Biologically Inspired Artificial Intelligence for Computer Games 1st Edition by Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen Mcglinchey 1591406463 9781591406464

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ISBN 10: 1591406463

ISBN 13: 9781591406464

Author: Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen Mcglinchey

Computer games are often played by a human player against an artificial intelligence software entity. In order to truly respond in a human-like manner, the artificial intelligence in games must be adaptive, or respond as a human player would as he/she learns to play a game. Biologically Inspired Artificial Intelligence for Computer Games reviews several strands of modern artificial intelligence, including supervised and unsupervised artificial neural networks; evolutionary algorithms; artificial immune systems, swarms, and shows—using case studies for each to display how they may be applied to computer games. This book spans the divide which currently exists between the academic research community working with advanced artificial intelligence techniques and the games programming community which must create and release new, robust, and interesting games on strict deadlines, thereby creating an invaluable collection supporting both technological research and the gaming industry.

Biologically Inspired Artificial Intelligence for Computer Games 1st Table of contents:

Chapter I: Contemporary Video Game AI

Introduction

The Dawn of the Computer Video Game

Contemporary Video Game AI

Future of Game AI

Conclusion

References

Chapter II: An Introduction to Artificial Neural Networks

Introduction

Biological Neural Networks

Artificial Neural Networks

Neural Networks Classification

Learning in Artificial Neural Networks

References

Chapter III: Supervised Learning with Artificial Neural Networks

Introduction

The Delta Rule

Multilayered Perceptrons

Issues in Backpropagation

An Example

References

Chapter IV: Case Study Supervised Neural Networks in Digital Games

Introduction

Robocode

Conclusion

References

Chapter V: Unsupervised Learning in Artificial Neural Networks

Unsupervised Learning

Hebbian Learning

Hebbian Learning and Information Theory

Anti-Hebbian Learning

Independent Component Analysis

A Case Study: Independent Component Analysis

Competitive Learning

Applications

Case Study: The Self-Organizing Map and Pong

Conclusion

References

Chapter VI: Fast Learning in Neural Networks

Introduction

Radial Basis Functions

Error Descent

Pong: A Comparative Study, MLP vs. RBF

Conclusion

Reference

Endnote

Chapter VII: Genetic Algorithms

Introduction

Genetic Algorithms

The Schema Theorem (The Fundamental Theorem of Gas)

A First Example

Case Study: GA and Backpropagation ANN for Motocross Controllers

Further Reading

Summary

References

Chapter VIII: Beyond the GA: Extensions and Alternatives

Introduction

The Iterated Prisoners’ Dilemma (IPD)

N-Persons Iterated Prisoners’ Dilemma (NIPD)

References

Chapter IX: Evolving Solutions for Multiobjective Problems and Hierarchical AI

Introduction

Multiobjective Problems

Coevolution in Hierarchical AI for Strategy Games

Conclusion

References

Chapter X: Artificial Immune Systems

Introduction

The Immune System

Artificial Immune Systems

Hypermutations

The Immune Network

Agent Wars

Cooperating Strategies

Incomplete Information

Changing the Game

Duelling

Discussion

Comparison with Gas

Conclusion

References

Chapter XI: Ant Colony Optimisation

Introduction

Foraging Behaviour of Ants

Simulating Artificial Ant Colonies with theS-ACO Algorithm

Improvements to the S-ACO Algorithm

Case Study: S-ACO and Combat

Summary

Further Reading

References

Chapter XII: Reinforcement Learning

Introduction

The Main Elements

Finding the Best Policy

Temporal Difference Learning

TD(λ) Methods

Continuous State Spaces

Immediate Rewards

The Bernoulli Learner

The Gaussian Learner

Application to Games

Comparison with Other Methods

Conclusion

Further Reading

References

Chapter XIII: Adaptivity within Games

Introduction

Existing Approaches to Adaptivity in Games

Adaptive Technologies

Developing Adaptive Solutions

Emergent Gameplay

Conclusion

References

Chapter XIV: Turing’s Test and Believable AI

Introduction: Contemporary Game AI

The Popular Turing Test

The Real Turing Test?

Playing the Turing Test

Generalising the Turing Test

Measuring Believability

Believable Computer Players

Unbelievable Characters

The Embedded Turing Test

Conclusion

Further Reading

References

About the Contributors

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