AI Techniques for Game Programming

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Overview


"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, path-finding, and evolving control sequences for their game agents. Coverage of neural network basics quickly advances to evolving neural motion controllers for their game agents and applying neural networks to obstacle avoidance and map exploration. Backpropagation and pattern recognition is also explained. By the end of the book, readers will 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.
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Product Details

  • ISBN-13: 9781931841085
  • Publisher: Cengage Learning
  • Publication date: 10/14/2002
  • Series: The Premier Press Game Development Series
  • Edition description: BOOK & CD
  • Pages: 480
  • Product dimensions: 7.36 (w) x 9.26 (h) x 1.01 (d)

Meet the Author


Mat Buckland studied Computer Science at London University, then spent many years as a Risk Management Consultant. He went on to work for a developer producing games fro Gremlin Software. Buckland now works as a freelance programmer and AI consultant. He has been interested in evolutionary computing and AI in general since he first read about these techniques back in the early 80¿s. He is the author of the ai-junkie.com web site (www.ai-junkie.com), which provides tutorials and advice on evolutionary algorithms.
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Table of Contents


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
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