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ARTIFICIALCREATIVITY:
ASyntheticApproachtotheStudyofCreativeBehaviour
ROBSAUNDERS,JOHNS.GERO
TheKeyCentreofDesignComputingandCognition
DepartmentofArchitecturalandDesignScience
UniversityofSydney,NSW,2006,Australia
e-mail:{rob,john}@arch.usyd.edu.au
Abstract.Wepresentanovelapproachtothecomputationalstudyofcreativity, called Artificial Creativity. Artificial Creativity promotesthe study of the creative behaviour of individuals and societies inartificialsocietiesofagents.Itissimilartotheapproachtothattakenby Artificial Life researchers involved in developing computational
models.We presenta frameworkfor developingArtificialCreativitysystems as an adaptation of Lius dual generate-and-test model ofcreativity. An example implementation of an Artificial Creativitysystem is presented to illustrate the potential benefits of our newapproachasawayofinvestigatingtheemergentnatureofcreativityinsocieties of communicating agents. Finally, we discuss some futureresearch directions that are possible by extending the abilities ofindividualsandstudyingtheemergentbehaviourofsocieties.
1.Introduction
The aim of Artificial Creativity is to gain a better understanding of
creativity-as-it-isinthecontextofcreativity-as-it-could-be.Inotherwords,
itisthestudyofcreativityas foundinhumansocietieswithcreativityas it
maybefoundinartificialsocietiesofagentsthatmayfollowquitedifferent
socialconventions.Inthisway,thestudyofArtificialCreativityissimilarto
thestudyArtificialLife; both aresynthetic approachesto understanding a
complex, and ill-defined behavioural phenomenon, i.e. creativity and life
respectively.
The Artificial Creativity approach provides an opportunity for
researchers to study the emergence of creative behaviour in controllable
environments,affordinganumberofpossiblestudiesnotpossibleinthereal
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2 A.AUTHOR,B.AUTHORANDC.AUTHOR
world. The parameters that control the behaviour of individuals can be
experimentedwithto study theaffectthat they haveon theemergence of
socialstructures.Theenvironmentthatthesocietyofagentsissituatedin
can be adjusted to study the affects that external factors have on the
creativityofindividualsandsocieties.
As with Artificial Life, one of the most interesting possibilities of
Artificial Creativity is to be able to re-run history with different starting
conditions to find out how products of creative individuals and the
structures of creative societies might have differed. For example, by re-
running an Artificial Creativity simulation with different communicationpolicies we can simulate the affect that different communication
technologiesmighthaveonthe developmentanddisseminationofcreative
ideas.
Artificial Creativity is compatible with previous approaches that have
developedcomputationalmodelsofcreativethinkingbyallowingthemtobe
deployed within the context of artificial societies as long as they can be
embedded within agents that conform to the requirements of Artificial
Creativity.The study ofthebehaviourof creativethinkingwithinartificial
societiesprovidestheopportunitytodevelopabetterunderstandingofthe
situatednessofcreativeprocesseswithinsocio-culturalsituations.AsSimon
(1981)notes,muchofthecomplexityof humanbehaviourmaycomefrom
thecomplexnatureoftheenvironmentthattheyinteractwith.
2.Creativity
Theneedtodefinethenatureofcreativityhashauntedattemptstodevelop
theoriesoftheprocessesinvolvedincreativethinking.Thedifficultyofthis
task is apparent from the number of definitions that can be foundin the
literature:Taylor(1988),forexample,givessome50definitions.Expressed
in the definitions of creativity are some widely different opinions about
what it means for a person to be creative. From reading the literature, it
seemsthatnoagreementmaybereachedondetailsofthecreativeprocess;
however,thedefinitionsprovidedcanbedividedintotwobroadcategories.
Firstly,therearedefinitionsofcreativitythatemphasisecreativethinking
and promote the view that creativity can be studied solely as a mental
phenomenon.Thesedefinitionshavebeena popularin variousapproaches
tostudyingcreativitythatdealwithindividuals,forexample,inpsychology,
cognitive science and artificial intelligence. The models of creativity
proposedbyKoestler(1964),Newelletal.(1962),andHofstadter(1979)go
intogreatdetailaboutthecognitiveprocessesinvolvedincreativethinking,
particularly theprocessesinvolvedinthegenerationofpotentiallycreative
ideas. Many of the computational models of creativity are either based
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PAPERTITLE 3
directlyonthesemodels(e.g.Langleyet al.,1987;Hofstadteret al.,1995)
orarebasedonsimilarmodelsofcreativethinkingfrompsychology(e.g.
PartridgeandRowe,1994).
Definitionsof creativityinthe secondcategoryrecognisethatcreativity
goesbeyondthegenerationofnovelideasandthatsociety,astheaudience
of the creative individual, plays an important role in defining what is
creative.Creativityisthereforedefinedwithastronghonorificsensethatis
as much the result of an audiences appreciation of a work as it is the
creatorsproduction.Proponentsofthesedefinitionscontendthatcreativity
cannotoccurinavacuumandmustbestudiedinthecontextofthesocio-culturalenvironmentofthecreator(Csikszentmihalyi,1999).Thisdefinition
hasbeenpopularinfieldsthatconsiderthecreativityofmultipleindividuals
over extended periods of time, for example, in history, sociology and
anthropology(e.g.Martindale,1990).
