Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavor that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts.
The goal of computational creativity is to model, simulate or
replicate creativity using a computer, to achieve one of several ends:
- To construct a program or computer capable of human-level creativity.
- To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans.
- To design programs that can enhance human creativity without necessarily being creative themselves.
The field of computational creativity concerns itself with
theoretical and practical issues in the study of creativity. Theoretical
work on the nature and proper definition of creativity is performed in
parallel with practical work on the implementation of systems that
exhibit creativity, with one strand of work informing the other.
Theoretical issues
As
measured by the amount of activity in the field (e.g., publications,
conferences and workshops), computational creativity is a growing area
of research.
But the field is still hampered by a number of fundamental problems.
Creativity is very difficult, perhaps even impossible, to define in
objective terms. Is it a state of mind, a talent or ability, or a
process? Creativity takes many forms in human activity, some eminent (sometimes referred to as "Creativity" with a capital C) and some mundane.
These are problems that complicate the study of creativity in general, but certain problems attach themselves specifically to computational creativity:
- Can creativity be hard-wired? In existing systems to which creativity is attributed, is the creativity that of the system or that of the system's programmer or designer?
- How do we evaluate computational creativity? What counts as creativity in a computational system? Are natural language generation systems creative? Are machine translation systems creative? What distinguishes research in computational creativity from research in artificial intelligence generally?
- If eminent creativity is about rule-breaking or the disavowal of convention, how is it possible for an algorithmic system to be creative? In essence, this is a variant of Ada Lovelace's objection to machine intelligence, as recapitulated by modern theorists such as Teresa Amabile. If a machine can do only what it was programmed to do, how can its behavior ever be called creative?
Indeed, not all computer theorists would agree with the premise that computers can only do what they are programmed to do—a key point in favor of computational creativity.
Defining creativity in computational terms
Because
no single perspective or definition seems to offer a complete picture
of creativity, the AI researchers Newell, Shaw and Simon
developed the combination of novelty and usefulness into the
cornerstone of a multi-pronged view of creativity, one that uses the
following four criteria to categorize a given answer or solution as
creative:
- The answer is novel and useful (either for the individual or for society)
- The answer demands that we reject ideas we had previously accepted
- The answer results from intense motivation and persistence
- The answer comes from clarifying a problem that was originally vague
Whereas the above reflects a "top-down" approach to computational
creativity, an alternative thread has developed among "bottom-up"
computational psychologists involved in artificial neural network
research. During the late 1980s and early 1990s, for example, such
generative neural systems were driven by genetic algorithms. Experiments involving recurrent nets were successful in hybridizing simple musical melodies and predicting listener expectations.
Concurrent with such research, a number of computational psychologists took the perspective, popularized by Stephen Wolfram,
that system behaviors perceived as complex, including the mind's
creative output, could arise from what would be considered simple
algorithms. As neuro-philosophical thinking matured, it also became
evident that language actually presented an obstacle to producing a
scientific model of cognition, creative or not, since it carried with it
so many unscientific aggrandizements that were more uplifting than
accurate. Thus questions naturally arose as to how "rich," "complex,"
and "wonderful" creative cognition actually was.
Artificial neural networks
Before 1989, artificial neural networks
have been used to model certain aspects of creativity. Peter Todd
(1989) first trained a neural network to reproduce musical melodies from
a training set of musical pieces. Then he used a change algorithm to
modify the network's input parameters. The network was able to randomly
generate new music in a highly uncontrolled manner. In 1992, Todd
extended this work, using the so-called distal teacher approach that had been developed by
Paul Munro, Paul Werbos, D. Nguyen, Bernard Widrow, Michael I. Jordan, David Rumelhart.
In the new approach there are two neural networks, one of which is
supplying training patterns to another.
In later efforts by Todd, a composer would select a set of melodies that
define the melody space, position them on a 2-d plane with a
mouse-based graphic interface, and train a connectionist network to
produce those melodies, and listen to the new "interpolated" melodies
that the network generates corresponding to intermediate points in the
2-d plane.
