Artificial Intelligence and the Arts: Computational Creativity, Artistic Behavior, and Tools for Creatives

Preface
	References
Contents
List of Contributors
Part I Visual Arts
	1 Artificial Life and Artificial Intelligence Advances in the Visual Arts
		1.1 Introduction
		1.2 Swarm Art
			1.2.1 Agent-Based Swarms
			1.2.2 Ant Paintings
				Abstract Ant Paintings
				Representational Ant Paintings
				Ant-Behavior Art
		1.3 Robotics
			1.3.1 Virtual Robots
			1.3.2 Physical Robots
		1.4 Neural Nets and Deep Learning
			1.4.1 Neural Nets
			1.4.2 Deep Learning
			1.4.3 Artistic Analysis
		1.5 Computational Aesthetics and Creativity
			1.5.1 Computational Aesthetics
			1.5.2 Computational Creativity
			1.5.3 Knowledge-Based Agents
			1.5.4 Artificial Societies
		1.6 Conclusion
		References
	2 Aesthetics, Artificial Intelligence, and Search-Based Art
		2.1 Introduction
		2.2 Search, AI, and Art
			2.2.1 What Drives Search in Creative Domains?
		2.3 Aesthetic Theories
		2.4 Imitation
			2.4.1 Aesthetic Rescaling
			2.4.2 Exposing Inner Workings
		2.5 Skill and Expertise
		2.6 Expression
			2.6.1 Can Soulless Computers Express?
			2.6.2 Expression Without Transmission
			2.6.3 Expression, Emotion, and Expression Systems
			2.6.4 Self-Contagion
			2.6.5 Expression: Summary
		2.7 Form
			2.7.1 Form and Aesthetic Measure
			2.7.2 Form and Corpus
			2.7.3 Form and Multicriterion Optimisation
			2.7.4 Form: Summary
		2.8 Focus and Sake
		2.9 Imaginative Experience
		2.10 Criticism and the Artworld
		2.11 Aesthetics as a Cluster Concept
		2.12 Conclusions
			2.12.1 Aesthetic Theories and AI Art
			2.12.2 Expanding Theories for AI Art
			2.12.3 Gaps and Assumptions
			2.12.4 Understanding Human Art-Making
			2.12.5 Future Directions
		References
	3 Applicability of Convolutional Neural Network Artistic Style Transfer Algorithms
		3.1 Artistic Style Transfer
			3.1.1 Concept
				Formalization of Artistic Style Transfer
		3.2 State of the Art Style Transfer
			3.2.1 Analogy Style
			3.2.2 Neural Style
				Other Networks
				Fast Style Transfer
			3.2.3 Structural Semantic
		3.3 Conclusion and Outlook
		References
		Images
Part II Music
	4 Artificial Musical Intelligence: Computational Creativity in a Closed Cognitive World
		4.1 Music
		4.2 What Is Music?
		4.3 What Are the Components of Musical Behaviour?
		4.4 What Does Music Mean?
		4.5 What Does the Future Hold for Arti cial Musical Intelligence?
		4.6 Where Is Creativity in AMI?
		References
	5 Evolutionary Music, Deep Learning and Conceptual Blending: Enhancing User Involvement in Generative Music Systems
		5.1 Introduction
		5.2 Music, Information and Evolutionary Exploration of Alternatives
			5.2.1 Symbolic Feature Extraction and Music Information
			5.2.2 Generating Musical Alternatives with Given Specific Qualities
		5.3 Power to the User: Exploring Variation and Human Preference in Generative Systems
			5.3.1 User Involvement in Generating Variations
				Variation Using Features and Distance
				Towards a User-Machine Creative Loop
			5.3.2 Interactive Evolution: Capturing, Assessing and Expanding User Preference
		5.4 Breaking New Ground: Evolution, Machine Learning and Conceptual Blending
			5.4.1 Cognitive Models of Creativity and Conceptual Blending
			5.4.2 Evolutionary Computation, Machine Learning and User-Driven Conceptual Blending for Melody Generation
			Melodic Representation, Learning and Generation
				Explicit Melodic Features
				Implicit, Deep Machine Learning and System Architecture
			Example Scenario
		5.5 Conclusion
		References
	6 Representation Learning for the Arts: A Case Study Using Variational Autoencoders for Drum Loops
