Artificial Life Models in Software (eBook)

Preface 5
Contents 8
List of Contributors 10
Part I Virtual Living Worlds 12
1 Avida: Evolution Experiments with Self- Replicating Computer Programs 13
1.1 Introduction to Avida 13
1.1.1 History of Digital Life 14
1.1.2 The Scientific Motivation for Avida 16
1.2 The Avida Software 18
1.2.1 Avida Organisms 18
1.2.2 The Avida World 27
1.2.3 Test Environments 31
1.3 Using Avida 32
1.3.1 Performing Avida Experiments 32
1.3.2 Analyze Mode 35
1.4 A Summary of Avida Research 38
1.4.1 The Evolution of Complex Features 38
1.4.2 Survival of the Flattest 39
1.4.3 Evolution of Digital Ecosystems 40
1.5 Outlook 42
References 43
2 Framsticks: A Platform for Modeling, Simulating, and Evolving 3D Creatures 46
2.1 Available Software and Tools 47
2.2 Simulation 48
2.2.1 Body 49
2.2.2 Brain 50
2.2.3 Receptors and E.ectors 51
2.2.4 Environment 52
2.3 Framework and Evolution 52
2.3.1 Genetics 52
2.3.2 Scripting 56
2.3.3 Experiment De.nitions 57
2.3.4 Illustrative Example ("Standard Experiment” Definition) 59
2.4 Advanced Tools for Research and Education 60
2.4.1 Brain Analysis 61
2.4.2 Clustering of Similar Individuals 62
2.4.3 History of Evolution 63
2.4.4 Understanding Evolved Behaviors: Fuzzy Control 63
2.5 Research Experiments 65
2.5.1 Comparison of Genotype Encodings 65
2.5.2 Automatic Optimization Versus Human Design 66
2.5.3 Clustering with Similarity Measure 67
2.5.4 Other Experiments 69
2.6 Education with Entertainment 71
2.7 Summary 73
Acknowledgment 74
References 74
3 Nerve Garden: Germinating Biological Metaphors in Net- based Virtual Worlds 76
3.1 History and Background of the Project 76
3.1.1 Artificial Life Meets the World Wide Web 76
3.1.2 Background: L-Systems 77
3.2 Nerve Garden I: Inspiration, Architecture, and Experience 78
3.2.1 Inspiration 78
3.2.2 Architectural Elements 78
3.2.3 Experience: What Was Learned 81
3.3 A Next Evolutionary Step: Nerve Garden II 83
3.4 The Role of ALife in Virtual Worlds on the Internet 85
3.4.1 Multi-User Online Worlds: A Rich Space for Biological Metaphors 85
3.4.2 Using ALife to Draw Attention Span 86
3.4.3 Artifical Life Techniques Powering Better Virtual World Architectures 86
3.5 Other Examples of L-System-based Virtual World Construction and Considerations for the Future Use of L- Systems 87
References 88
Online Resources 89
4 GenePool: Exploring the Interaction Between Natural Selection and Sexual Selection 90
4.1 History 90
4.2 Background 91
4.2.1 Dawkins’ Call 91
4.2.2 Physics, in Various Forms 91
4.2.3 Sexual Selection 92
4.3 Description of the Software 92
4.3.1 Initialization 92
4.3.2 Food Bit Behavior 93
4.3.3 Swimbots 93
4.3.4 Locomotion Is Required for Mating 95
4.3.5 Special Body Parts 95
4.3.6 Swimbot Mental States 96
4.3.7 Energy Flow 96
4.3.8 Turning 97
4.3.9 Perceiving and Choosing Mates 98
4.3.10 Pseudo-FlatLand 98
4.3.11 Mating and Birth 99
4.4 Usage 99
4.4.1 Pool Menu 99
4.4.2 Love Menu 99
4.4.3 Stats Menu 99
4.4.4 Info Menu 99
4.4.5 A.ecting Views 100
4.4.6 Ways to Use GenePool 100
4.4.7 A Sample User Session 100
4.4.8 Mini-Dramas 101
4.4.9 Anthropomorphizing 101
4.5 Discoveries 101
4.5.1 Polymorphism? 101
4.5.2 Celebrating Diversity 103
4.6 Future Development 103
4.6.1 Recursive Embryology 103
4.6.2 Parental Investment and Gender 104
