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Biologically Inspired Artificial Intelligence for Computer Games - Darryl Charles

Biologically Inspired Artificial Intelligence for Computer Games

Hardcover

Published: 28th April 2011
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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.

Forewordp. vii
Prefacep. ix
Acknowledgmentp. xiv
Contemporary Video Game AIp. 1
Introductionp. 1
The Dawn of the Computer Video Gamep. 1
Contemporary Video Game AIp. 5
Conclusionp. 10
Referencesp. 10
An Introduction to Artificial Neural Networksp. 12
Introductionp. 12
Biological Neural Networksp. 13
Artificial Neural Networksp. 14
Neural Networks Classificationp. 17
Learning in Artificial Neural Networksp. 21
Referencesp. 23
Supervised Learning with Artificial Neural Networksp. 24
Introductionp. 24
The Delta Rulep. 24
Multipayered Perceptronsp. 28
The Backpropagationp. 28
Issues in Backpropagationp. 31
An Examplep. 36
Referencesp. 39
Case Study: Supervised Neural Networks in Digital Gamesp. 41
Introductionp. 41
Robocodep. 41
Conclusionp. 46
Referencesp. 46
Unsupervised Learning in Artificial Neural Networksp. 48
Unsupervised Learningp. 48
Hebbian Learningp. 49
Hebbian Learning and Information Theoryp. 51
Anti-Hebbian Learningp. 59
Independent Component Analysisp. 61
A Case Study: Independent Component Analysisp. 62
Competitive Learningp. 68
Applicationsp. 77
Case Study: The Self-Organizing Map and Pongp. 77
Conclusionp. 89
Referencesp. 89
Fast Learning in Neural Networksp. 91
Introductionp. 91
Radial Basis Functionsp. 91
Error Descentp. 100
Pong: A Comparative Study, MLP vs. RBFp. 101
Conclusionp. 104
Referencep. 104
Endnotep. 104
Genetic Algorithmsp. 105
Introductionp. 105
Genetic Algorithmsp. 106
A First Examplep. 114
Case Study: GA and Backproagation ANN for Motocross Controllersp. 116
Futher Readingp. 118
Summaryp. 119
Referencesp. 120
Beyond the GA: Extensions and Alternativesp. 121
Introductionp. 121
The Iterated Prisoners' Dilemma (IPD)p. 130
N-Persons Iterated Prisoners' Dilemma (NIPD)p. 132
Referencesp. 138
Evolving Solutions for Multiobjective Problems and Hierarchical AIp. 139
Introductionp. 139
Multiobjective Problemsp. 140
Coevolution in Hierarchical AI for Strategy Gamesp. 141
Conclusionp. 147
Referencesp. 148
Artificial Immune Systemsp. 150
Introductionp. 150
The Immue Systemp. 151
Artificial Immune Systemsp. 153
Hypermutationsp. 156
The Immune Networkp. 158
Agent Warsp. 160
Cooperating Strategiesp. 162
Incomplete Informationp. 164
Changing the Gamep. 166
Duellingp. 167
Discussionp. 167
Comparision with Gasp. 170
Conclusionp. 178
Referencesp. 178
Ant Colony Optimisationp. 180
Introductionp. 180
Foraging Behaviour of Antsp. 181
Simulating Artificial Ant Colonies with the S-ACO Algorithmp. 183
Improvements to the S-ACO Algorithmp. 188
Case Study: S-ACO and Combatp. 193
Summaryp. 199
Further Readingp. 200
Referencesp. 200
Reinforcement Learningp. 202
Introductionp. 202
The Main Elementsp. 203
Finding the Best Policyp. 205
Temporal Difference Learningp. 211
TD([lambda] Methodsp. 213
Continuous State Spacesp. 214
Immediate Rewardsp. 218
The Bernoulli Learnerp. 219
The Gaussian Learnerp. 221
Application to Gamesp. 223
Comparison with Other Methodsp. 224
Conclusionp. 225
Further Readingp. 225
Referencesp. 226
Adaptivity within Gamesp. 227
Introductionp. 227
Existing Approaches to Adaptivity in Gamesp. 229
Adaptive Technologiesp. 229
Developing Adaptive Solutionsp. 234
Emergent Gameplayp. 236
Conclusionp. 236
Referencesp. 237
Turing's Test and Believable AIp. 239
Introduction: Contemporary Game AIp. 239
The Popular Turing Testp. 240
The Real Turing Test?p. 242
Playing the Turing Testp. 243
Generalising the Turing Testp. 243
Measuring Believabilityp. 245
Believable Computer Playersp. 247
Unbelievable Charactersp. 251
The Embedded Turing Testp. 251
Conclusionp. 253
Further Readingp. 254
Referencesp. 254
About the Contributorsp. 256
Indexp. 258
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781591406464
ISBN-10: 1591406463
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 301
Published: 28th April 2011
Publisher: IGI Global
Country of Publication: US
Dimensions (cm): 25.4 x 17.8  x 1.7
Weight (kg): 0.71