Some researchers have attempted to combine these two views of
creativity into unified theoretical frameworks. However, the resulting
frameworks often maintain the distinction between personal and socio-
cultural notions of creativity, as in Bodens P-creativity and H-creativity
(Boden,1990)andGardnerssmall-candbig-ccreativity(Gardner,1993).
2.1.ASYSTEMSVIEWOFCREATIVITY
WhenCsikszentmihalyidevelopedhissystemsviewofcreativity,heturned
his attention away from the question What is creativity? and focussed
upontheissuessurroundingthequestionWhereiscreativity?Importantly,
Csikszentmihalyi questioned the mentalistic assumption that creative
processesareonlytobefoundinthemindofthecreativeindividual.Instead
heproposedthatprocessesessentialtocreativity,whetherpersonalorsocio-
culturallydefined, are to be found in theinteractions betweenindividuals
andthesocietythattheyaresituatedwithin.
Thesystemsviewof creativitywasdevelopedbyCsikszentmihalyias a
model of the dynamic behaviour of creative systems that include
interactions between the major components of a creative society
(Csikszentmihalyi, 1988). Csikszentmihalyi identified three importantcomponents of a creative system; firstly there is the individual, secondly
thereisacultural,orsymbolic,componentcalledthedomain,andthirdly
thereis a social, orinteractive,component called the field.A map ofthe
systemsviewofcreativityispresentedinFigure1.
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4 A.AUTHOR,B.AUTHORANDC.AUTHOR
SOCIETY
Field Individual
CULTURE
Domain
PERSONAL
BACKGROUND
Stimulates
Novelty
ProducesNovelty
Transmits
Information
Selects
Novelty
Figure1:Csikszentmihalyissystemsviewofcreativity(afterCsikszentmihalyi,1999).An individuals role in the systems view is to bring about some
transformation ofthe knowledge heldin the domain. The fieldis a setof
social institutions thatselects from thevariations producedby individuals
thosethatareworthpreserving.Thedomainisarepositoryofknowledgeheldbytheculturethatpreservesideasorformsselectedbythefield.
Inatypicalcycle,anindividualtakessomeinformationprovidedbythe
culture and transforms it, if the transformation is deemed valuable by
society,itwillbeincludedinthedomainofknowledgeheldbytheculture,
thusprovidinganewstartingpointforthenextcycleoftransformationand
evaluation.In Csikszentmihalyisview,creativityis nottobe foundinany
oneoftheseelements,butintheinteractionsbetweenthem.
2.1.1.LiusDualGenerate-and-TestModelofCreativity
Recognisingtheneedforaunifiedmodelofcreativityindesigncomputing,
Liu(2000)presentedasynthesisofthepersonalandsocio-culturalviewsof
creativityinasinglemodel.Liurealisedthattheexistingmodelsofpersonalcreativity complemented the socio-cultural models by providing details
abouttheinnerworkingsofthecreativeindividualmissingfromthemodels
ofthelargercreativesystem.
Liuproposeda dualgenerate-and-testmodelofcreativityasasynthesis
ofSimonetalsmodelofcreativethinkingandCsikszentmihalyissystems
view. Asits name suggests, thedual generate-and-test model of creativity
encapsulatestwo generate-and-testloops:oneat theleveloftheindividual
and the other at the level of society. The generate-and-test loop at the
individual level, illustrated in Figure 2(a), provides a model of creative
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PAPERTITLE 5
thinking, incorporatingproblemfinding, solutiongeneration andcreativity
evaluation.Thesocio-culturalgenerate-and-testloopmodelstheinteractions
among the elements of Csikszentmihalyis systems view of creativity, as
illustrated in Figure 2(b). In particular, it captures the role that the field
playsasasocio-culturalcreativitytest.Thecombineddualgenerate-and-test
modelofcreativityisillustratedinFigure2(c).
GenerateProblem
Finding
no
yes
yes
no
Field
Individual
Domain
Personalcreativesolution
generationandrecognition
Social-culturalrecognitionofcreativeideasbyagroupofauthorisedpeople
Sourceof
initialdataand
knowledgein
thedomain
Creativity
Test
GenerateProblem
Finding
no
Individual
Personalgenerate-and-test
Test
Domain
Knowledge
Creative
Product
yes
no
yesField
Individual
Domain
Socio-culturaltest
Problems&
SolutionsSocio-culturalgenerate
(a)
(b)
(c)
Figure2:Liu'sDualGenerate-and-TestModelofCreativeDesign:(a)thepersonalgenerate-and-testmodel,(b)thesocio-culturalgenerate-and-testmodel,(c)thecombineddual
generate-and-testmodel.
