary of Software Requirements 41 Table 3.2: Summary of Hardware Requirements 41 Table 4.1: Summary of Chromosome Encoding 50 Table 4.2: : Summary of Chromosome Encoding Based on Gene Position, Value and the Traits 51 Table 5.1: Experiment Setup 77 Table 6.1: The Overview of Experiment One Setting 80 Table 6.2: Experiment One Result Summary 83 Table 6.3: The Overview of Experiment Two Setting 85 Table 6.4: Experiment Two Result Summary 89 Table 6.5: The Overview of Experiment Three Setting 93 Table 6.6: Experiment Three Neighborhood Size (Lowest) Summary 96 Table 6.7: Experiment Three Neighborhood Size (Default) Summary 97 Table 6.8: Experiment Three Neighborhood Size (Highest) Summary 97 Table 6.9: The Overview of Experiment Four Setting 101 Table 6.10: Experiment Four Wall Probability (Lowest) Summary 104 Table 6.11: Experiment Four Wall Probability (Default) Summary 105 Table 6.12: Experiment Four Wall Probability (Highest) Summary 105 Table 7.1: Status of Objective 110 xii

14 LIST OF ABBREVIATIONS 2D - 2-Dimension 3D - 3-Dimension AI - Artificial Intelligence ANN - Artificial Neural Network CA - Cellular Automata DFS - Depth First Search EA - Evolutionary Algorithm ES - Evolution Strategies EP - Evolution Programming FLARE - Free Libre Action Roleplaying Engine FPS - First Person Shooter GA - Genetic Algorithm GP - Genetic Programming MOEA - Multi Objective Evolutionary Algorithm NPC - Non Player Character PCG - Procedural Content Generation RPG - Role-Playing Game RTS - Real Time Strategy xiii

15 CHAPTER 1 INTRODUCTION 1.1 Introduction This chapter discusses about the general overview and background of this project with the title AUTOMATED RPG MAP SYNTHESIS USING EVOLUTIONARY OPTIMIZATION ALGORITHMS. This project uses C++ language to implement the map and evolving of the map by using Evolutionary Algorithm (EA). This chapter also includes problem statement, objective, scope and organization report. The main purpose of this project is to evolve the existing maps in Role-Playing Games (RPG) genre using EA to provide more gaming experience to the user. Since most of the time, RPG uses the same map over and over again which makes it boring over time. Evolving the map each time new user plays the game make it challenging and not boring to play. 1.2 Problem Background Since the emergence of RPG, different maps were made in the game as part of games experience to challenge the player. The maps used were totally hard coded and can be easily memorized if the players play the games many times. Imagine player A already play the game and memorize the maps location, he then teach his knowledge about the map to player B who is new to the game. Eventually over time, player will get tired and bored of the game and result in abandoning the game. FLARE game is a single-player 2D action RPG with fast-paced action and a dark fantasy style, like any other RPG maps, FLARE game map is hard coded. Thus, start a motivation to do research in develop and implement evolving maps in RPG 1

16 using EA. This research starts to implement evolving the dungeon maps in the game, permitting the player to explore a fresh map each time the game is played. 1.3 Problem Statement To evolve and create a new map in RPG, first we have to understand how the maps in RPG genre are built and implement into the games. Then we have to make sure the map evolved is playable to the player. Thus to evolve it into a better playable maps comes from this observation that helps in the process. By using Cellular Automata (CA) to randomly generate a cave-like dungeon in FLARE game. By using EA which is Genetic Algorithm (GA) to tune the parameter in the CA to produce and evolve into playable map in RPG game FLARE. 1.4 Research Question Can evolutionary algorithm be used to automate the synthesis of RPG map? 1.5 Objective To design framework to evolve the flare game maps generated by cellular automata using genetic algorithm To implement and deploy the designed framework above in evolving new map for flare game To analyze and test the performance of the newly evolved maps. 2

17 1.6 Scope The game engine, Flare (Free Libre Action Roleplaying Engine) is found developed in C++ which allows us to code the AI and map. It also allows testing inside the game. Cellular Automata (CA) used to allow random generation of cave-like dungeon in FLARE game. EA which is the Genetic Algorithm (GA) will be used to tune the parameter in the CA to produce and evolve into playable map in RPG game FLARE. Depth First Search (DFS) algorithm is used to detect the dungeon area in the map Evolutionary algorithm used to tune the parameter in Cellular Automata (CA) and evolve game map in this project using Genetic Algorithm (GA) Algorithm implementation to evolve game map in FLARE game engine is using C++ programming language Only dungeon map in FLARE game will be evolve with alpha_demo and fantasycore mod Test algorithm will be used in mineshaft_longsword dungeon map in FLARE game. 3

18 1.7 Organization of Report This report contains 7 chapters. Chapter 1 includes the overview of the project, problem background, problem statement, objective, scope and also the project organization. Chapter 2 discusses about the literature review done for this project, related papers that show the related automated game environment researches about this project. Chapter 3 discusses about the method that is used in this project. System development life cycle model that will be used for this project and other related methods used also included in this chapter. Chapter 4 is the system analysis and design. It includes the code snippet and screenshot of the classes and functions. It is defined in order to provide a better understanding for the project. Chapter 5 shows the implementation of the project based on the system analysis and design. It includes describing the function and algorithm used. Chapter 6 shows the results and analysis of all the experiment conducted. Chapter 7 concludes this report and states the work that has been done and future work to be done. 4

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