		6.1 Introduction
		6.2 Approaches and Issues in Representation Learning
			6.2.1 Dimensionality Reduction
			6.2.2 Manual Design of Representations
			6.2.3 Representation Learning with Neural Networks
				Dimensionality
				Disentangled Representations
				L2 Normalisation
			6.2.4 Neural Networks and Music
			6.2.5 Evaluating Representation Learning
		6.3 Case Study: Representation Learning for MIDI Drum Tracks
			6.3.1 Data and Preprocessing
			6.3.2 Proposed Model
				Hyperparameters
			6.3.3 Evaluation
				Loss During Training
				Distribution of Latent Codes
				Numerical Experiments
				Samples
				Listening Tests and Reconstructions
				Interpolations
				User Interface and User Experience
				Disentanglement and the Beta VAE
		6.4 Conclusions
			6.4.1 Future Work
		References
Part III 3D
	7 Case Studies in Computer Graphics and AI
		7.1 Introduction
		7.2 Materials and Methods
			NodeBox
			Pattern
		7.3 Results
			7.3.1 Evolution (Agent-Based + Genetic Algorithm)
			7.3.2 Creatures (Procedural Generation)
			7.3.3 Perception (Knowledge Representation)
			7.3.4 Timeline of Paintings (Knowledge Representation)
			7.3.5 Valence (Human-Computer Interaction)
			7.3.6 Virtual Underwater World (Human-Computer Interaction)
			7.3.7 AI Stories (NLG + ML)
		7.4 Analysis
			Evidence-based design
			Participatory design
		7.5 Conclusions
		References
	8 Setting the Stage for 3D Compositing with Machine Learning
		8.1 Introduction
		8.2 Challenges
			8.2.1 Data
			8.2.2 Frameworks
			8.2.3 Introspection
			8.2.4 Evaluation
		8.3 Case Study: 3D Compositing
			8.3.1 Typical Compositing Work ows
			8.3.2 A Machine Learning Driven Compositing Workflow
				Perspective
				Environment Light
				Discrete Lighting
				Outdoor Lighting
				Indoor Lighting
				Final Steps
		8.4 User Feedback
		8.5 Machine Learning Lessons
			8.5.1 Representation
			8.5.2 Training
			8.5.3 Evaluation
			8.5.4 Con dence Classi cation
		8.6 Conclusion
		References
Part IV Other Art Forms
	9 Computational Models of Narrative Creativity
		9.1 Introduction
			9.1.1 Overview of Chapter Organization
		9.2 Relevant Concepts from Related Fields
			9.2.1 Narratology
			9.2.2 Natural Language Generation
			9.2.3 Cognitive Science
		9.3 Building Fabula: Inventing Content
			9.3.1 Plot-Based Fabula Generation
			9.3.2 Character-Based Fabula Generation
			Modelling Character Emotions
			Modelling Affnities Between Characters
			Modelling Social Norms
			Modelling Conflict
		9.4 Constructing Discourse: Arranging the Telling of Stories
			9.4.1 Systems That Generate Discourse Based on Accounts of Plot
			9.4.2 Systems That Generate Discourse Based on Neural Networks
			9.4.3 Emergent Discourse in Interactive Systems
			9.4.4 Systems That Compose a Discourse for a Given Fabula or Storyworld
				Basic Concepts of Narrative Discourse
				Simple Instances of Narrative Composition
				Discourse Generation Based on Focalisation
				Discourse Generation Based on Chronology
				Discourse Generation Based on Suspense
			9.4.5 Systems That Combine Fabula and Discourse Construction
		9.5 Producing Renderings for Proto-literary Drafts
			9.5.1 Systems That Generate Text Directly
			9.5.2 Systems That Generate Renderings for a Given Discourse
				Prose
				Dialogue
				Visual Discourse
		9.6 Modelling Cognitive Aspects of Literary Creation
			9.6.1 Computational Appreciation of Proto-Literary Drafts
			9.6.2 Generation Systems Based on Cognitive Accounts of Writing
			9.6.3 Evolutionary Generation Systems
		9.7 Conclusions
		References
	10 Learning from Responses to Automated Videogame Design