Lius model unifies Simon et als and Csikszentmihalyis models of
creativity to form a computational model of creativity that shows how
personalandsocio-culturalviewsofcreativitycanbemodelledinasingle
system.ComparedtoBodensmodelofcreativity,thedualgenerate-and-test
model of creativity models both the P-creativity and H-creativity of
individuals using the generate-and-test loops at different levels.Using the
languageofGardnerwe may say thatwhatdistinguishessmall-c creativity
frombig-ccreativityisthatbig-ccreativityaffectschangestothedomain
whereassmall-ccreativitydoesnot.Lius dual generate-and-test model shows that it is possible to cast
Csikszentmihalyis systems model in computational terms and thereby
providesuswithausefulbasisforaframeworkfordevelopingmodelsof
Artificial Creativity. Before developing Lius model further, we will
examinetherequirementsofacomputationalmodelofArtificialCreativity.
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6 A.AUTHOR,B.AUTHORANDC.AUTHOR
3.ArtificialCreativity
The Artificial Creativity approach that we propose here is based on
Langtons approach todevelopingcomputational modelsof ArtificialLife
(Langton, 1989). The essential requirements of a computational model of
ArtificialCreativityare:
Themodelcontainsasocietyofagentssituatedinaculturalenvironment.
Thereisnoagentthatcandirectthebehaviourofalloftheotheragents.
Therearenorulesthatdictateglobalbehaviour.
Agentsinteractwithotheragentstoexchangeartefactsandevaluations.
Agentsinteractwiththeenvironmenttoaccessculturalsymbols.
Agentsevaluatethecreativityofartefactsandotheragents.
Many of the requirements of a computational model of Artificial
Creativity are similar to the requirements of a computational model of
Artificial Life. Although some of the details are different, both types of
modelsconsistofa populationofagents,andbothrequirethatthereareno
rulesoragentsthatcandictateglobalbehaviour.
AnadditionalrequirementofArtificialCreativityagentsnotfoundinthe
requirementsofArtificialLifeisthattheagentsinanArtificialCreativitymodelmustbeabletomakeindependentvaluejudgementsandadapttheir
behaviour accordingly.Morespecifically,agentsinan ArtificialCreativity
systemmustbe abletomakeevaluativejudgementsaboutthe creativityof
agentsandproductsinordertoimplementthepersonalandsocio-cultural
creativitytestsfoundinLiusmodel.
Toillustratetheapproach,considerhowonewouldmodelasocietyof
artists.First,wewoulddefinearepertoireofbehavioursfordifferentartistic
agentsandcreatelotsoftheseagents.Wewouldthenstartasimulationrun
by specifying some initial social configuration of the agents within a
simulatedculturalenvironment.Fromthispoint onwardsthe behaviour of
the system would depend entirely on the interactions between different
agents and the interactions between the agents and their culturalenvironment.Importantly,therewouldbenosingleagentthatcouldenforce
a definition of creativity by controlling the behaviour of all of the other
agents. In addition, there would be no rules in the agents or in the
environmentthatwoulddefineaglobaldefinitionofcreativity.Thenotions
ofwhomandwhatarecreativeheldbythesocietywouldemergefromthe
multiplenotionsofcreativityheldbytheindividualagents.
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PAPERTITLE 7
3.1. THEIMPORTANCEOFEMERGENCE
The requirements of Artificial Creativity are designed to model the
emergenceofphenomenainsocietiesofagentsconsistentwithcreativityin
human society. Emergence is an important feature of Artificial Creativity
systems,wherephenomenaatacertainlevelarisefrominteractionsatlower
levels.
Inphysicalsystems,temperatureandpressureareexamplesofemergent
phenomena. Temperature and pressure are emergent properties of large
ensemblesof molecules andare dueto interactionsat themolecularlevel.Anindividualmoleculepossessesneithertemperaturenorpressure;theyare
propertiesthatonlyemergewhenmany moleculesarebrought together.In
Artificial Life, the stable patterns in cellular automata, and the flocking
behaviourofsimulatedbirdsareexamplesofemergentphenomena.
InArtificialCreativity,thesocio-culturalevaluationsofwhomandwhat
are creative are emergent phenomena; no individual can dictate the
collectiveevaluationsofwhomandwhatarecreative,theycanonlytryto
influence other individuals by exposing them to their products and their
personal evaluations. The emergence of macro-level creativity from the
interactionsofindividualsatthemicro-levelisillustratedinFigure3.
Figure3:Abehaviour-basedapproachtothestudyemergenceofcreativebehaviouratthe
levelofsocietybymodellingthebehaviourofindividuals(afterLangton,1989).
In Bodens terms we might be tempted to say that H-creativity is
emergent whereas P-creativityis notbecausetheprocessesthatimplementP-creativity test are fixed. However, in the Artificial Creativity system
describedlatertheinteractionbetweenagentsandthecontinuallearningof
the agents through exposure to new artefacts mean that what an agent
considerstobeP-creativeisanemergentpropertyofthewholesystem.An
individualembeddedwithinanArtificialCreativitysystemisaffectedbyits
socio-cultural context such that it will not produce the same P-creative
productsas itwouldin isolation.Hence,bothH-creativityandP-creativity
mustbeconsideredemergentpropertiesofcreativesystems.
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8 A.AUTHOR,B.AUTHORANDC.AUTHOR
4. AFrameworkforArtificialCreativity
Thissectionpresentsaframeworkfordevelopingcomputationalmodelsof
Artificial Creativity. The framework is presented by adapting Lius dual
generate-and-test model to meet the requirements of Artificial Creativity
listedabove.
