Computers and Creativity	3
	Foreword	5
	Preface	7
	Why Does Computing Matter to Creativity?	7
	Summary of Contributions	9
	Acknowledgements	14
	Contents	15
	Contributors	17
	Part I: Art	23
	Chapter 1: The Painting Fool: Stories from Building an Automated Painter	24
	1.1 Introduction	24
	1.2 The Painting Fool in Context	27
	1.3 Guiding Principles	32
	1.3.1 Ever-Decreasing Circles	32
	1.3.2 Paradigms Lost	32
	1.3.3 The Whole Is More Than a Sum of the Parts	33
	1.3.4 Climbing the Meta-mountain	33
	1.3.5 The Creativity Tripod	34
	1.3.6 Beauty Is in the Mind of the Beholder	35
	1.3.7 Good Art Changes Your Mind	36
	1.4 Illustrative Projects	37
	1.4.1 Non-photorealistic Rendering	38
	1.4.2 Emotional Modelling	41
	1.4.3 Scene Construction	45
	1.4.4 Collage Generation	50
	1.4.5 Paint Dances	52
	1.5 Future Directions	53
	1.6 Conclusions	56
	References	57
	Chapter 2: Creative Ecosystems	60
	2.1 Creative Systems	60
	2.1.1 Spaces of Possibility	62
	2.2 Evolutionary Computing and Creativity	64
	2.3 Ecosystems	66
	2.3.1 Biological Ecosystems	67
	2.3.2 Ecosystem Models in the Creative Arts	68
	Design and Architecture.	68
	Music and Performance.	69
	Visual and Installation Art.	69
	2.4 Ecosystem Design Patterns	72
	2.4.1 Environments: Conditions and Resources	72
	2.4.2 Self-observation and Feedback	73
	2.4.3 Automation and the Creative Role of the Artist	76
	2.5 Conclusions	78
	References	79
	Chapter 3: Construction and Intuition: Creativity in Early Computer Art	82
	3.1 Introduction	82
	3.2 The First Narration: On Random Polygons	85
	3.2.1 Georg Nees	90
	3.2.2 A. Michael Noll	92
	3.2.3 Frieder Nake	92
	3.3 The Second Narration: On Three Artists	95
	3.3.1 Vera Molnar	97
	3.3.2 Charles Csuri	98
	3.3.3 Manfred Mohr	100
	3.4 The Third Narration: On Two Programs	102
	3.4.1 Harold Cohen: AARON	104
	3.4.2 Frieder Nake: Generative Aesthetics I	107
	3.5 The Fourth and Last Narration: On Creativity	111
	3.6 Conclusion	112
	References	114
	Chapter 4: Evaluation of Creative Aesthetics	116
	4.1 Introduction	117
	4.2 Background: Evaluation of Artistic Artefacts	117
	4.3 A Conversation on Evaluation	119
	4.4 Conclusion	132
	References	132
	Part II: Music	133
	Chapter 5: Musical Virtuosity and Creativity	134
	5.1 Virtuosos as Exceptional Humans	134
	5.1.1 Virtuosity in Art	134
	5.1.2 The Cognitive Science Perspective on Virtuosity	136
	5.1.3 Virtuosity as an Attraction Device	136
	5.1.4 Virtuosos as Creators	137
	5.2 The Case of Jazz	138
	5.2.1 The Rules of the Game	139
	5.2.2 Bebop Phrases	139
	5.2.3 The Melodic/Harmonic Interplay	140
	5.2.3.1 Harmonic Consistency	140
	5.2.3.2 Continuity	141
	The One-Step-Max Theorem	141
	5.2.4 Playing Outside and Side-Slipping	141
	5.2.5 Virtuosity Is to Improvisation as Running Is to Walking	143
	5.2.6 Claims	144
	5.3 Modelling Jazz Improvisation Generation	145
	5.3.1 Non-Markovian Approaches	145
	5.3.2 Markov Chain Approaches	146
	5.4 A Note-Based Jazz Generator	148
	5.4.1 Pitches for Representation, Beats for Generation	148
	5.4.2 Handling Harmony	149
	5.4.3 Chord Change Negotiation	151
	5.4.4 An Example Training Set	152
	5.5 Escaping Markovian Boredom	155
	5.5.1 Side-Slips and Formal Transforms	155
	5.5.2 The Control Issue	157
	5.5.3 Reusing Intentional Scores	159
