Part I: Background

1. Introduction

1.1 This Book
1.2 A Brief History of Artificial Intelligence and Games
1.3 Why Games for Artificial Intelligence
1.4 Why Artificial Intelligence for Games
1.5 Structure of This Book
1.6 Summary

2. AI Methods

2.1 General Notes
2.2 Ad-Hoc Behavior Authoring
2.3 Tree Search
2.4 Evolutionary Computation
2.5 Supervised Learning
2.6 Reinforcement learning
2.7 Unsupervised learning
2.8 Notable Hybrid Algorithms
2.9 Summary

Part II: Ways of Using AI in Games

3. Playing Games

3.1 Why Use AI to Play Games?
3.2 Game Design and AI Design Considerations
3.3 How Can AI Play Games?
3.4 Which Games Can AI Play?
3.5 Further Readings
3.6 Exercises
3.7 Summary

4. Generating Content

4.1 Why Generate Content?
4.2 Taxonomy
4.3 How Could We Generate Content?
4.4 Role of PCG in Games
4.5 What Could be Generated?
4.6 Evaluating Content Generators
4.7 Further Readings
4.8 Exercises
4.9 Summary

5. Modeling Players

5.1 What Player Modeling Is and What Is Not
5.2 Why Model Players?
5.3 A High-Level Taxonomy of Approaches
5.4 What is the Model’s Input Like?
5.5 What is the Model’s Output Like?
5.6 How Can we Model Players?
5.7 What Is There to Model?
5.8 Further Readings
5.9 Exercises
5.10 Summary

Part III: The Road Ahead

6. Game AI Panorama

6.1 Panoramic Views of Game AI
6.2 How Game AI Areas Inform Each Other
6.3 The Road Ahead
6.4 Summary

7. Frontier Game AI Research

7.1 General General Game AI
7.2 AI in Other Roles for Games
7.3 Ethical Considerations
7.4 Summary