4.1. ADAPTINGLIUSMODELTOARTIFICIALCREATIVITY
A critical aspect of Lius model that must be addressed to developcomputational models ofartificial creativityis the definition of thesocio-
cultural creativity test. A literal implementation of Lius model would
produce a separate process that would model the socio-cultural creativity
test.This is a viable solutionformodelling some aspects ofcreativity,as
demonstratedbythecomputationalmodeldevelopedbyGaboratostudythe
memeticspreadof innovationsthrough asimulatedcultureGabora(1997).
Colton (2000) applied a similar socio-culturalcreativity test to assess the
increaseincreativityduetotheco-operationofagentssearchingaspaceof
mathematical possibilities using different search heuristics. However,
implementing a single function, or agent, that model a socio-cultural
creativitytestwouldviolateoneoftherequirementsforArtificialCreativity
outlinedpreviously,i.e.thatnoruleoragentshoulddirectglobalbehaviour.Liudoesnotgointodetailsaboutthedefinitionofthisfunctionbutit
appears that he considers this function to be outside the scope of
computationalmodelsandsomethingthatcanonlybeimplementedbysome
form of interaction with human society. Many computational models
developed reinforce this view by concentrating on the constrained
generationofnovelideasintheircomputationalmodelsandrelyingonusers
toevaluatethecreativeworthofideas.Forexample,seeClancey(1997)for
adiscussionofthesocialsituatednessofHaroldCohensAARON.
To computationally model the behaviour of creative societies, it is
necessary to define a socio-cultural creativity test without violating the
requirementsofArtificialCreativity.Thekeytosolvingthisproblemisto
realisethatthepersonalcreativitytestinsideeachindividualcanbeusedto
developasocio-culturaltestforcreativity.Thesocio-culturalcreativitytest
can be modelled by permitting the communication of artefacts and
evaluations of personal creativity between individuals. An illustration of
twoindividualscommunicatingcreativityevaluationsisillustratedinFigure
4.
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PAPERTITLE 9
IndividualB
IndividualA
Problem
Finding
Creativity
TestGenerate
Creativity
TestGenerateProblem
Finding
no
no
yes
To
Domainyes
yes
Figure4:Thecommunicationofevaluationsbetweenindividualsanditsintegrationintothe
individualgenerate-and-testcycle.
In the interaction illustrated in Figure 4, Agent A communicates an
artefactthatitconsiderstobecreative,i.e.thatpassesitpersonalcreativity
test, to Agent B. Agent B evaluates the artefact according to its own
personal creativity test and sendsits evaluation back to Agent A. In thisway,Agent Bcan affectthe generationof future artefactsbyAgentA by
rewardingAgentAwhenitgeneratesartefactsthatAgentBconsiderstobe
creative. More subtly, Agent A can affect the personal creativity test of
AgentBbyexposingittoartefactsthatAgentAconsiderstobecreative,
becausetheevaluationofcreativityinvolvesanevaluationofnovelty,Agent
AaffectsachangeinAgentBsnotionofcreativitybyreducingthenovelty
of the type of artefacts that it communicates. By exposing Agent B to
artefactsthatAgentAconsiderstobecreative,becausetheyarenoveland
yetunderstandable,itcanaltertheevaluationofcreativitymadebyAgent
B.
Agent-centric evaluations of creativity permit the emergence of socio-
cultural definitions of creativity as the collective function of many
individualevaluations.Withoutagent-centricevaluationsofinterestingness
thecollectionofagentswouldsimplyrepresentparallelsearchesofthesame
designspace.Toimplementthesocio-culturalcreativitytestasacollective
functionofindividualcreativitytestsa communicationpolicyisneeded.A
simplecommunicationpolicywouldbeforagentstocommunicateaproduct
whentheirevaluationofthatproductisgreaterthansomefixedthreshold.
More complex communication policies might incorporate more strategic
knowledgeaboutwhentocommunicateandwhotocommunicatewith.
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10 A.AUTHOR,B.AUTHORANDC.AUTHOR
To complete the implementation of the field as a collection of
individuals, the individuals must be given the ability to interact with the
domain according to some domain interaction policy. A simple domain
interactionpolicywouldfollowthecommunicationpolicyaboveandallow
agents to addproductsof thegenerative process if thepersonalcreativity
evaluationisgreaterthanadomaininteractionthreshold.Thisapproachis
illustratedinFigure4.However,toensuresomelevelofsocialagreement
before the addition of products to the domain, a slightly more complex
domain interaction policy ensures thatno individual is allowed to submit
theirownworktothedomain.Thus,atleastoneotheragentmustfindanindividualsworkcreativebeforeitisenteredintothedomain.
Makingtheseamendmentsto Lius dualgenerate-and-testresultsin the
modelofsocio-culturalcreativityillustratedinFigure5.
Field Domain
Individual
Individual Individual
Individual
Individual
Individual
Individual Individual
Figure5:TheArtificialCreativitymodelofsocio-culturalcreativity.