More recently a neurodynamical model of semantic networks
has been developed to study how the connectivity structure of these
networks relates to the richness of the semantic constructs, or ideas,
they can generate. It was demonstrated that semantic neural networks
that have richer semantic dynamics than those with other connectivity
structures may provide insight into the important issue of how the
physical structure of the brain determines one of the most profound
features of the human mind – its capacity for creative thought.
Key concepts from the literature
Some high-level and philosophical themes recur throughout the field of computational creativity.
Important categories of creativity
Margaret Boden refers to creativity that is novel merely to the agent that produces it as "P-creativity" (or "psychological creativity"), and refers to creativity that is recognized as novel by society at large
as "H-creativity" (or "historical creativity"). Stephen Thaler has
suggested a new category he calls "V-" or "Visceral creativity" wherein
significance is invented to raw sensory inputs to a Creativity Machine
architecture, with the "gateway" nets perturbed to produce alternative
interpretations, and downstream nets shifting such interpretations to
fit the overarching context. An important variety of such V-creativity
is consciousness itself, wherein meaning is reflexively invented to
activation turnover within the brain.
Exploratory and transformational creativity
Boden
also distinguishes between the creativity that arises from an
exploration within an established conceptual space, and the creativity
that arises from a deliberate transformation or transcendence of this
space. She labels the former as exploratory creativity and the latter as transformational creativity,
seeing the latter as a form of creativity far more radical,
challenging, and rarer than the former. Following the criteria from
Newell and Simon elaborated above, we can see that both forms of
creativity should produce results that are appreciably novel and useful
(criterion 1), but exploratory creativity is more likely to arise from a
thorough and persistent search of a well-understood space (criterion 3)
-- while transformational creativity should involve the rejection of
some of the constraints that define this space (criterion 2) or some of
the assumptions that define the problem itself (criterion 4). Boden's
insights have guided work in computational creativity at a very general
level, providing more an inspirational touchstone for development work
than a technical framework of algorithmic substance. However, Boden's
insights are more recently also the subject of formalization, most
notably in the work by Geraint Wiggins.
Generation and evaluation
The
criterion that creative products should be novel and useful means that
creative computational systems are typically structured into two phases,
generation and evaluation. In the first phase, novel (to the system
itself, thus P-Creative) constructs are generated; unoriginal
constructs that are already known to the system are filtered at this
stage. This body of potentially creative constructs are then evaluated,
to determine which are meaningful and useful and which are not. This
two-phase structure conforms to the Geneplore model of Finke, Ward and
Smith, which is a psychological model of creative generation based on empirical observation of human creativity.
Combinatorial creativity
A great deal, perhaps all, of human creativity can be understood as a novel combination of pre-existing ideas or objects. Common strategies for combinatorial creativity include:
- Placing a familiar object in an unfamiliar setting (e.g., Marcel Duchamp's Fountain) or an unfamiliar object in a familiar setting (e.g., a fish-out-of-water story such as The Beverly Hillbillies)
- Blending two superficially different objects or genres (e.g., a sci-fi story set in the Wild West, with robot cowboys, as in Westworld, or the reverse, as in Firefly; Japanese haiku poems, etc.)
- Comparing a familiar object to a superficially unrelated and semantically distant concept (e.g., "Makeup is the Western burka"; "A zoo is a gallery with living exhibits")
- Adding a new and unexpected feature to an existing concept (e.g., adding a scalpel to a Swiss Army knife; adding a camera to a mobile phone)
- Compressing two incongruous scenarios into the same narrative to get a joke (e.g., the Emo Philips joke "Women are always using me to advance their careers. Damned anthropologists!")
- Using an iconic image from one domain in a domain for an unrelated or incongruous idea or product (e.g., using the Marlboro Man image to sell cars, or to advertise the dangers of smoking-related impotence).
The combinatorial perspective allows us to model creativity as a
search process through the space of possible combinations. The
combinations can arise from composition or concatenation of different
representations, or through a rule-based or stochastic transformation of
initial and intermediate representations. Genetic algorithms and neural networks can be used to generate blended or crossover representations that capture a combination of different inputs.
