AI Techniques for Game Programming

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Author: Mat Buckland

ISBN-10: 193184108X

ISBN-13: 9781931841085

Category: Game Programming

"AI Techniques for Game Programming" takes the difficult topics of genetic algorithms and neural networks and explains them in plain English. Gone are the tortuous mathematic equations and abstract examples to be found in other books. Each chapter takes readers through the theory a step at a time, explaining clearly how they can incorporate each technique into their own games. After a whirlwind tour of Windows programming, readers will learn how to use genetic algorithms for optimization,...

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AI Techniques for Game Programming takes the difficult topics of genetic algorithms and neural networks and explains them in plain English. Gone are the tortuous mathematic equations and abstract examples to be found in other books. Each chapter takes you through the theory a step at a time, explaining clearly how you can incorporate each technique into your own games. After a whirlwind tour of Windows programming, you will learn how to use genetic algorithms for optimization, path-finding, and evolving control sequences for your game agents. Coverage of neural network basics quickly advances to evolving neural motion controllers for your game agents and applying neural networks to obstacle avoidance and map exploration. Backpropagation and pattern recognition is also explained. By the time you re done, you ll know how to train a network to recognize mouse gestures and how to use state-of-the-art techniques for creating neural networks with dynamic topologies.

Introduction Part 1: Windows Programming 1. In the Beginning, There Was a Word, and the Word Was Windows 2. Further Adventures With Windows Programming Part 2: Genetic Algorithms 3. An Introduction to Genetic Algorithms 4. Permutation Encoding and the Traveling Salesman Problem 5. Building a Better Genetic Algorithm 6. Moon Landing Made Easy Part 3: Neural Networks 7. Neural Networks in Plain English 8. Giving Your Bot Senses 9. A Supervised Training Approach 10. Real-Time Evolution 11. Evolving Neural Topology Appendices: A: Web Resources B: Bibliography and Recommende Reading C: What's On the CD