5. TheDigitalClockworkMuseProject
In The Clockwork Muse Martindale (1990) presented an extensive
investigation into the role that an individuals search for noveltyplays inliterature,music,visualartsandarchitecture.He concludedthatthesearch
fornoveltyexertsasignificantforceonthedevelopmentofstyles.
Martindale illustrated the influence of the search for novelty by
individuals in a thought experiment where he introduced The Law of
Novelty.TheLawofNoveltyforbidstherepetitionofwordordeedand
punishesoffendersbyostracisingthem.MartindalearguedthatTheLawof
Noveltywasmerelyamagnificationoftherealityincreativefields.
Someof theconsequencesof thesearchfornoveltyare thatindividuals
thatdonotinnovateappropriatelywillbeignoredinthelongrunandthat
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PAPERTITLE 11
the complexity of any one style will increase over time to support the
increasing need for novelty. In this section, we present a computational
model of the Law of Novelty developed using our Artificial Creativity
approachusingcuriousdesignagentsthatsearchfornovelty(Saunders&
Gero,2001b).
Ourmodelconsistsof multiplecuriousdesign agentswithina single
field conducting searches for interesting and potentially creative genetic
artworks.Eachagentisequippedwithanevolutionaryartsystemtoallow
itto generategeneticartworksandcancommunicatewithoneotheragent,
chosenatrandom,oneachtimestep.Individualsthatproduceartworksthatare considered creative by other agents are rewarded with creativity
credit.
5.1. THEINDIVIDUAL:ACURIOUSDESIGNAGENT
This subsection describes the important components of a curious design
agent and the interactive evolutionary system that it interacts with. The
agentsintheDigitalClockworkMuseProjecthavebeendevelopedusinga
model of curiosity that wehaveappliedto several domainscan befound
elsewhere (Gero and Saunders, 2000; Saunders and Gero, 2001a; 2001b;
2001c).Themodelofcuriosityprovidestheessentialabilityforagentsto
evaluate thecreativity of artefacts andtakeappropriate action, i.e.evolvenew artefacts, communicate with otherindividuals in the field,or add an
artefacttothedomain.
5.1.1. InteractiveEvolution
Every agent in The Digital Clockwork Muse uses an interactive
evolutionaryartsystem,similartotheonesdevisedbyDawkins,Sims,Todd
and Latham, and others (Dawkins, 1987; Sims, 1991; Todd and Latham,
1992) to generate genetic artworks. Interactive evolutionary art systems
useastandardevolutionarysystem,e.g.ageneticalgorithm,toevolvesmall
populationsofartworksthatarepresentedtoahumanuserforevaluation.In
our system, agents take the place of human users and interact with the
evolutionaryartsystemstosearchfornovelgeneticartworks.Theflowofinformationbetweenanagentanditsevolutionaryartsystemisillustratedin
Figure6.
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12 A.AUTHOR,B.AUTHORANDC.AUTHOR
IndividualB
Creativity
TestGenerateProblem
Finding
no yes
img img img
EvolutionarySystem
img img img
img img img
SelectsInteresting
Images
EvolvesPopulation
ofImages
Figure6:Acuriousdesignagentandaninteractiveevolutionaryartsystem.
5.1.2. GeneticArtworksKarlSimsisbestknownforhisworkdevelopingoneofthefirstinteractive
evolutionaryartsystemsforcomplextwo-dimensionalbitmapimages(Sims,
1991). Using a process similar to Genetic Programming, Sims devised an
evolutionary art system that produced artworks by evolving symbolic
functiontrees.
An example genetic artwork of the type evolved by the agents in this
project is shown in Figure 3. This genetic artwork was evolved over the
InternetaspartoftheInternationalInteractiveGeneticArt(IIGA)project
(WitbrockandReilly,1999). Theevolutionarysystemsusedinthisproject
weredevelopedusingsourcecodefromtheIIGAproject.
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PAPERTITLE 13
Figure3:Anexampleofageneticartworkinteractivelyevolvedbyahumanuser.(Fromthe
archiveofevolvedgeneticartworksinInteractiveGeneticArtIII.)
5.1.3. Sensing
A 32x32-pixel image of each genetic artwork is analysed by a curiousdesign agent to determine its novelty. Although this is a low-resolution
imageitisstilllargeenoughtoallowcomplexartworkstobeevolved.To
sense the image, a relatively simple combination of a Laplacian edge-
detectorandafixedintensitythresholdfunctionwereusedtotransforma
geneticartworkintoabinaryimage,asshowninFigure7.
(a) (b)
Figure7:Theimageprocessingappliedtogeneticartworkstoextracttheedgestructureof
theimages,(a)theoriginalimage,and(b)thebinaryimageproducedbytheimageprocessing
tofindthemostprominentedges.
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14 A.AUTHOR,B.AUTHORANDC.AUTHOR
5.1.4. Novelty
Each agent is equipped with a neural network to learn the categories of
images as it explores the space of possible genetic artworks. A self-
organising map, or SOM, (Kohonen, 1995) is used to categorise each
artworkthatanagentencountersintoacategoryrepresentedbyoneofthe
networksneurons.Ateachpresentationofanartworktheprocessedbinary
image is converted into a vector consisting of 1024 values. As an agent
exploresthespaceofpossibilitiesitlearnsamapoftypicalartworksforthe
region of the genetic art space it currently occupies. By comparing new
artworks against this map, the agent can detect novel, and potentiallyinteresting,artworks.