Conceptual blending
Mark Turner and Gilles Fauconnier propose a model called Conceptual Integration Networks that elaborates upon Arthur Koestler's ideas about creativity as well as more recent work by Lakoff and Johnson, by synthesizing ideas from Cognitive Linguistic research into mental spaces and conceptual metaphors. Their basic model defines an integration network as four connected spaces:
- A first input space (contains one conceptual structure or mental space)
- A second input space (to be blended with the first input)
- A generic space of stock conventions and image-schemas that allow the input spaces to be understood from an integrated perspective
- A blend space in which a selected projection of elements from both input spaces are combined; inferences arising from this combination also reside here, sometimes leading to emergent structures that conflict with the inputs.
Fauconnier and Turner describe a collection of optimality principles
that are claimed to guide the construction of a well-formed integration
network. In essence, they see blending as a compression mechanism in
which two or more input structures are compressed into a single blend
structure. This compression operates on the level of conceptual
relations. For example, a series of similarity relations between the
input spaces can be compressed into a single identity relationship in
the blend.
Some computational success has been achieved with the blending
model by extending pre-existing computational models of analogical
mapping that are compatible by virtue of their emphasis on connected
semantic structures. More recently, Francisco Câmara Pereira presented an implementation of blending theory that employs ideas both from GOFAI and genetic algorithms
to realize some aspects of blending theory in a practical form; his
example domains range from the linguistic to the visual, and the latter
most notably includes the creation of mythical monsters by combining 3-D
graphical models.
Linguistic creativity
Language provides continuous opportunity for creativity, evident in the generation of novel sentences, phrasings, puns, neologisms, rhymes, allusions, sarcasm, irony, similes, metaphors, analogies, witticisms, and jokes. Native speakers of morphologically rich languages frequently create new word-forms that are easily understood, and some have found their way to the dictionary. The area of natural language generation
has been well studied, but these creative aspects of everyday language
have yet to be incorporated with any robustness or scale.
Hypothesis of creative patterns
In
the seminal work of applied linguist Ronald Carter, he hypothesized two
main creativity types involving words and word patterns:
pattern-reforming creativity, and pattern-forming creativity.
Pattern-reforming creativity refers to creativity by the breaking of
rules, reforming and reshaping patterns of language often through
individual innovation, while pattern-forming creativity refers to
creativity via conformity to language rules rather than breaking them,
creating convergence, symmetry and greater mutuality between
interlocutors through their interactions in the form of repetitions.
Story generation
Substantial
work has been conducted in this area of linguistic creation since the
1970s, with the development of James Meehan's TALE-SPIN system. TALE-SPIN viewed stories as narrative descriptions of a
problem-solving effort, and created stories by first establishing a goal
for the story's characters so that their search for a solution could be
tracked and recorded. The MINSTREL
system represents a complex elaboration of this basis approach,
distinguishing a range of character-level goals in the story from a
range of author-level goals for the story. Systems like Bringsjord's
BRUTUS
elaborate these ideas further to create stories with complex
inter-personal themes like betrayal. Nonetheless, MINSTREL explicitly
models the creative process with a set of Transform Recall Adapt Methods
(TRAMs) to create novel scenes from old. The MEXICA
model of Rafael Pérez y Pérez and Mike Sharples is more explicitly
interested in the creative process of storytelling, and implements a
version of the engagement-reflection cognitive model of creative
writing.
The company Narrative Science
makes computer generated news and reports commercially available,
including summarizing team sporting events based on statistical data
from the game. It also creates financial reports and real estate
analyses.
Metaphor and simile
Example of a metaphor: "She was an ape."
Example of a simile: "Felt like a tiger-fur blanket."
