Computational and Collective Creativity: Who’s Being Creative?
Mary Lou Maher
University of Maryland
mlmaher@umd.edu
Abstract
Creativity research has traditionally focused on human creativity,
and even more specifically, on the psychology of individual
creative people. In contrast, computational creativity
research involves the development and evaluation of
creativity in a computational system. As we study the effect
of scaling up from the creativity of a computational system
and individual people to large numbers of diverse computational
agents and people, we have a new perspective: creativity
can ascribed to a computational agent, an individual
person, collectives of people and agents and/or their interaction.
By asking “Who is being creative?” this paper examines
the source of creativity in computational and collective
creativity. A framework based on ideation and interaction
provides a way of characterizing existing research in computational
and collective creativity and identifying directions
for future research.
Human and Computational Creativity
Creativity is a topic of philosophical and scientific study
considering the scenarios and human characteristics that
facilitate creativity as well as the properties of computational
systems that exhibit creative behavior. “The four Ps
of creativity”, as introduced in Rhodes (1987) and more recently
summarized by Runco (2011), decompose the complexity
of creativity into separate but related influences:
• Person: characteristics of the individual,
• Product: an outcome focus on ideas,
• Press: the environmental and contextual factors,
• Process: cognitive process and thinking techniques.
While the four Ps are presented in the context of the psychology
of human creativity, they can be modified for
computational creativity if process includes a computational
process. The study of human creativity has a focus
on the characteristics and cognitive behavior of creative
people and the environments in which creativity is facilitated.
The study of computational creativity, while inspired
by concepts of human creativity, is often expressed in the
formal language of search spaces and algorithms.
Why do we ask who is being creative? Firstly, there is
an increasing interest in understanding computational systems
that can formalize or model creative processes and
therefore exhibit creative behaviors or acts. Yet there are
still skeptics that claim computers aren’t creative, the computer
is just following instructions. Second and in contrast,
there is increasing interest in computational systems that
encourage and enhance human creativity that make no
claims about whether the computer is being or could be
creative. Finally, as we develop more capable socially intelligent
computational systems and systems that enable
collective intelligence among humans and computers, the
boundary between human creativity and computer creativity
blurs. As the boundary blurs, we need to develop ways
of recognizing creativity that makes no assumptions about
whether the creative entity is a person, a computer, a potentially
large group of people, or the collective intelligence
of human and computational entities. This paper
presents a framework that characterizes the source of creativity
from two perspectives, ideation and interaction, as a
guide to current and future research in computational and
collective creativity.
Creativity: Process and Product
Understanding the nature of creativity as process and product
is critical in computational creativity if we want to
avoid any bias that only humans are creative and computers
are not. While process and product in creativity are
tightly coupled in practice, a distinction between the two
provides two ways of recognizing computational creativity
by describing the characteristics of a creative process and
separately, the characteristics of a creative product. Studying
and describing the processes that generate creative
products focus on the cognitive behavior of a creative person
or the properties of a computational system, and describing
ways of recognizing a creative product focus on
the characteristics of the result of a creative process.
When describing creative processes there is an assumption
that there is a space of possibilities. Boden (2003) refers
to this as conceptual spaces and describes these spaces
as structured styles of thought. In computational systems
such a space is called a state space. How such spaces are
changed, or the relationship between the set of known
products, the space of possibilities, and the potentially
creative product, is the basis for describing processes that
can generate potentially creative artifacts.
There are many accounts of the processes for generating
creative products. Two sources are described here: Boden
(2003) from the philosophical and artificial intelligence
perspective and Gero (2000) from the design science perspective.
Boden (2003) describes three ways in which creative
products can be generated: combination, exploration,
International Conference on Computational Creativity 2012 67
and transformation: each one describes the way in which
the conceptual space of known products provides a basis
for generating a creative product and how the conceptual
space changes as a result of the creative artifact. Combination
brings together two or more concepts in ways that
hasn’t occurred in existing products. Exploration finds
concepts in parts of the space that have not been considered
in existing products. Transformation modifies concepts
in the space to generate products that change the
boundaries of the space. Gero (2000) describes computational
processes for creative design as combination, transformation,
analogy, emergence, and first principles. Combination
and transformation are similar to Boden’s processes.