Themapthattheneuralnetworkproducesprovidesaformofshort-term
memory for the agent to compare new artworks with previously created
ones.Thelargerthenetwork,themoreneuronstheagenthas,andthemore
categoriesofartworksitcanrememberandrecallforcomparison.
Figure 8 shows the neighbourhoods thathave formed for similar input
patterns,e.g.aroundE2andA6,aswellasthemixingofthesepatternsin
theintermediateareas,e.g.aroundD4.Themixingofrepresentationsinthis
wayprovidesanagentwiththeabilitytogeneralisefrompastexperiences
andhencepredictaspectsofunseenartefacts.Thisisanimportantability
forcuriousdesignagentsbecauseitallowsthemtodeterminethenoveltyof
newartefactswithoutsamplingallofthedesignspace.
A B C D E F
1
2
3
4
5
6
Figure8:Theprototypesrepresentedbythe36neuronsofaself-organisingmaphavingjust
categorisedtheinputshowninFigure4batlocationE2.
Novelty(N)iscalculatedasthecategorisationerrorofanagentsSOM
asitattemptstoidentifyasuitablecategoryforanartwork.Noveltyvalues,
i.e.thevaluesofoutputbythebestmatchingneuronoftheneuralnetwork
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PAPERTITLE 15
dependonthesizeof image,inthiscasethesevaluesareintherangeN=0
and N=32, with N=0 being an exact match and N=32 being a complete
mismatch. Effectively this measures the distance of the closest category
prototypetotheinputpattern.
TheEuclideandistancebetweentheclosestcategoryprototypeandanew
inputpatternisarathercrudemeasureofnovelty,andmoresophisticated
measureshavebeendevelopedbyseveralresearchersincludingtheauthors
(Kohonen, 1993; Marsland et al., 2000; Saunders and Gero, 2001c),
however, for the purposes of this demonstration system the measure of
novelty provided by the categorisation error is sufficient andcomputationallyinexpensive.
Novelty is used as the sole criterion to evaluate evolved artworks for
interestingness.Assuchwedefinetheinterestingnessofanartworkbased
on the degree to which it could not have been predicted from previous
experience. Thisis similar to BodensnotionofP-novelty(Boden,1990).
Ourdefinition of interestingnessbasedonnoveltyalonelackstheexplicit
requirement for usefulness needed to model P-creativity as defined by
Boden but,we argue that because interesting artworks are actionable,i.e.
theypromotecuriousaction,theusefulnessofanartworkisitspotentialto
leadtootherinterestingartworksandistherefore,withintheconfinesofthis
simplesystem,relatedtoitsnovelty.
5.1.5. Interestingness
Interest in an artwork is calculated using an approximationto the Wundt
curve, a well-known arousal response curve developed from studies of
animals and humans to exposed to arousal producing stimuli, including
novelty(Berlyne,1971).TheWundtcurveissketchedinFigure9.Berlyne
(1971) refers to the Wundt curve as a hedonic function, to indicate its
relationship to the pleasure/pain response that is often associated with
arousingstimuli.
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16 A.AUTHOR,B.AUTHORANDC.AUTHOR
HEDO
NIC
VALUE
NOVELTYNx
Hx
Reward
Punish
0
1
-1
n1 n2
Figure9.Thehedonicfunctionusedtocalculateinterest.Thehedonicfunctionisshownasa
solidline,therewardandpunishmentcurvesareshowndashed.
In our model the hedonic function is calculated as the sum of two
sigmoidal functions whereas the Wundt curve is calculatedas the sum of
cumulative-Gaussianfunctions. Themost important feature ofthe hedonic
function used in this research that it shares in common with the Wundt
curveis that it is the sum of two non-linear functions. Ineither case the
functionsaresummedtoproduceaninvertedUshapedcurve,assketched
in Figure 9. The sigmoidal function labelled Reward represents the
intrinsicrewardgiventotheagentforfindinganarousal-inducingstimulus
over a fairly lowthreshold, n1.The second function, labelled Punish,is
the amount of punishment that the agent receives for finding an arousal-
inducingstimulusoverahigherthreshold,n2.Byalteringthethresholdsfor
the reward and punishment sigmoid curves this peak can be positioned
anywherealongthenoveltyaxis.
The agents in the Digital Clockwork Muse use the above hedonic
function to calculate the level of interest that they have in a particular
artworkbaseduponthenoveltydetectedbytheself-organisingmap.Figure9illustratestheuseof thehedoniccurvewithanexamplenoveltyvalueNx
thatismappedtoitscorrespondinghedonicvalueHx.