	5.6 Virtuoso: A Virtuoso Enabling Interactive System	161
	5.7 Discussion	162
	References	163
	Chapter 6: Live Algorithms: Towards Autonomous Computer Improvisers	166
	6.1 Introduction	166
	6.2 The Field: Creative Group Improvisation	168
	6.2.1 Collective Improvisation	168
	6.2.2 The Individual (Human or Machine) in Interaction	168
	6.2.2.1 Autonomy	168
	6.2.2.2 Novelty	169
	6.2.2.3 Participation	169
	6.2.2.4 Leadership	170
	6.2.3 Relationship of the Four Attributes to Creativity	170
	6.3 Theoretical Considerations	171
	6.3.1 P, Q and f	171
	6.3.2 De?nition of a Live Algorithm	171
	6.3.3 Architecture	171
	6.3.4 The Live Algorithm from the Outside	175
	6.3.5 Arti?cial Intelligence	178
	6.4 Live Algorithms in Context	180
	6.4.1 Live Algorithm Behaviour	180
	6.4.1.1 Shadowing	180
	6.4.1.2 Mirroring	181
	6.4.1.3 Coupling	181
	6.4.1.4 Negotiation	182
	6.4.2 Agency and Live Algorithms	183
	6.4.3 Live Algorithms as Musicians	185
	6.5 Prototypes	186
	6.6 Further Considerations	189
	6.6.1 Embodiment	189
	6.6.2 Learning	190
	6.6.3 Anticipated Criticisms	190
	6.6.4 Cultural Embeddedness	191
	6.6.5 A Final Note	191
	References	192
	Chapter 7: The Extended Composer	194
	7.1 Introduction	194
	7.1.1 Thinking Through Tools	195
	7.1.2 The Computer as Meta-tool	197
	7.1.3 Digital Partners in Creative Practice	197
	7.2 Computational Aides for Algorithmic Inspiration	199
	7.2.1 Computational Strategies and Algorithmic Aides	200
	7.3 The Human-Computer Partnership: Characteristics and Categories	201
	7.3.1 Feedback	202
	7.3.2 Exploration	204
	7.3.3 Intimacy	207
	7.3.4 Interactivity	208
	7.3.5 Introspection	210
	7.3.6 Time	212
	7.3.7 Authorship	214
	The "Inhuman" Argument	214
	The "Invisible Hand" Argument	215
	The "Creative Vitalism" Argument	215
	7.3.8 Value	216
	7.4 In Summary	217
	7.4.1 Future Explorations	218
	7.4.2 Final Re?ections	219
	References	219
	Chapter 8: Between Material and Ideas: A Process-Based Spatial Model of Artistic Creativity	223
	8.1 Introduction	223
	8.1.1 Background	224
	8.1.2 Outline	226
	8.2 Tools	226
	8.3 The Model	228
	8.3.1 Material Space and Representation	229
	8.3.2 The Conceptual Representation	233
	8.3.3 Interplay Between Representations	234
	8.3.4 Example Scenarios	235
	8.3.5 Appreciation and Novelty	237
	8.3.6 The Model in Context	238
	8.3.7 Craft and Skill	240
	8.3.8 New Tools and Tool Design	241
	8.3.9 Social and Cultural Creativity	242
	8.3.10 Abstraction Levels	242
	8.4 Implications for Computational Creativity	243
	8.4.1 Implementation of the Model	244
	8.4.2 Conceptual Representations	245
	8.4.3 Re-conceptualisation	246
	8.4.4 Memory and Learning	247
	8.5 Final Remarks	248
	References	249
	Chapter 9: Computer Programming in the Creative Arts	252
	9.1 Introduction	252
	9.2 Creative Processes	255
	9.2.1 Creative Process of Bricolage	256
	9.3 Anthropomorphism and Metaphor in Programming	257
	9.4 Symbols and Space	259
	9.5 Components of Creativity	263
	9.6 Programming in Time	265
	9.6.1 Interactive Programming	266
	9.7 Conclusion	267
	References	268
	Part III: Theory	270
	Chapter 10: Computational Aesthetic Evaluation: Past and Future	271
	10.1 Introduction	271
	10.1.1 What Do We Mean by Computational Aesthetic Evaluation?	272