		10.1 Introduction
			10.1.1 Stakeholders
			10.1.2 Background
		10.2 Press
			10.2.1 Replacing Humans
			10.2.2 Gender and Names
			10.2.3 Secondary Sources
		10.3 Public
			10.3.1 Expectations
				Mass Production of Creative Artefacts
				Fear
			10.3.2 Exhibition Structure
		10.4 Peers
			10.4.1 Optimism
			10.4.2 Peer Communication
		10.5 Conclusions
			10.5.1 Everything Is Context
			10.5.2 Stakeholder Goals
			10.5.3 Relationships
		References
	11 Artificial Intelligence for Designing Games
		11.1 Introduction
		11.2 Games as a Multi-faceted Creative Domain
			11.2.1 Visuals
			11.2.2 Audio
			11.2.3 Narrative
			11.2.4 Levels
			11.2.5 Rules
			11.2.6 Gameplay
		11.3 Orchestration
		11.4 Procedural Content Generation in Games
		11.5 Arti cial Intelligence and Game Design
			11.5.1 Generating Content Through AI
			11.5.2 Orchestrating Game Generation
			11.5.3 Cases of AI-Based Game Design
		11.6 Conclusions
			11.6.1 Future Directions
			11.6.2 Challenges
			11.6.3 Parting Words
		References
Part V Artistic Perspectives
	12 Bees Select Flowers, Humans Select Images: New Designs for Open-Ended Interactive Evolutionary Computation Inspired by Pollination Ecology
		12.1 An Introduction to the Open-Ended Interactive Evolution of Images
			12.1.1 What Is IEC?
			12.1.2 What Are the Limitations of IEC?
			12.1.3 What Is the Goal of IEC?
		12.2 Creativity in Art and Evolution
			12.2.1 Defining Creativity
			12.2.2 Transformational Creativity and Painting Styles
			12.2.3 Is Painting Open-Ended, or Dead?
			12.2.4 Exploratory Creativity in Painting
			12.2.5 Combinational Creativity
		12.3 Creativity, Biological Evolution and Pollination Ecology
			12.3.1 Evolution Doesn't Prefer Novelty (But Produces It All the Same)
			12.3.2 Humans Prefer Novelty (But IEC Software Struggles to Produce It)
		12.4 Creativity and Open-Endedness of Syles in Digital Image-Making
			12.4.1 Rendering Lines with Turtle LOGO
			12.4.2 Rendering Re ections, Ray-Tracing
			12.4.3 Rendering Complexity, Fractals
			12.4.4 Image Synthesis Techniques as Visual Styles
		12.5 Open-Ended IEC
			12.5.1 Are Existing IEC Implementations Open-Ended?
			12.5.2 A Fundamental Diffculty of Navigating the Space of Images and Styles
			12.5.3 Learning from the Bees to See Beyond the Limitations of Existing IEC
			12.5.4 Insect-Pollinated Flowering Plants Form Part of a Community
			12.5.5 Bees learn
			12.5.6 Bees remember
			12.5.7 Bee decision-making
			12.5.8 Bees search collectively
		12.6 Conclusion
		References
	13 Breaking the Black Box: Procedural Reading, Creation of Meaning, and Closure in Computational Artworks
		13.1 Introduction
		13.2 Computational Artworks
		13.3 Subface-Surface Duality
		13.4 Ergodicity
		13.5 Open-Ended Works
		13.6 Meaning
		13.7 Closure
		13.8 Closure in Computational Media
		13.9 Procedural Reading
		13.10 Development of a Theory of the System
		13.11 Consequences of Procedural Reading
		13.12 Procedural Reading and Aesthetic Pleasure
		13.13 Intersubjectivity and Computational Art
		References
	14 Organism-Machine Hybrids
		14.1 Introduction: Designing with Nature
		14.2 Creating with Nature
			14.2.1 The Organism as Programmable Builder
			14.2.2 Trivariate Co-Creation
		14.3 Classification of Approaches
			14.3.1 Nano-Scale Interventions
			14.3.2 Bio-Scaffolds
			14.3.3 Fabricated Host Materials
			14.3.4 Tropisms
			14.3.5 Robotic Augmentation
		14.4 Case Study: Mycelium Experiments
			14.4.1 Material Explorations
			14.4.2 Design Tectonics
			14.4.3 Computational Simulation
			14.4.4 Results
		14.5 Conclusions
		References