5.1.6. Curiosity
Throughacombinationoftheneuralnetworkandthehedonicfunctionthe
agentsdisplayaformofcuriousbehaviour.Givenasetofnewartworks
an agent will favour those that are imperfectly represented by the self-
organisingmap,indicatingtheneedforsomelearning,butarenotsonovel
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PAPERTITLE 17
as to fall beyond the peak of the hedonic function. Thus the agent is
motivated to choose artworks it has a good chance of improving its
representation of by favouring similar-yet-different artworks at each time
step (Berlyne, 1971). In other words, the agent shows little interest in
artworks that are either too similar or too different to its previous
experiences(Schmidhuber,1991)
Anagentsinterestinanartworkdeterminestheartworksactionability.
If an artwork is the most interesting at a given moment without being
interestingenoughtobeconsideredcreativethentheartworkisselectedas
thestartingpointforfurthersearchbutnotsenttoanyotheragents.
5.2. THEFIELD:ACOMMUNITYOFINTEREST
Todefine a fieldin anArtificial Creativitysystemwe need todefine the
communication mechanisms and policies used by agents to exchange
artworks and evaluations. For this project we have chosen to use the
simplestimplementationspossible.
5.2.1. Communication
Iftheinterestingnessofanartworkbreachesathresholdvaluethatmarksthe
lowerboundoftherangeofpotentiallycreativeartworksthentheartworkis
senttootheragentsforpeerreview.Artworksareexchangedasmessagesthatencodethesymbolicdescriptions
of the artworks. Receiving agents must then express the genetic
representationtorecovertheartworkandthenevaluateit.Havingexpressed
areceivedartworkanagentevaluatesitaccordingto itspersonalcreativity
testbasedonitsownexperiences.Theexperiencesofareceivingagentare
likely to be different than those of the sender and this can lead to very
different evaluationsof thesame artwork.An artwork thatwasinteresting
foritscreatormaybeboringtoasecondagentbecauseitistoofamiliaror
uninterestingtoathirdbecauseitisnotfamiliarenough.
Anagentmayfindareceivedartworkmoreinterestingthanitsowncurrent
artworks,inwhichcaseitcanusethereceivedartworkasthestartingpoint
foranewsearchofthegeneticartspace.An advantage of passing the genetic representations of artworks between
agents,ratherthantheartworksthemselves,isthatifareceivingagentfinds
an artwork interesting it can usethegenetic representationto evolvenew
artworks without having to reverse engineer an artwork first. This is a
computationally efficient approach to distributing artworksbut it removes
thepossibilityofmemeticevolutionofartworksthroughtheintroductionof
errorsduringthe imitationprocess(Dawkins, 1976).Tosafeguard against
plagiarismandtherebystopapopularartworkbeingcopiedbyallmembers
of a population unaltered, an agent is not allowed to pass on a received
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18 A.AUTHOR,B.AUTHORANDC.AUTHOR
artwork as its own in the same cycle; it must perform at least one
evolutionarygenerationfirst.
Before using an artwork received from elsewhere an agent must pay the
creatoroftheinterestingartworksomecredit,proportionaltotheinterestthe
receiving agent has in the artwork. The amount of credit accumulated
throughoutalifetimeisusedtoassesshowcreativeaparticularindividual
is.
5.3. THEDOMAIN:AREPOSITORYFORCREATIVEARTWORKS
Adomainismaintainedbythecollectiveactionsofagentsinitsassociated
field. We have implemented the minimal domain interaction policy that
ensuressomeformofsocialagreementwithinafieldbeforeanartworkcan
beaddedtoadomain.Agentscannotaddtheirownartworkstothedomain;
they can only add artworks that they receive from others. Toqualifyfor
addition to the domain an artwork must be of particularly high
interestingnessforthereceivingagent,mostlikelyhigherthanthatrequired
for an artwork to be considered worthy of communicating to another
memberof isfield.If so, the artworkis addedtothe domainwith alabel
indicatingtheagentthatcreatedit.
Futuregenerationsofgeneticartistsbegintheirsearchwithartworksthat
havebeenaddedtothedomain,however,thedynamicnatureofthesocio-cultural evaluation process means that artworks that were considered
creativearelikelytobe nolongerconsideredcreativebecausetheyaretoo
familiarto thefield.Therefore,thedomaindoesnotprovideinstantaccess
tocreativeworks,butratherastoreoffamiliarstartingpointsfromwhich
newcreativeartworkscanbeproduced.Therealadvantageofstartingwith
artworks stored in the domain is that they are already familiar to other
membersofthefield.Theresultofashortsearchfornovelartworksstarting
withexamplesfromthedomainislikelytobenewartworksthataresimilar-
yet-differentwithrespecttothedomain,makingthemidealcandidatesfor
beingcreative.
ResearchersofArtificialCreativitycanalsousetherecordskeptinthe
domain as a means to trace the development of artistic stylesconsideredcreativeovertime.
6. ExperimentsinArtificialCreativity
The following experiments were conducted with the aim of confirming
Martindales predictionsfor Artificial Creative systemsand to investigate
otherinterestingemergentbehaviour.
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PAPERTITLE 19
6.1. THELAWOFNOVELTY
Weinvestigatedthe effectsof thesearchfor novelty, byproducingagents
withdifferenthedonicfunctions.Theaimwastoshowthatagentsarenot
recognised as creative when they fail to innovateinappropriately. Agents
can innovateinappropriately either byproducing boringimagesthat are
too similar to images previously experienced by other agents, or by
producing radical images that are too different for other agents to
appreciate.