The computational study of these phenomena has mainly focused on
interpretation as a knowledge-based process. Computationalists such as Yorick Wilks, James Martin, Dan Fass, John Barnden,
and Mark Lee have developed knowledge-based approaches to the
processing of metaphors, either at a linguistic level or a logical
level. Tony Veale and Yanfen Hao have developed a system, called
Sardonicus, that acquires a comprehensive database of explicit similes
from the web; these similes are then tagged as bona-fide (e.g., "as hard
as steel") or ironic (e.g., "as hairy as a bowling ball", "as pleasant as a root canal");
similes of either type can be retrieved on demand for any given
adjective. They use these similes as the basis of an on-line metaphor
generation system called Aristotle
that can suggest lexical metaphors for a given descriptive goal (e.g.,
to describe a supermodel as skinny, the source terms "pencil", "whip", "whippet", "rope", "stick-insect" and "snake" are suggested).
Analogy
The
process of analogical reasoning has been studied from both a mapping and
a retrieval perspective, the latter being key to the generation of
novel analogies. The dominant school of research, as advanced by Dedre Gentner, views analogy as a structure-preserving process; this view has been implemented in the structure mapping engine or SME, the MAC/FAC retrieval engine (Many Are Called, Few Are Chosen), ACME (Analogical Constraint Mapping Engine) and ARCS (Analogical Retrieval Constraint System). Other mapping-based approaches include Sapper,
which situates the mapping process in a semantic-network model of
memory. Analogy is a very active sub-area of creative computation and
creative cognition; active figures in this sub-area include Douglas Hofstadter, Paul Thagard, and Keith Holyoak. Also worthy of note here is Peter Turney and Michael Littman's machine learning approach to the solving of SAT-style
analogy problems; their approach achieves a score that compares well
with average scores achieved by humans on these tests.
Joke generation
Humor is an especially knowledge-hungry process, and the most
successful joke-generation systems to date have focussed on
pun-generation, as exemplified by the work of Kim Binsted and Graeme
Ritchie. This work includes the JAPE
system, which can generate a wide range of puns that are consistently
evaluated as novel and humorous by young children. An improved version
of JAPE has been developed in the guise of the STANDUP system, which has
been experimentally deployed as a means of enhancing linguistic
interaction with children with communication disabilities. Some limited
progress has been made in generating humor that involves other aspects
of natural language, such as the deliberate misunderstanding of
pronominal reference (in the work of Hans Wim Tinholt and Anton
Nijholt), as well as in the generation of humorous acronyms in the
HAHAcronym system of Oliviero Stock and Carlo Strapparava.
Neologism
The
blending of multiple word forms is a dominant force for new word
creation in language; these new words are commonly called "blends" or "portmanteau words" (after Lewis Carroll). Tony Veale has developed a system called ZeitGeist that harvests neological headwords from Wikipedia and interprets them relative to their local context in Wikipedia and relative to specific word senses in WordNet.
ZeitGeist has been extended to generate neologisms of its own; the
approach combines elements from an inventory of word parts that are
harvested from WordNet, and simultaneously determines likely glosses for
these new words (e.g., "food traveler" for "gastronaut" and "time
traveler" for "chrononaut"). It then uses Web search
to determine which glosses are meaningful and which neologisms have not
been used before; this search identifies the subset of generated words
that are both novel ("H-creative") and useful. Neurolinguistic inspirations have been used to analyze the process of novel word creation in the brain, understand neurocognitive processes responsible for intuition, insight, imagination and creativity and to create a server that invents novel names for products, based on their description. Further, the system Nehovah
blends two source words into a neologism that blends the meanings of
the two source words. Nehovah searches WordNet for synonyms and
TheTopTens.com for pop culture hyponyms. The synonyms and hyponyms are
blended together to create a set of candidate neologisms. The neologisms
are then scored based on their word structure, how unique the word is,
how apparent the concepts are conveyed, and if the neologism has a pop
culture reference. Nehovah loosely follows conceptual blending.
A corpus linguistic approach to the search and extraction of neologism have also shown to be possible. Using Corpus of Contemporary American English as a reference corpus, Locky Law has performed an extraction of neologism, portmanteaus and slang words using the hapax legomena which appeared in the scripts of American TV drama House M.D.
In terms of linguistic research in neologism, Stefan Th. Gries
has performed a quantitative analysis of blend structure in English and
found that "the degree of recognizability of the source words and that
the similarity of source words to the blend plays a vital role in blend
formation." The results were validated through a comparison of
intentional blends to speech-error blends.