	10.1.2 Why Is Computational Aesthetic Evaluation so Dif?cult if not Impossible?	273
	10.2 A Brief History of Computational Aesthetic Evaluation	273
	10.2.1 Formulaic and Geometric Theories	274
	10.2.2 Design Principles	276
	10.2.3 Arti?cial Neural Networks and Connectionist Models	277
	10.2.4 Evolutionary Systems	278
	10.2.5 Interactive Evolutionary Computation	279
	10.2.6 Automated Fitness Functions Based on Performance Goals	280
	10.2.7 Evolutionary Fitness Measured as Error Relative to Exemplars	282
	10.2.8 Automated Fitness Functions Based on Complexity Measures	283
	10.2.9 Automated Fitness Functions in Evolutionary Music Systems	283
	10.2.10 Multi-objective Aesthetic Fitness Functions in Evolutionary Systems	284
	10.2.11 Biologically Inspired Extensions to Simple Evolutionary Computation	285
	10.2.11.1 Coevolution	286
	10.2.11.2 Niche Construction by Agents	287
	10.2.11.3 Agent Swarm Behaviour	288
	10.2.11.4 Curious Agents	289
	10.2.11.5 Human Aesthetics, Meta-aesthetics, and Alternatives to Fitness Functions	290
	10.2.12 Complexity Based Models of Aesthetics	290
	10.3 The Future of Computational Aesthetic Evaluation	293
	10.3.1 The Origins of Art and the Art Instinct	293
	10.3.2 Psychological Models of Human Aesthetics	295
	10.3.2.1 Arnheim-Gestalt and Aesthetics	295
	10.3.2.2 Berlyne-Arousal Potential and Preferences	295
	10.3.2.3 Martindale-Prototypicality and Neural Networks	297
	10.3.3 Empirical Studies of Human Aesthetics	299
	10.3.4 Neuroaesthetics	300
	10.3.5 Computing Inspired by Neurology	300
	10.3.6 The Neocortex and Hierarchical Temporal Memory	301
	10.3.7 Computer Architectures for Evolvable Hardware	302
	10.4 Conclusion	302
	References	303
	Chapter 11: Computing Aesthetics with Image Judgement Systems	310
	11.1 Introduction	310
	11.2 Validation Approaches for AJS	314
	11.2.1 Psychological Tests	314
	11.2.2 User Evaluation and Popularity Prediction	317
	11.2.3 Style and Author Classi?cation	320
	11.3 The Evolution of an AJS	322
	11.3.1 A Heuristic AJS	322
	11.3.2 Learning AJSs	324
	11.3.2.1 Feature Extraction	325
	Pre-processing	325
	Metrics Application	326
	Feature Building	327
	11.3.2.2 DJT Experiments	327
	11.3.2.3 Author Identi?cation Experiments	328
	11.3.2.4 Image Classi?cation Based on Online Evaluation	330
	11.3.2.5 Integration in an Image Generation System	330
	11.4 Conclusions	332
	References	333
	Chapter 12: A Formal Theory of Creativity to Model the Creation of Art	338
	12.1 The Basic Idea	338
	12.2 Relation to Previous, Less Formal Work	339
	12.3 Formal Details	341
	12.3.1 Continuous Time Formulation	344
	12.4 Previous Approximative Implementations of the Theory	345
	12.5 Aesthetic Reward = Change of Subjective Compressibility?	347
	12.6 Low-Complexity Art as End Product of a Search Process Modelled by the Formal Theory of Creativity	348
	12.7 Conclusion	349
	References	349
	Chapter 13: Creativity Re?ned: Bypassing the Gatekeepers of Appropriateness and Value	353
	13.1 Introduction	353
	13.2 What Is Creativity?	355
	13.3 De?ning Creativity	357
	13.3.1 Methods for Discovering Novel Representations	359
	13.3.2 Objective Versus Psychological Creativity	360
	13.4 Objections and Replies	361
	13.4.1 The Failure of Randomness	361
	13.4.2 The Verstehen Objection	362