4.6.3 Environmental Variation 104
4.7 Similar Simulations 104
References 105
5 Sodarace: Adventures in Artificial Life 106
5.1 Introduction: The Sodarace Project 106
5.2 Scientific Background 108
5.2.1 Sodaconstructor: The Physics Engine of Sodarace 108
5.2.2 Sodarace Environmental Variables 110
5.2.3 Previous Work 110
5.3 Software for Artificial Life in Sodarace 111
5.3.1 Approaches to Optimization 111
5.3.2 Simulated Annealing and Daintywalker 112
5.3.3 Genetic Algorithms and the Amoeba 112
5.3.4 Genetic Algorithms from Scratch the Wodka Way
5.4 Usage 112
5.4.1 Interactions in Sodarace: The Evolution of the Forums 112
5.4.2 Forum Involvement in Scientific Research Projects 113
5.4.3 Community Development of Peer-to-Peer Learning Web Sites 113
5.4.4 Programming Support Web Sites 114
5.4.5 Experimental Investigation into the Physics of Sodarace 114
5.4.6 The Pandora’s Box: An Example of the Spontaneous Development of Scientific Method 115
5.4.7 Interdisciplinary Interaction: Art & Music Meet Science and Engineering in Sodarace
5.4.8 Sodarace in Schools 116
5.5 Experiments with Sodarace 119
5.6 Summary: The Future of Sodarace 119
Acknowledgments 119
References 120
Part II Collective Artificial Life 121
6 Escaping the Accidents of History: An Overview of Artificial Life Modeling with Repast 122
6.1 Introduction 122
6.1.1 Artificial Life 123
6.1.2 Agent-based Modeling for Artificial Life 124
6.1.3 Chapter Organization 125
6.2 REPAST 125
6.2.1 The Repast Development Ecosystem 126
6.3 RepastJ in the Eclipse Development Environment 131
6.3.1 Repast Concepts 133
6.3.2 Using Repast 136
6.4 Repast Artificial Life Models 137
6.4.1 Artificial Evolution and Ecosystems 137
6.4.2 Artificial Societies 139
6.4.3 Artificial Biological Systems 141
6.5 Conclusions 145
References 146
7 EINSTein: A Multiagent-based Model of Combat 149
7.1 Background 149
7.2 Land Combat as a Complex Adaptive System 151
7.3 Agent-based Modeling and Simulation 151
7.4 EINSTein 153
7.4.1 Features 154
7.4.2 Source Code 154
7.4.3 Design Philosophy 156
7.4.4 Program Flow 157
7.5 Combat Engine 157
7.5.1 Agents 157
7.5.2 Battlefield 158
7.5.3 Agent Personalities 158
7.5.4 Penalty Function 159
7.5.5 Meta-Rules 160
7.5.6 Combat 161
7.5.7 Run Modes 162
7.6 Sample Patterns and Behavior 162
7.6.1 Qualitative Classes of Behavior 164
7.6.2 Lanchesterian Combat 165
7.6.3 A Step Away from Lanchester 167
7.6.4 Swarming Forces 169
7.6.5 Non-Monotonicity 170
7.7 Genetic Algorithm Breeding 174
7.7.1 Search Space 174
7.7.2 Mission Fitness 175
7.7.3 EINSTein’s GA Recipe 176
7.7.4 Sample GA Breeding Experiment #1 177
7.7.5 Sample GA Breeding Experiment #2 180
7.8 Discussion 183
7.8.1 Other Features and Future Enhancements 185
7.8.2 Why Are Agent-based Models of Combat Useful? 186
Acknowledgments 189
References 189
8 StarLogo: A Programmable Complex Systems Modeling Environment for Students and Teachers 192
8.1 Background 192
8.2 Approaches to Modeling 194
8.3 Additional Design Criteria 195
8.4 The StarLogo Platform 196
8.4.1 Termites Example 197
8.5 StarLogo Design Through the Ages 200
8.5.1 The StarLogo Virtual Machine 200
8.5.2 The Anatomy of a Virtual Machine 201
8.5.3 The Process Scheduler and Its Processes 202
8.5.4 The StarLogo Interface 204
8.6 Learning to Model Through 206