Analogy transfers concepts from a source product
that may be in a different conceptual space to a target
product to generate a novel product in the target’s space.
Emergence is a process that finds new underlying structures
in a concept that give rise to a new product, effectively
a re-representation process. First principles as a
process generates new products without relying on concepts
as defined in existing products.
While these processes provide insight into the nature of
creativity and provide a basis for computational creativity,
they have little to say about how we recognize a creative
product. As we move towards computational systems that
enhance or contribute to human creativity, the articulation
of process models for generating creative artifacts does not
provide an evaluation of the product. Computational systems
that generate creative products need evaluation criteria
that are independent of the process by which the product
was generated.
There are also numerous approaches to defining characteristics
of creative products as the basis for evaluating or
assessing creativity. Boden (2003) claims that novelty and
value are the essential criteria and that other aspects, such
as surprise, are kinds of novelty or value. Wiggins (2006)
often uses value to indicate all valuable aspects of a creative
products, yet provides definitions for novelty and
value as different features that are relevant to creativity.
Oman and Tumer (2009) combine novelty and quality to
evaluate individual ideas in engineering design as a relative
measure of creativity. Shah, Smith, and Vargas-Hernandez
(2003) associate creative design with ideation and develop
metrics for novelty, variety, quality, and quantity of ideas.
Wiggins (2006) argues that surprise is a property of the receiver
of a creative artifact, that is, it is an emotional response.
Cropley and Cropley (2005) propose four broad
properties of products that can be used to describe the level
and kind of creativity they possess: effectiveness, novelty,
elegance, genesis. Besemer and O'Quin (1987) describe a
Creative Product Semantic Scale which defines the creativity
of products in three dimensions: novelty (the product is
original, surprising and germinal), resolution (the product
is valuable, logical, useful, and understandable), and elaboration
and synthesis (the product is organic, elegant, complex,
and well-crafted). Horn and Salvendy (2006) after
doing an analysis of many properties of creative products,
report on consumer perception of creativity in three critical
perceptions: affect (our emotional response to the product),
importance, and novelty. Goldenberg and Mazursky (2002)
report on research that has found the observable characteristics
of creativity in products to include "original, of
value, novel, interesting, elegant, unique, surprising."
Amabile (1982) says it most clearly when she summarizes
the social psychology literature on the assessment of
creativity: While most definitions of creativity refer to
novelty, appropriateness, and surprise, current creativity
tests or assessment techniques are not closely linked to
these criteria. She further argues that “There is no clear,
explicit statement of the criteria that conceptually underlie
the assessment procedures.” In response to an inability to
establish and define criteria for evaluating creativity that is
acceptable to all domains, Amabile (1982, 1996) introduced
a Consensual Assessment Technique (CAT) in
which creativity is assessed by a group of judges that are
knowledgeable of the field. Since then, several scales for
assisting human evaluators have been developed to guide
human evaluators, for example, Besemer and O'Quin's
(1999) Creative Product Semantic Scale, Reis and
Renzulli's (1991) Student Product Assessment Form, and
Cropley et al’s (2011) Creative Solution Diagnosis Scale.
Maher (2010) presents an AI approach to evaluating
creativity of a product by measuring novelty, value and
surprise that provides a formal model for evaluating creative
products. Novelty is a measure of how different the
product is from existing products and is measured as a distance
from clusters of other products in a conceptual space,
characterizing the artifact as similar but different. Value is
a measure of how the creative product compares to other
products in its class in utility, performance, or attractiveness.
The measure of value uses clustering algorithms and
distance measures operating on the value attributes of existing
products. Surprise has to do with how we develop
expectations for the next new idea. This is distinguished
from novelty because it is based on tracking the progression
of one or more attributes, and changing the expected
next difference.
Computational creativity can be described by identifying
the generative processes that are associated with being
creative and how the process changes the conceptual space.
Alternatively, computational creativity can be asserted
when the product is recognized as creative, independently
of the process. However, computational creativity is more
complicated than a single process that generates a selfcontained
product, partly due to the different roles that
people and computers play in computational creativity but
also due to recent phenomena of scaling up participation to
achieve collective human-computer creativity.
