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Im Werk suchen
Online imitation and adaptation in modern computer games / Steffen Priesterjahn. 2008
Inhalt
1 Introduction
Part I Artificial Intelligence & Computer Games
2 An Introduction to Game AI
2.1 Basic Terms
2.2 A Taxonomy of Computer Games
2.3 Challenges in Game AI
2.3.1 Tactical Enemies and Partners
2.3.2 Support Characters
2.3.3 Racing Opponents
2.3.4 Strategic Opponents
2.3.5 Units
2.3.6 Commentators
3 Methodology
3.1 Evolutionary Computation
3.1.1 Genetic Algorithms
3.1.2 Evolutionary Programming
3.1.3 Evolution Strategies
3.1.4 Genetic Programming
3.1.5 Learning Classifier Systems
3.1.6 Lamarckian Evolution
3.2 Imitation & Memetics
3.2.1 Memetics
3.2.2 Imitation-Based Learning
3.3 Neural Networks
3.3.1 Backpropagation
3.3.2 Neuroevolution
3.4 Reinforcement Learning
3.5 Swarm Intelligence
3.5.1 Emergence
3.5.2 Artificial Swarms
4 State of the Art
4.1 Industry
4.1.1 An Overview of AI in Games
4.1.2 An in-depth Example Quake3
4.1.3 Artificial Stupidity
4.2 Science
4.2.1 Origins & Related Fields
4.2.2 Action Games
4.2.3 Arcade Games
4.2.4 Puzzle Games
4.2.5 Racing Games
4.2.6 Strategy Games
4.2.7 AI Games
4.2.8 Common Methods & Tendencies
Part II Working with quake3 and the clientbot interface
5 Working with Quake3
5.1 Introduction
5.2 The Alternatives
5.2.1 Quake
5.2.2 Unreal Tournament
5.2.3 FarCry
5.2.4 Morrowind
5.2.5 GameBots
5.2.6 QASE
5.2.7 Stratagus
5.2.8 Comparison
5.3 The Complexity of Quake3
5.4 The Architecture
5.5 Reengineering the Quake3 Engine
6 The ClientBot Interface
6.1 The Architecture
6.2 Design Principles
6.3 The Subinterfaces
6.3.1 The DLL Manager
6.3.2 The Console Manager
6.3.3 The Bot Interface
6.4 The Shared Libraries
6.4.1 The Messaging Library
6.4.2 The DLL Manager Library
6.4.3 The Functors Library
6.4.4 The Logging Library
6.4.5 The Math Library
Part III Imitation and Cooperation in Quake3
7 Introduction
8 Cooperative Navigation
8.1 Basics
8.1.1 The Artificial Environment
8.1.2 Waypoint Systems
8.2 The Danger Adaptive Waypoint System
8.2.1 Basic Idea
8.2.2 Global Danger Accessibility
8.2.3 Danger Propagation by the Agents
8.3 Results
8.3.1 Experimental Setup
8.3.2 Static Scenario
8.3.3 Dynamic Scenario
8.3.4 Large Map Scenario
8.4 Conclusion
9 Combat a Learning Problem in Quake3
9.1 Problem Description
9.2 The Environment Model -- Grids & Rules
9.3 Evolutionary Learning
9.3.1 Evolution Model
9.3.2 Experimental Setup
9.3.3 Results
9.3.4 Coevolution
9.3.5 Analysis of the Results
9.3.6 Conclusion
9.4 Reinforcement Learning
9.4.1 State & Action Model
9.4.2 Agent Model
9.4.3 Experimental Setup
9.4.4 Results
9.4.5 Conclusion
10 Imitation-Based Learning
10.1 Imitation-Based Neural Networks
10.1.1 Idea & Modelling
10.1.2 Experiments
10.1.3 Conclusion
10.2 Imitation-Based Evolutionary Learning
10.2.1 Creating the Rule Base
10.2.2 The Evolutionary Algorithm
10.2.3 Experimental Setup
10.2.4 Results
10.2.5 Analysis
10.2.6 Conclusion
11 Cooperative Imitation-Based Learning
11.1 Idea & Modelling
11.2 Imitation-Based Adaptation
11.3 Experimental Setup
11.4 Results
11.5 Learning from Scratch
11.6 Possible Application Scenario
11.7 Conclusion
12 Conclusion
Part IV Appendices
A Overview of the mentioned Computer Games
B Imitation-Based Evolution -- All Results
C Cooperative Imitation-Based Learning -- All Results
References
List of Figures
List of Tables
List of Algorithms
Index
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