Poetry
Like jokes, poems involve a complex interaction of different
constraints, and no general-purpose poem generator adequately combines
the meaning, phrasing, structure and rhyme aspects of poetry.
Nonetheless, Pablo Gervás has developed a noteworthy system called ASPERA that employs a case-based reasoning
(CBR) approach to generating poetic formulations of a given input text
via a composition of poetic fragments that are retrieved from a
case-base of existing poems. Each poem fragment in the ASPERA case-base
is annotated with a prose string that expresses the meaning of the
fragment, and this prose string is used as the retrieval key for each
fragment. Metrical rules are then used to combine these fragments into a well-formed poetic structure. Racter is an example of such a software project.
Musical creativity
Computational
creativity in the music domain has focused both on the generation of
musical scores for use by human musicians, and on the generation of
music for performance by computers. The domain of generation has
included classical music (with software that generates music in the
style of Mozart and Bach) and jazz. Most notably, David Cope has written a software system called "Experiments in Musical Intelligence" (or "EMI")
that is capable of analyzing and generalizing from existing music by a
human composer to generate novel musical compositions in the same
style. EMI's output is convincing enough to persuade human listeners
that its music is human-generated to a high level of competence.
In the field of contemporary classical music, Iamus is the first computer that composes from scratch, and produces final scores that professional interpreters can play. The London Symphony Orchestra played a piece for full orchestra, included in Iamus' debut CD, which New Scientist described as "The first major work composed by a computer and performed by a full orchestra". Melomics, the technology behind Iamus, is able to generate pieces in different styles of music with a similar level of quality.
Creativity research in jazz has focused on the process of
improvisation and the cognitive demands that this places on a musical
agent: reasoning about time, remembering and conceptualizing what has
already been played, and planning ahead for what might be played next. The robot Shimon, developed by Gil Weinberg of Georgia Tech, has demonstrated jazz improvisation.
Virtual improvisation software based on machine learning models of
musical style include OMax, SoMax and PyOracle, are used to create
improvisations in real-time by re-injecting variable length sequences
learned on the fly from live performer.
In 1994, a Creativity Machine architecture (see above) was able
to generate 11,000 musical hooks by training a synaptically perturbed
neural net on 100 melodies that had appeared on the top ten list over
the last 30 years. In 1996, a self-bootstrapping Creativity Machine
observed audience facial expressions through an advanced machine vision
system and perfected its musical talents to generate an album entitled
"Song of the Neurons"
In the field of musical composition, the patented works by René-Louis Baron
allowed to make a robot that can create and play a multitude of
orchestrated melodies so-called "coherent" in any musical style. All
outdoor physical parameter associated with one or more specific musical
parameters, can influence and develop each of these songs (in real time
while listening to the song). The patented invention Medal-Composer raises problems of copyright.
Visual and artistic creativity
Computational
creativity in the generation of visual art has had some notable
successes in the creation of both abstract art and representational art.
The most famous program in this domain is Harold Cohen's AARON,
which has been continuously developed and augmented since 1973. Though
formulaic, Aaron exhibits a range of outputs, generating black-and-white
drawings or color paintings that incorporate human figures (such as
dancers), potted plants, rocks, and other elements of background
imagery. These images are of a sufficiently high quality to be displayed
in reputable galleries.
Other software artists of note include the NEvAr system (for "Neuro-Evolutionary Art") of Penousal Machado.
NEvAr uses a genetic algorithm to derive a mathematical function that
is then used to generate a coloured three-dimensional surface. A human
user is allowed to select the best pictures after each phase of the
genetic algorithm, and these preferences are used to guide successive
phases, thereby pushing NEvAr's search into pockets of the search space
that are considered most appealing to the user.
The Painting Fool, developed by Simon Colton
originated as a system for overpainting digital images of a given scene
in a choice of different painting styles, colour palettes and brush
types. Given its dependence on an input source image to work with, the
earliest iterations of the Painting Fool raised questions about the
extent of, or lack of, creativity in a computational art system.
