Veale T., Cardoso A. (eds.) Computational Creativity: The Philosophy and Engineering of Autonomously Creative Systems

New York: Springer, 2019. — 403 p.From C to CC
The Association for Computational Creativity
The PROSECCO Vision
A Thematic Overview
Conclusion: Baby Steps in the Right DirectionBackground: Boden's Analysis of Creative Systems
Terminology
A Universe of Possibilities
Defining the Conceptual Space
Exploring the Conceptual Space
The Value of Two Rule Sets, R and T
Characterising an Exploratory Creative System
Exploring and Transforming
Transformational Creativity
Creative Behaviour and the Meta-level
Combinatorial Creativity
Creative Behaviour Is Not Just Traditional AI Search
Uninspiration
Aberration
Discussion
The Dark Ages and the Proto-Renaissance
The Renaissance period
The Baroque Period
Modernist Music
Summary and ConclusionDARCI
Image Perception
Semantic Network
Image Generation
Image Rendering
Perception-Based Understanding
Communicating Intention
Imagination
Meta-level Artefacts
Evaluation
Evaluating Semantic Transferability
Evaluating Semantic Coherence
Concluding RemarksThe Plumbing of Creative Thought
Related Work and Ideas
``Milking'' Knowledge from the Web
Conceptual ``Mash-ups''
Multisource Mash-ups
Empirical Evaluation
ConclusionsThe CB Framework: An Overview
Optimality Principles
Computational Approaches to Conceptual Blending
Divago
Evaluating the Optimality Constraints
The Pegasus
Other Creatures
Recent Developments
ConclusionsChapter Bisociative Knowledge Discovery for Cross-domain Literature Mining
Related Work
Bisociative Knowledge Discovery
Literature-Based Discovery
Creativity Support Tools and HCI
CrossBee Methodology
Heuristics for Bridging-Term Discovery
Ensemble Heuristic
Exploring Outlier Documents in Literature-Based Discovery
Outlier Document Detection and B-term Identification Through Document Classification
Outlier Document Detection and B-term Identification Through Document Clustering
Relating Outlier Document Detection to CrossBee Heuristics
Conclusions and Further WorkCreativity in Design
Analogical Thinking in Creative Design
Model-Based Analogy
Biologically Inspired Design
Model-Based Analogies in Biologically Inspired Design
ConclusionsThe Need for Evaluation
Two Meanings of ``Creative''
Characteristics of a CreativeL System
Varieties of Goals
Two Kinds of ``Evaluation''
Descriptions, Causes and Symptoms
Boden's Analysis
Some Symptomatic Criteria
The Creative Tripod
Vocabulary Analysis
Formative versus Summative Evaluation
Levels of Performance
Organising Evaluation Runs
Quality or Creativity?
Effects
Naturalistic Setting
Use of Judges
The Turing Test
Could It Be Automated?
ConclusionsIntroduction: Novelty as Violated Expectations
Expectation-Based Novelty for Evaluating Creative Artefacts
A Formal Model of Expectation-Based Novelty
Related Approaches to Creativity Evaluation
Implementing Expectation-Based Novelty
How Expectation-Based Novelty Affects the Generation of Creative Artefacts
Implementing Expectation-Based GenerationChapter Evaluating Evaluation: Assessing Progress and Practices in Computational Creativity Research
The Role of Evaluation and Why It Is Needed
Development of Creativity Evaluation Practices Over Time
Definitional Difficulties in Evaluating Creativity
Conflicting Messages on the Importance of Evaluating the Creativity of Computational Creativity Systems
Difficulties in Finding Relevant Systems for Comparison
Different Types of Evaluation
Why Not Just Ask Humans How Creative Our Systems Are?
Issues in Providing a Standard Tool Across Creativity
Standardising Our Approach to Evaluation
Identifying the Relative Contributions of Different Aspects in a Creative Domain
Step : Testing Systems Using the Components
The Intention of the SPECS Approach
Incorporating Other Evaluation Frameworks
Key Standardised Aspects of SPECS
Evaluating Creativity Evaluation Methods
Concluding RemarksChapter Computer-Supported Human Creativity and Human-Supported Computer Creativity in LanguageMinimally Interactive Systems
Interactive Systems
Computer-Supported Human Creativity
GraphLaugh
Subvertiser
Human-Supported Computer Creativity
Heady-Lines
ConclusionsCommon-Sense Knowledge
Characteristics of Story Actions
MEXICA
Representation of Knowledge
Employing CSK to Generate Coherent Sequences of Actions
ConclusionsExisting Formalisations of Creativity Measurement
Assessing Creativity Based on How Good and How Typical the Results Are
Automatic Generation of Poetry: Approaches and Evaluation Issues
Poetry Generation Based on Textual Templates
Assessing Outputs Obtained from Text-Based Templates
Poetry Generation Based on Syntactic Templates
Poetry Generation as Prose-to-Verse Conversion
Starting from Semantic Relations Between Words
Poetry Generation Based on Semantic Relations
Poetry Generation Targeting Specific Emotions
Poetry Generation Targeting Specific Semantic Content
Assessing Outputs Obtained from Semantically Specified Content
Assessing Outputs Obtained from Evolutionary Approaches
Poetry Generation Based on n-Gram Language Models
Assessing Outputs Obtained from n-Gram Models of Language
Applying Creativity Measurements to a Particular Example
Assessing Creativity Based on Existing Evaluation
Effect of Evaluation Parameters on Creativity Assessment
Evaluation of the Degree of Fine Tuning
Analysis of the Results
ConclusionsA Systems View of Creativity
Modelling the Systems View of Creativity Computationally
Artificial Creative Systems
Fields
Individuals
Novelty Detection
Hedonic Functions
The Digital Clockwork Muse
The Law of Novelty
The Formation of Cliques
Domains
Individuals
InteractionsCreative Systems and Post-anthropocentric Creativity
Spaces of Possibility
Evolutionary Computing and Creativity
Ecosystems
Biological Ecosystems
Design and Architecture
Visual and Installation Art
Ecosystem Design Patterns
Environments: Conditions and Resources
Self-observation and Feedback
Automation and the Creative Role of the Artist
ConclusionsChapter Breaking the MouldState of the Art
The Framework
Classifier System
Feature Extraction
Artificial Neural Network
Initial Datasets
Evolutionary Engine
Archive Assessment
Selection Mechanism
Experimental Results
Analysis of the Numeric Results Concerning Evolution
Analysis of the Visual Results
First Iteration
Intermediate Iterations
Thirteenth Iteration
Training of the Classifiers
Conclusions and Future Work