8.7 Lessons Learned 207
8.7.1 Fire 207
8.7.2 But This One Goes to 1000 209
8.7.3 The Evolution of Rabbits and Grass 210
8.7.4 The Tides Are Turning 212
8.8 Conclusion 212
Acknowledgments 213
References 213
9 On the Evolution of Sonic Ecosystems 215
9.1 Introduction 215
9.1.1 Artificial Life Art 216
9.1.2 Related Work 217
9.2 Eden: An Artificial Life Artwork 217
9.3 Agents and Environments 219
9.3.1 The Eden World 219
9.3.2 Agent Implementation 220
9.3.3 Image 225
9.3.4 Sound 226
9.4 Interaction 228
9.4.1 The Problem of Aesthetic Evolution 229
9.4.2 Eden as a Reactive System 230
9.5 Results 231
9.6 Conclusion 232
References 233
Part III Magic of Discrete Worlds 235
10 Exploring Cellular Automata with MCell 236
10.1 What Is MCell? 236
10.1.1 Program Interface 237
10.1.2 Some History 238
10.1.3 Other Popular CA Simulators 238
10.2 Description of the Software 239
10.2.1 Areas of Application 239
10.2.2 Supported Cellular Automata Rules 240
10.2.3 Cellular Automata Patterns 250
10.2.4 Interesting Rules and Experiments 252
10.3 Program Usage 255
10.3.1 Browsing Cellular Automata Patterns 255
10.3.2 Designing Cellular Automata Patterns 256
10.3.3 Exploring Cellular Automata Rules 256
10.3.4 Program Configuration 258
10.3.5 Analyses 258
10.4 Extending MCell 261
10.4.1 Programming User Rules 261
10.4.2 Extending the MCell Interface 261
10.4.3 Going Java 262
10.5 Summary 263
References 263
11 Discrete Dynamics Lab: Tools for Investigating Cellular Automata and Discrete Dynamical Networks 265
11.1 Basins of Attraction 267
11.2 Discrete Dynamical Networks 267
11.3 Space-Time Patterns and Basins of Attraction 270
11.4 DDLab User Interface 272
11.5 Initial Choices 273
11.6 Setting the Network Size 274
11.7 The Neighborhood k or k-mix 275
11.8 Wiring 279
11.9 Rules 279
11.10 The Initial Network State, the Seed 282
11.11 Networks of Subnetworks 283
11.12 Presentation Options for Space-Time Patterns 284
11.13 Presentation Options for Attractor Basins 285
11.14 Filing 286
11.15 Mutations 287
11.16 Network Architecture 289
11.17 The Network Graph 289
11.18 Static Parameters Measures 290
11.19 Measures on Space-Time Patterns 290
11.20 Measures on Attractor Basins 294
11.21 Reverse Algorithms 295
11.22 Chain Rules and Encryption 296
11.23 Sequential Updating 296
11.24 Sculpting Attractor Basins 297
11.25 Acknowledgments 298
References 299
Part IV Artificial Life Arts 300
12 Simulated Breeding - A Framework of Breeding Artifacts on the Computer 301
12.1 Introduction 301
12.2 Basic Framework of IEC 303
12.3 SBART and SBEAT 304
12.3.1 SBART 304
12.3.2 SBEAT 306
12.4 Breeding in a Field Window 308
12.5 Multifield User Interface 308
12.5.1 Migration Among Fields 310
12.5.2 Protection of Individual 310
12.5.3 Effects of Multi.led Interface 311
12.6 Partial Breeding 312
12.7 Direct Genome Operation 314
12.8 Production Samples 316
12.9 Future Works 318
12.10 Conclusion 320
Acknowledgments 320
References 321
13 Enriching Aesthetics with Artificial Life 323
13.1 Introduction 323
13.2 Wonder and the Sublime in Art and Nature 324
13.3 Sublime Software 327
13.4 The Betrayal of Points and Lines 328
13.5 Moving Beyond Two Dimensions 329
13.6 Spaces That Build Themselves 331
13.7 Conclusion 333
References 334
A Appendix: Artificial Life Software 336
Index 341