Nonetheless, in more recent work, The Painting Fool has been extended to
create novel images, much as AARON
does, from its own limited imagination. Images in this vein include
cityscapes and forests, which are generated by a process of constraint
satisfaction from some basic scenarios provided by the user (e.g., these
scenarios allow the system to infer that objects closer to the viewing
plane should be larger and more color-saturated, while those further
away should be less saturated and appear smaller). Artistically, the
images now created by the Painting Fool appear on a par with those
created by Aaron, though the extensible mechanisms employed by the
former (constraint satisfaction, etc.) may well allow it to develop into
a more elaborate and sophisticated painter.
The artist Krasimira Dimtchevska and the software developer
Svillen Ranev have created a computational system combining a rule-based
generator of English sentences and a visual composition builder that
converts sentences generated by the system into abstract art.
The software generates automatically indefinite number of different
images using different color, shape and size palettes. The software also
allows the user to select the subject of the generated sentences or/and
the one or more of the palettes used by the visual composition builder.
An emerging area of computational creativity is that of video
games. ANGELINA is a system for creatively developing video games in
Java by Michael Cook. One important aspect is Mechanic Miner, a system
which can generate short segments of code which act as simple game
mechanics.
ANGELINA can evaluate these mechanics for usefulness by playing simple
unsolvable game levels and testing to see if the new mechanic makes the
level solvable. Sometimes Mechanic Miner discovers bugs in the code and
exploits these to make new mechanics for the player to solve problems
with.
In July 2015 Google released DeepDream – an open source
computer vision program, created to detect faces and other patterns in
images with the aim of automatically classifying images, which uses a
convolutional neural network to find and enhance patterns in images via
algorithmic pareidolia, thus creating a dreamlike psychedelic appearance in the deliberately over-processed images.
In August 2015 researchers from Tübingen, Germany
created a convolutional neural network that uses neural representations
to separate and recombine content and style of arbitrary images which
is able to turn images into stylistic imitations of works of art by
artists such as a Picasso or Van Gogh in about an hour. Their algorithm is put into use in the website DeepArt that allows users to create unique artistic images by their algorithm.
In early 2016, a global team of researchers explained how a new
computational creativity approach known as the Digital Synaptic Neural
Substrate (DSNS) could be used to generate original chess puzzles that
were not derived from endgame databases.
The DSNS is able to combine features of different objects (e.g. chess
problems, paintings, music) using stochastic methods in order to derive
new feature specifications which can be used to generate objects in any
of the original domains. The generated chess puzzles have also been
featured on YouTube.
Creativity in problem solving
Creativity is also useful in allowing for unusual solutions in problem solving. In psychology and cognitive science, this research area is called creative problem solving. The Explicit-Implicit Interaction (EII) theory of creativity has recently been implemented using a CLARION-based computational model that allows for the simulation of incubation and insight in problem solving. The emphasis of this computational creativity project is not on performance per se (as in artificial intelligence
projects) but rather on the explanation of the psychological processes
leading to human creativity and the reproduction of data collected in
psychology experiments. So far, this project has been successful in
providing an explanation for incubation effects in simple memory
experiments, insight in problem solving, and reproducing the
overshadowing effect in problem solving.
Debate about "general" theories of creativity
Some
researchers feel that creativity is a complex phenomenon whose study is
further complicated by the plasticity of the language we use to
describe it. We can describe not just the agent of creativity as
"creative" but also the product and the method. Consequently, it could
be claimed that it is unrealistic to speak of a general theory of creativity.
Nonetheless, some generative principles are more general than others,
leading some advocates to claim that certain computational approaches
are "general theories". Stephen Thaler, for instance, proposes that
certain modalities of neural networks are generative enough, and general
enough, to manifest a high degree of creative capabilities. Likewise,
the Formal Theory of Creativity is based on a simple computational principle published by Jürgen Schmidhuber in 1991. The theory postulates that creativity and curiosity and selective attention in general are by-products of a simple algorithmic principle for measuring and optimizing learning progress.