International Conference on Computational Creativity 2012 68
Collective Creativity
Collective creativity is associated with two or more people
contributing to a creative process. Using the internet to develop
and encourage creative communities has led to large
scale collective creativity. Some examples of such creative
communities are: Designcrowd.com, Quirky.com,
99Designs.com and OpeningDesign.com. Designcrowd and
99Designs are examples of websites that source creative
work from a very large community of people that identify
themselves as designers. Quirky crowdsources innovative
product development, where the community works together
with an in-house design team to design products
from idea to market. OpeningDesign is a platform for architecture
and urban planning, encouraging people from
different backgrounds to participate in projects and providing
a space for opinion polls and crowdsourcing jobs.
These platforms rely on community participation, both
amateur and professional, and their websites support community
discussion and various amounts of involvement.
They attract a range of contributions, from the casual observer
who might be motivated to comment once or twice,
to the active contributor who closely tracks progress, contributes
new ideas, and responds often and with minimal
delay. Maher, Paulini, and Murty (2010) show how the nature
of the contributions and collaboration can be considered
along a spectrum of approaches, ranging from collected
intelligence to collective intelligence. DesignCrowd
collects individual designs and is an example of collected
intelligence and Quirky is an example of collective intelligence
in design by encouraging collaboration and voting.
Large scale participation from individuals that may or
may not have expertise in the class of products being designed
or created can synthesis ideas that go beyond the
capability of a single person or a more carefully constructed
team. Page (2007) describes how diverse individuals
bring different perspectives and heuristics to problem
solving, and shows how that diversity can result in better
solutions than those produced by a group of like-minded
individuals. Hong and Page (2004) prove a theorem that
“Diversity Trumps Ability” every time. Page (2007) argues
that diversity improves problem solving, even though our
individual experiences in working with a diverse group
may be associated with the difficulty of understanding
other viewpoints and reaching consensus. Many of the successful
examples of collective creativity encourage diversity
but do not require that everyone understand others’
perspectives or even necessarily to reach concensus.
A recent study of communication in Quirky.com shows
how the crowd contributes to ideation and evaluation as
part of a larger design process (Paulini, Maher, and Murty,
2011). Their analysis shows that a design process that includes
crowdsourcing shares processes of ideation and
evaluation with individual and team design, and also includes
a significant amount of social networking. Collective
creativity is an emergent property of an online community
where team design is structured and managed intentionally
to produce an innovative product.
Who is being creative?
Creativity can be the result of introducing a novel and surprising
idea and developing that idea into a product that is
valuable in the context of an existing conceptual class of
products. A creative idea can originate by bringing a different
perspective or set of heuristics (as described by Page
(2007)) to a conceptual class or existing patterns of design.
This diversity can be achieved through social, computational
and collective creativity. The various processes as
described by Boden and Gero show how different algorithms
or heuristics result in creative ideas. The various
approaches to evaluating creativity show how creative
ideas can be evaluated. The field of computational creativity
now has the basis for developing and evaluating creative
systems, and can benefit from characterizing individual
contributions to the field. By asking “Who is being
creative?” we can identify where the focus of computational
creativity is now, and where there are research opportunities.
Did the computer generate the creative idea or
did a person, or was it an emergent structure from the interaction
of people and computational systems? In this section
we structure a framework around the concept of human/computer
ideation and interaction, and map individual
contributions onto a space of possibilities. The contributions
include a sample of computational creativity drawing
on the proceedings of ICCC 2011 (Ventura et al, 2011) and
ICCCX (Ventura et al, 2010), including Quirky
(quirky.com) and Scratch (Maloney et al, 2010) to fill gaps
in ICCC coverage.
Ideation
Ideation is a process of generating, synthesizing, evaluating,
implementing ideas that lead to a potentially creative
product or solution. Ideation is a creative process and an
idea is a product of that process. Using the term ideation to
characterize computational creativity provides a basis for
analyzing human, computational, and collective creativity
with respect to the origin of a creative idea. While it may
be hard to track precisely where an idea comes from in a
complex creative process, we can identify where in a human-computer
collective there is potential for creative
ideas to be expressed and evaluated. Figure 1 places systems
that contribute to computational creativity within a
space according to the origin of the creative idea as human
or computational agent. The “human” and “computational
agent” dimensions of this space characterize the role of
each in the computational creativity system.
