Inhalt

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   1 Introduction
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   Part I Artificial Intelligence & Computer Games
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  2 An Introduction to Game AI
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   2.1 Basic Terms
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   2.2 A Taxonomy of Computer Games
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   2.3 Challenges in Game AI
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   2.3.1 Tactical Enemies and Partners
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   2.3.2 Support Characters
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   2.3.3 Racing Opponents
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   2.3.4 Strategic Opponents
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   2.3.5 Units
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   2.3.6 Commentators
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  3 Methodology
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   3.1 Evolutionary Computation
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   3.1.1 Genetic Algorithms
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   3.1.2 Evolutionary Programming
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   3.1.3 Evolution Strategies
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   3.1.4 Genetic Programming
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   3.1.5 Learning Classifier Systems
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   3.1.6 Lamarckian Evolution
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  3.2 Imitation & Memetics
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   3.2.1 Memetics
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   3.2.2 Imitation-Based Learning
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   3.3 Neural Networks
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   3.3.1 Backpropagation
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   3.3.2 Neuroevolution
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   3.4 Reinforcement Learning
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  3.5 Swarm Intelligence
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   3.5.1 Emergence
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   3.5.2 Artificial Swarms
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  4 State of the Art
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  4.1 Industry
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   4.1.1 An Overview of AI in Games
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   4.1.2 An in-depth Example Quake3
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   4.1.3 Artificial Stupidity
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  4.2 Science
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   4.2.1 Origins & Related Fields
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   4.2.2 Action Games
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   4.2.3 Arcade Games
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   4.2.4 Puzzle Games
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   4.2.5 Racing Games
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   4.2.6 Strategy Games
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   4.2.7 AI Games
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   4.2.8 Common Methods & Tendencies
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   Part II Working with quake3 and the clientbot interface
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  5 Working with Quake3
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   5.1 Introduction
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  5.2 The Alternatives
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   5.2.1 Quake
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   5.2.2 Unreal Tournament
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   5.2.3 FarCry
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   5.2.4 Morrowind
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   5.2.5 GameBots
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   5.2.6 QASE
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   5.2.7 Stratagus
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   5.2.8 Comparison
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   5.3 The Complexity of Quake3
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   5.4 The Architecture
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   5.5 Reengineering the Quake3 Engine
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  6 The ClientBot Interface
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   6.1 The Architecture
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   6.2 Design Principles
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  6.3 The Subinterfaces
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   6.3.1 The DLL Manager
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   6.3.2 The Console Manager
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   6.3.3 The Bot Interface
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  6.4 The Shared Libraries
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   6.4.1 The Messaging Library
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   6.4.2 The DLL Manager Library
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   6.4.3 The Functors Library
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   6.4.4 The Logging Library
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   6.4.5 The Math Library
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   Part III Imitation and Cooperation in Quake3
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   7 Introduction
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  8 Cooperative Navigation
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  8.1 Basics
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   8.1.1 The Artificial Environment
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   8.1.2 Waypoint Systems
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  8.2 The Danger Adaptive Waypoint System
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   8.2.1 Basic Idea
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   8.2.2 Global Danger Accessibility
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   8.2.3 Danger Propagation by the Agents
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  8.3 Results
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   8.3.1 Experimental Setup
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   8.3.2 Static Scenario
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   8.3.3 Dynamic Scenario
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   8.3.4 Large Map Scenario
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   8.4 Conclusion
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  9 Combat a Learning Problem in Quake3
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   9.1 Problem Description
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   9.2 The Environment Model -- Grids & Rules
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  9.3 Evolutionary Learning
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   9.3.1 Evolution Model
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   9.3.2 Experimental Setup
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   9.3.3 Results
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   9.3.4 Coevolution
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   9.3.5 Analysis of the Results
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   9.3.6 Conclusion
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  9.4 Reinforcement Learning
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   9.4.1 State & Action Model
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   9.4.2 Agent Model
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   9.4.3 Experimental Setup
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   9.4.4 Results
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   9.4.5 Conclusion
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  10 Imitation-Based Learning
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   10.1 Imitation-Based Neural Networks
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   10.1.1 Idea & Modelling
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   10.1.2 Experiments
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   10.1.3 Conclusion
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  10.2 Imitation-Based Evolutionary Learning
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   10.2.1 Creating the Rule Base
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   10.2.2 The Evolutionary Algorithm
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   10.2.3 Experimental Setup
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   10.2.4 Results
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   10.2.5 Analysis
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   10.2.6 Conclusion
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   11 Cooperative Imitation-Based Learning
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   11.1 Idea & Modelling
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   11.2 Imitation-Based Adaptation
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   11.3 Experimental Setup
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   11.4 Results
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   11.5 Learning from Scratch
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   11.6 Possible Application Scenario
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   11.7 Conclusion
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   12 Conclusion
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   Part IV Appendices
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   A Overview of the mentioned Computer Games
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   B Imitation-Based Evolution -- All Results
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   C Cooperative Imitation-Based Learning -- All Results
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   References
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   List of Figures
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   List of Tables
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   List of Algorithms
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   Index