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	112
	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	116
	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