	13.4.3 The Very Possibility of Creativity	363
	13.5 Consequences	363
	13.5.1 The Irrelevance of Value	363
	13.5.2 The Irrelevance of Appropriateness	364
	13.5.3 Inferring Frameworks from Patterns	364
	13.5.4 Creativity Viewed as Compression	364
	13.5.5 Degrees of Creativity	365
	13.6 Examples: Creativity in Human Endeavour	365
	Number Theory.	365
	Visual Arts.	365
	13.7 Examples: Creativity in Nature	366
	From Physics and Chemistry to Evolutionary Biology.	366
	Ecosystems.	366
	Dancing Bowerbirds, Painting Elephants and Primate Typists.	367
	13.8 Realising Our De?nition of Creativity in Software	368
	13.8.1 The Automatic Generation of Creative Biomorphs	368
	13.8.2 Testing Creative Software Against Human Concepts of Creativity	370
	13.9 Discussion	371
	13.10 Conclusions	372
	References	373
	Chapter 14: Generative and Adaptive Creativity: A Uni?ed Approach to Creativity in Nature, Humans and Machines	375
	14.1 Questions About Creativity	375
	14.2 Generative and Adaptive Creativity	377
	14.3 Generative and Adaptive Creativity in the Arts, in Humans, Human Groups and in Silico	379
	14.3.1 The Creativity of Social Systems Is More than the Sum of Individual Creative Acts	380
	14.3.2 Social Systems Can Exhibit Both Generative and Adaptive Creativity	382
	14.3.2.1 The Causes and Effects of Culture	382
	14.3.2.2 Social Groups as Adaptive Units	383
	14.3.2.3 Social Groups as Non-adaptive Generators	385
	14.3.2.4 Modelling Creativity in Social Systems	386
	14.3.3 Individual Humans Can Exhibit Generative and Adaptive Creativity	387
	14.4 Generative and Adaptive Approaches to Arts-Based Computational Creativity	388
	14.4.1 Generative Creative Systems Can Be Externally Useful	389
	14.4.2 Adaptive Creative Systems Can Be Useful to Others	390
	14.5 Conclusion	392
	References	393
	Chapter 15: Creating New Informational Primitives in Minds and Machines	396
	15.1 Introduction	396
	15.2 Emergence and Creativity	399
	15.2.1 What Constitutes a New Primitive?	400
	15.2.2 Primitives and Interpretive Frames	401
	15.2.3 Novel Combinations of Closed Sets of Primitives	402
	15.2.4 Limits on Computations on Existing Primitives	403
	15.2.5 Creation of New Primitives	405
	15.2.6 Combinatoric and Creative Emergence in Aesthetic Contexts	407
	15.3 Creativity in Self-constructing Cybernetic Percept-Action Systems	408
	15.3.1 A Taxonomy of Adaptive Devices	408
	15.3.2 Semiotics of Adaptive Devices	410
	15.3.3 Capabilities and Limitations of Adaptive Devices	411
	15.3.4 Pask's "Organic Analogues to the Growth of a Concept"	413
	15.3.5 Organisational Closure and Epistemic Autonomy	415
	15.4 Recognising Different Types of Creativity	416
	15.4.1 Emergence-Relative-to-a-Model	416
	15.4.2 Tracking Emergent Functions in a Device	416
	15.5 New Signal Primitives in Neural Systems	418
	15.5.1 New Primitives in Signalling Networks	419
	15.5.2 Brains as Networks of Adaptive Pattern-Resonances	420
	15.5.3 Regenerative Loops	421
	15.5.4 Multidimensional Signals	423
	15.5.5 Temporal Coding and Signal Multiplexing	423
	15.5.6 Emergent Annotative Tags and Their Uses	425
	References	427
	Part IV: Epilogue	431
	Chapter 16: Computers and Creativity: The Road Ahead	432
	16.1 Where to From Here?	432
	Index	436