International Conference on Computational Creativity 2012 69
Along the human dimension the framework includes two
categories that describe the role of the human in computational
creativity: model or generate.
• Model: the role of the human is developing a computational
model or process. The computational system is effectively
being creative because it is the source of the
creative ideas or artifact. For example, The Painting Fool
is a computational system that generates artistic paintings
(Colton, 2011).
• Generate: the human generates the creative idea and the
computational system facilitates or enhances human
creativity by providing information, by providing a digital
environment for generating the creative artifact,
and/or by providing a perceptual interface to digital content
that influences creative cognition. For example,
Scratch, is a computational system for people to create
interactive stories, animations, games, music, and art
(Maloney et al, 2010).
Along the computational dimension the framework includes
three categories that describe the role of the computational
system: support, enhance, generate.
• Support: the computational system supports human
creativity by providing tools and techniques. Scratch is
an example of a creativity support tool.
• Enhance: the computational system extends the ability
of the person to be creative by providing knowledge or
changing human perception in ways that encourage creative
cognition. For example, Scuddle uses a genetic algorithm
to generate movement catalysts for dancers (Carlson
et al, 2011).
• Generate: the computational system generates creative
ideas that the human then interprets, evaluates or integrates
as a creative product. The Painting Fool is a computational
system that generates creative paintings.
Figure 1 shows a distribution of computational systems in
which the human generates the creative ideas, aka creativity
support tools, and the computational system generates
creative ideas. The contributions in the space that is empty
in Figure 1 includes theoretical contributions rather than
the development of computational systems, for example
the contributions to models of process that generate creative
products and models for evaluating creative products.
Interaction
Interaction plays an important role in computational creativity,
particularly interaction between computers and humans
(as the generators or users of the computational system).
Traditionally, human-computer interaction has been a
one-to-one interaction in which one person interacts with
one computational device or environment. Recently, interaction
has changed in scale. Figure 2 places the same systems
from Figure 1 within a space that characterizes the interaction
between people and computers where the dimensions
of this space express scale: from a single human or
computational system to many.
Figure 1. Ideation and Computational Creativity
Along the human dimension there are three categories
that describe the scale of the human interaction: individual,
group, or public.
• Individual: the computational system is developed to
support one person working alone, for example Scuddle.
• Group: the computational system supports a group or a
predefined team of people. This is exemplified by the
collaborative technologies that support design and drawing
such as Groupboard1. This area is not well represented
in the ICCC series.
• Society: the computational system encourages crowdsourcing
and collective intelligence, for example Quirky.
Along the computational agent dimension there are three
categories that describe the scale of the computational
agent interaction: individual, team, or multi-agent society.
• Individual: there is one computational system with a
centralized control that is interacting with a person or
people, for example The Painting Fool.
• Team: there are multiple, centrally organized agents that
interact with one or more people. For example, Curious
Whispers is a collection of autonomous mobile robots
that communicate through simple songs (Saunders et al,
2010).
• Multi-agent society: the computational system is a multiagent
society with distributed control. For example, the
designer agents and consumer agents in Gomez de Silva
Garza and Gero (2010). This area is not well represented
in the ICCC series.
From Figure 2 we see that the contributions in the ICCC
series focus on the interaction is between one person and
one computational system.
 1 http://www.groupboard.com/products/
International Conference on Computational Creativity 2012 70
Figure 2. Interaction and Computational Creativity
Conclusions
As we develop a better understanding of processes and
products in creative people or systems, we are able to develop
more capable computational creativity. Ideation and
interaction distinguish research in computational creativity
by asking: Who is being creative? The word “who” is used
to refer to one or more people or computational systems.
When creativity is ascribed to the plural “who”, that is
when the ideas come from multiple sources, there is an assumption
of interaction. An area of research in computational
creativity that has received little attention is the role
and scale of interaction. Interaction at the scale of one person
and one computational system has been the norm in
computational creativity, with a recent trend in developing
collaborative environments to support or enhance creativity,
multi-agent models of creativity and online communities
that achieve collective creativity. This paper shows
that there is an opportunity for researchers in computational
creativity to build on our theoretical and practical
advances in understanding creative processes and the
evaluation of creative products to address the concepts of
interaction and scale.
<references_biblio/>
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