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Online Imitation and Adaptation in Modern Computer Games

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Online Imitation and Adaptation in Modern Computer Games University of Paderborn Online Imitation and Adaptation in Modern Computer Games Steffen Priesterjahn Dissertation in Computer Science submitted to the Faculty of Electrical Engineering, Computer Science and Mathematics in partial fulfilment of the requirements for the degree of doctor rerum naturalium (Dr. rer. nat.) Paderborn, December 2007 II Steffen Priesterjahn Knowledge-Based Systems Research Group Department of Computer Science Faculty of Electrical Engineering, Computer Science and Mathematics University of Paderborn D-33095 Paderborn Office: E4.153 E-Mail: [email protected] Phone: ++49 (0)5251-60 3349 Fax: ++49 (0)5251-60 3338 Abstract Commercial computer games have significantly evolved in the recent decade. Current games feature vast and highly detailed virtual environments with realistic physics, complex and highly dynamic gameplay as well as complex multi-player game modes. Therefore, the creation of sophisticated and intelligent game agents that inhabit the game worlds and oppose or collaborate with the player has become a considerable challenge in terms of competitive performance and fast adaptation, but also concerning the believability and human-likeness of the shown behaviour. This thesis gives a general introduction into the field of artificial intelligence in com- puter games and proposes several approaches to create intelligent game characters for a three-dimensional, action-oriented computer game. The focus of these approaches is to create competitive and quickly adapting game agents that show sophisticated, human-like behaviours. To achieve this, the proposed methods are based on the imita- tion of other players and the usage of a population of several agents that collaborate to accelerate the learning process. The foundation of the presented work lies in the util- isation of powerful adaptation and learning techniques like evolutionary algorithms, reinforcement learning and cultural learning. Inhaltsangabe Computerspiele haben sich innerhalb des letzten Jahrzehnts signifikant weiter- entwickelt. Aktuelle Spiele zeigen hochdetaillierte, virtuelle Umgebungen mit realistischen Phyiskeffekten und verfugen¨ uber¨ vielfaltige¨ und hochdynamische Spielmodi. Daruberhinaus¨ ermoglichen¨ viele Spiele die Teilnahme mehrerer Spieler. Aus diesen Grunden¨ ist die Erstellung von konkurrenzfahigen¨ kunstlichen¨ Spielern, die in der Lage sind, sich in der Spielewelt autonom zu bewegen und sich schnell an die menschlichen Spieler anzupassen, eine große Herausforderung. Insbesondere muss dabei nicht nur auf die Performanz der Spieler, sondern auch auf ihre Glaubhaf- tigkeit und menschenahnliche¨ Spielweise geachtet werden. Diese Arbeit bietet eine allgemeine Einfuhrung¨ in das Thema der kunstlichen¨ Intelli- genz fur¨ Computerspiele und stellt verschiedene Verfahren zur automatischen Erstel- lung von intelligenten Spielecharakteren fur¨ ein dreidimensionales Actionspiel vor. Das Hauptaugenmerk dieser Ansatze¨ liegt dabei darin, konkurrenzfahige¨ und schnell lernende Agenten zu erstellen, die fortgeschrittene und menschliche Verhaltenswei- sen zeigen. Um dies zu erreichen, basieren die vorgeschlagenen Methoden auf der Imitation von anderen Spielern und der Ausnutzung einer Population von Agenten, die gemeinsam lernt, um die Lerngeschwindigkeit zu steigern. Das Fundament bilden dabei Adaptions- und Lerntechniken wie evolutionare¨ Algorithmen, Reinforcement Learning und kulturelles Lernen. Acknowledgements Several people have played an important role and have assisted in the creation of this thesis. First and foremost I want to thank my wife Claudia and my son Tobias for their strong support and for providing the distraction and family joy that I needed after long working days. I also which to thank my family for providing me with the support to start a scientific career. In addition, I wish to thank my colleagues at the research group Knowledge-Based Systems at the University of Paderborn. Especially I wish to thank Alexander Weimer, Andreas Goebels and Oliver Kramer, for numerous fruitful discussions and for sharing our visions, as well as my supervisor Prof. Kleine Buning,¨ for giving me a research position and for believing into my topic. I also wish to thank the students that have worked with me and have helped in the development of the used software and the execution of various experiments - especially Matthias Keller as well as Felix Schulte, Raphael Golombek, Christian Ikenmeyer, Markus Happe, Ulrich Scheller and the members of the project group “Cooperative Intelligence”. Steffen Priesterjahn Contents 1 Introduction ...................................................................... 1 Part I Artificial Intelligence & Computer Games 2 An Introduction to Game AI ........................................................ 9 2.1 Basic Terms...................................................................9 2.2 A Taxonomy of Computer Games................................................. 12 2.3 Challenges in Game AI......................................................... 16 3 Methodology ...................................................................... 21 3.1 Evolutionary Computation....................................................... 21 3.2 Imitation & Memetics.......................................................... 30 3.3 Neural Networks............................................................... 35 3.4 Reinforcement Learning......................................................... 40 3.5 Swarm Intelligence............................................................. 48 4 State of the Art .................................................................... 53 4.1 Industry...................................................................... 53 4.2 Science....................................................................... 68 Part II Working with QUAKE III & The CLIENTBOT INTERFACE 5 Working with QUAKE III ........................................................... 89 5.1 Introduction................................................................... 89 5.2 The Alternatives............................................................... 90 5.3 The “Complexity” of QUAKE III................................................. 97 5.4 The Architecture............................................................... 99 5.5 Reengineering the QUAKE III Engine............................................. 101 X Contents 6 The CLIENTBOT INTERFACE ....................................................... 103 6.1 The Architecture............................................................... 103 6.2 Design Principles.............................................................. 104 6.3 The Subinterfaces.............................................................. 106 6.4 The Shared Libraries........................................................... 112 Part III Imitation and Cooperation in QUAKE III 7 Introduction ...................................................................... 121 8 Cooperative Navigation ............................................................ 123 8.1 Basics........................................................................ 123 8.2 The Danger Adaptive Waypoint System............................................ 125 8.3 Results....................................................................... 129 8.4 Conclusion.................................................................... 132 9 Combat: A Learning Problem in QUAKE III ......................................... 133 9.1 Problem Description............................................................ 133 9.2 The Environment Model – Grids & Rules.......................................... 134 9.3 Evolutionary Learning.......................................................... 145 9.4 Reinforcement Learning......................................................... 158 10 Imitation-Based Learning .......................................................... 171 10.1 Imitation-Based Neural Networks................................................. 171 10.2 Imitation-Based Evolutionary Learning............................................ 177 11 Cooperative Imitation-Based Learning ............................................... 187 11.1 Idea & Modelling.............................................................. 188 11.2 Imitation-Based Adaptation...................................................... 191 11.3 Experimental Setup............................................................. 192 11.4 Results....................................................................... 194 11.5 Learning from Scratch.......................................................... 201 11.6 Possible Application Scenario.................................................... 203 11.7 Conclusion.................................................................... 204 Contents XI 12 Conclusion........................................................................ 207 Part IV Appendices A Overview of the mentioned Computer Games ......................................... 213 B Imitation-Based Evolution – All Results .............................................. 221 C Cooperative Imitation-Based Learning – All Results ................................... 235 References ............................................................................ 255 List of Figures ........................................................................ 271 List of Tables ......................................................................... 277 List of Algorithms ....................................................................
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