Biologically Inspired Artificial Intelligence for Computer Games

Overview

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, ...

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Overview

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.

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Product Details

  • ISBN-13: 9781591406464
  • Publisher: IGI Global
  • Publication date: 4/28/2011
  • Pages: 280
  • Product dimensions: 7.00 (w) x 10.00 (h) x 0.69 (d)

Table of Contents


Foreword     vii
Preface     ix
Acknowledgment     xiv
Contemporary Video Game AI     1
Introduction     1
The Dawn of the Computer Video Game     1
Contemporary Video Game AI     5
Conclusion     10
References     10
An Introduction to Artificial Neural Networks     12
Introduction     12
Biological Neural Networks     13
Artificial Neural Networks     14
Neural Networks Classification     17
Learning in Artificial Neural Networks     21
References     23
Supervised Learning with Artificial Neural Networks     24
Introduction     24
The Delta Rule     24
Multipayered Perceptrons     28
The Backpropagation     28
Issues in Backpropagation     31
An Example     36
References     39
Case Study: Supervised Neural Networks in Digital Games     41
Introduction     41
Robocode     41
Conclusion     46
References     46
Unsupervised Learning in Artificial Neural Networks     48
Unsupervised Learning     48
Hebbian Learning     49
Hebbian Learning and Information Theory     51
Anti-Hebbian Learning     59
Independent Component Analysis     61
A Case Study: Independent Component Analysis     62
Competitive Learning     68
Applications     77
Case Study: The Self-Organizing Map and Pong     77
Conclusion     89
References     89
Fast Learning in Neural Networks     91
Introduction     91
Radial Basis Functions     91
Error Descent     100
Pong: A Comparative Study, MLP vs. RBF     101
Conclusion     104
Reference     104
Endnote     104
Genetic Algorithms     105
Introduction     105
Genetic Algorithms     106
A First Example     114
Case Study: GA and Backproagation ANN for Motocross Controllers     116
Futher Reading     118
Summary     119
References     120
Beyond the GA: Extensions and Alternatives     121
Introduction     121
The Iterated Prisoners' Dilemma (IPD)     130
N-Persons Iterated Prisoners' Dilemma (NIPD)     132
References     138
Evolving Solutions for Multiobjective Problems and Hierarchical AI     139
Introduction     139
Multiobjective Problems     140
Coevolution in Hierarchical AI for Strategy Games     141
Conclusion     147
References     148
Artificial Immune Systems     150
Introduction     150
The Immue System     151
Artificial Immune Systems     153
Hypermutations     156
The Immune Network     158
Agent Wars     160
Cooperating Strategies     162
Incomplete Information     164
Changing the Game     166
Duelling     167
Discussion     167
Comparision with Gas     170
Conclusion     178
References     178
Ant Colony Optimisation     180
Introduction     180
Foraging Behaviour of Ants     181
Simulating Artificial Ant Colonies with the S-ACO Algorithm     183
Improvements to the S-ACO Algorithm     188
Case Study: S-ACO and Combat     193
Summary     199
Further Reading     200
References     200
Reinforcement Learning     202
Introduction     202
The Main Elements     203
Finding the Best Policy     205
Temporal Difference Learning     211
TD([lambda] Methods     213
Continuous State Spaces     214
Immediate Rewards     218
The Bernoulli Learner     219
The Gaussian Learner     221
Application to Games     223
Comparison with Other Methods     224
Conclusion     225
Further Reading     225
References     226
Adaptivity within Games     227
Introduction     227
Existing Approaches to Adaptivity in Games     229
Adaptive Technologies     229
Developing Adaptive Solutions     234
Emergent Gameplay     236
Conclusion     236
References     237
Turing's Test and Believable AI     239
Introduction: Contemporary Game AI     239
The Popular Turing Test     240
The Real Turing Test?      242
Playing the Turing Test     243
Generalising the Turing Test     243
Measuring Believability     245
Believable Computer Players     247
Unbelievable Characters     251
The Embedded Turing Test     251
Conclusion     253
Further Reading     254
References     254
About the Contributors     256
Index     258
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