Criticism of Computational Creativity
Traditional computers, as mainly used in the computational creativity
application, do not support creativity, as they fundamentally transform
a set of discrete, limited domain of input parameters into a set of
discrete, limited domain of output parameters using a limited set of
computational functions.
As such, a computer cannot be creative, as everything in the output
must have been already present in the input data or the algorithms.
For some related discussions and references to related work are
captured in some recent work on philosophical foundations of simulation.
Mathematically, the same set of arguments against creativity has been made by Chaitin.
Similar observations come from a Model Theory perspective. All this
criticism emphasizes that computational creativity is useful and may
look like creativity, but it is not real creativity, as nothing new is
created, just transformed in well defined algorithms.
Events
The International Conference on Computational Creativity (ICCC) occurs annually, organized by The Association for Computational Creativity. Events in the series include:
- ICCC 2018, Salamanca, Spain
- ICCC 2017, Atlanta, Georgia, USA
- ICCC 2016, Paris, France
- ICCC 2015, Park City, Utah, USA. Keynote: Emily Short
- ICCC 2014, Ljubljana, Slovenia. Keynote: Oliver Deussen
- ICCC 2013, Sydney, Australia. Keynote: Arne Dietrich
- ICCC 2012, Dublin, Ireland. Keynote: Steven Smith
- ICCC 2011, Mexico City, Mexico. Keynote: George E Lewis
- ICCC 2010, Lisbon, Portugal. Keynote/Inivited Talks: Nancy J Nersessian and Mary Lou Maher
Previously, the community of computational creativity has held a
dedicated workshop, the International Joint Workshop on Computational
Creativity, every year since 1999. Previous events in this series
include:
- IJWCC 2003, Acapulco, Mexico, as part of IJCAI'2003
- IJWCC 2004, Madrid, Spain, as part of ECCBR'2004
- IJWCC 2005, Edinburgh, UK, as part of IJCAI'2005
- IJWCC 2006, Riva del Garda, Italy, as part of ECAI'2006
- IJWCC 2007, London, UK, a stand-alone event
- IJWCC 2008, Madrid, Spain, a stand-alone event
The 1st Conference on Computer Simulation of Musical Creativity will be held
- CCSMC 2016, 17–19 June, University of Huddersfield, UK. Keynotes: Geraint Wiggins and Graeme Bailey.
Publications and forums
Design
Computing and Cognition is one conference that addresses computational
creativity. The ACM Creativity and Cognition conference is another forum
for issues related to computational creativity. Journées d'Informatique
Musicale 2016 keynote by Shlomo Dubnov was on Information Theoretic
Creativity.
A number of recent books provide either a good introduction or a
good overview of the field of Computational Creativity. These include:
- Pereira, F. C. (2007). "Creativity and Artificial Intelligence: A Conceptual Blending Approach". Applications of Cognitive Linguistics series, Mouton de Gruyter.
- Veale, T. (2012). "Exploding the Creativity Myth: The Computational Foundations of Linguistic Creativity". Bloomsbury Academic, London.
- McCormack, J. and d'Inverno, M. (eds.) (2012). "Computers and Creativity". Springer, Berlin.
- Veale, T., Feyaerts, K. and Forceville, C. (2013, forthcoming). "Creativity and the Agile Mind: A Multidisciplinary study of a Multifaceted phenomenon". Mouton de Gruyter.
In addition to the proceedings of conferences and workshops, the
computational creativity community has thus far produced these special
journal issues dedicated to the topic:
- New Generation Computing, volume 24, issue 3, 2006
- Journal of Knowledge-Based Systems, volume 19, issue 7, November 2006
- AI Magazine, volume 30, number 3, Fall 2009
- Minds and Machines, volume 20, number 4, November 2010
- Cognitive Computation, volume 4, issue 3, September 2012
- AIEDAM, volume 27, number 4, Fall 2013
- Computers in Entertainment, two special issues on Music Meta-Creation (MuMe), Fall 2016 (forthcoming)
In addition to these, a new journal has started which focuses on computational creativity within the field of music.
- JCMS 2016, Journal of Creative Music Systems