We have simulated both types of inappropriate innovation in a singlesimulation.Forthisexperimentwecreatedagroupofagentsmostofwhom,
agents0-9,sharedthesamehedonicfunction,i.e.thesamepreferencefor
average novelty (N=11). Two of the agents have quite different novelty
preferences. One, agent 10, has a preference for low amounts of novelty
(N=3)andtheother,agent11,hasapreferenceforhighamountsofnovelty
(N=19).Agentswithalowernoveltypreferencetendtoinnovateataslower
rate than those with a higher hedonic preference. The results of the
simulationarepresentedinTable1.
TABLE1.Theattributedcreativityforagroupofagentswithdifferentpreferencesfor
novelty.
Agent
ID
Preferred
Novelty
Attributed
Creativity
0 N=11 5.43
1 N=11 4.49
2 N=11 4.50
3 N=11 3.60
4 N=11 4.48
5 N=11 1.82
6 N=11 6.32
7 N=11 8.93
8 N=11 10.72
9 N=11 5.39
10 N=3 0.0
11 N=19 0.0
Theresultsshowtheagentswiththesamepreferencefornoveltytobe
somewhat creative according to their peers, with an average attributed
creativity of 5.57. However, neither agent 10 nor agent 11 received any
credit for their artworks.Consequently noneof the artworksproducedby
theseagentsweresavedinthedomainforfuturegenerations.Whenthese
agentsexpirednothingremainedinthesystemoftheirefforts.
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20 A.AUTHOR,B.AUTHORANDC.AUTHOR
The results show that while an agent must innovate to be considered
creative, it must do so at a pace that matches other agents to achieve
recognition. The agent with a preference for high levels of novelty and
hence rapid innovation was justas unsuccessful in gainingrecognition as
theagentwithalownoveltythresholdthatinnovatedtooslowly.
6.2. THEEMERGENCEOFCLIQUES
Wehavealsoinvestigatedthebehaviourofgroupsof agentswithdifferent
hedonicfunctions.Todothiswecreatedagroupof10agents,halfofthemhadahedonicfunctionthatfavourednoveltyN=6andtheotherfiveagents
favoured novelty values close to N=15. Figure 9 shows the payments of
creativity credit between the agents in recognition of interesting artworks
sentbytheagents.
2 8 2
21 3 4 5 6 7 8 9
1
2 1 1 3
4 5 2 5
2 23 3
1 16 3 5
3 4 5 1
3 5 1 4
4 3 2 4
1 4 4 4
0
0
1
2
3
4
56
7
8
9
Sender
Rec
eiver
Figure9:Amatrixshowingthetotalnumberofmessagescarryingcreditforbeingcreative
betweentheagentsofthesimulation.
Two areas of frequent communication can be seen in the matrix of
payment messages shown inFigure 9. The agents with the same hedonic
functionfrequentlysendcreditforinterestingartworksamongstthemselves
butrarelysendthemtoagentswithadifferenthedonicfunction.Therearea
largenumberofcreditmessagesbetweenagents0-4andagents5-9,butonly
onepaymentbetweenthetwogroupsagent4creditsagent5forasingle
interestingartwork.
The result of putting collections of agents with different hedonic
functionsinthesamegroupappearstobetheformationofcliques:groups
ofagentsthatcommunicatecreditfrequentlyamongstthemselvesbutrarely
acknowledgethecreativityofagentsoutsidetheclique.Asaconsequenceof
the lack of communication between the groups the style of artworks
producedbythetwocliquesalsoremainsdistinct.
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PAPERTITLE 21
Communicationbetweencliquesisrarebutitisanimportantaspectof
creativesocialbehaviour.Communicationbetweencliquesoccurswhentwo
individuals in the different cliques explore design subspaces that are
perceptuallysimilar.Eachoftheindividualsisthenabletoappreciatethe
others work because they have constructed appropriate perceptual
categories. The transfer of artworksfroma sourceto a destinationclique
will introducenew variablesinto thecreativeprocessesof thedestination
clique,thetwocliquescanthenexploreindifferentdirections,justastwo
individuals do when they share artworks. Cliques can therefore act as
super-artists,exploringadesignspaceasacollectiveandcommunicatinginterestingartworksbetweencliques.
Figure10is ascreenshotoftherunningsimulationthathasformedtwo
cliques. To help visualise the emergent cliques, the distances between
agentsare shortenedforagentsthatcommunicatefrequently.Thedifferent
stylesofthetwogroupscanalsobeseen,withagents0-4producingsmooth
radialimageswithlowafractaldimension(~1.4)andagents5-9producing
fracturedimageswith clearlydefinededgesandahigherfractaldimension
(~1.7).
Figure10:Ascreenshotofthesimulationclearlyshowingthetwocliques.Thesquares
representagents.Theimagesshowthecurrentlyselectedgeneticartworkforeachagent.The
numberaboveeachsquareshowstheagentsattributedcreativity.Thedarklinesbetween
agentsindicatethecommunicationofcredit.
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22 A.AUTHOR,B.AUTHORANDC.AUTHOR
7. FutureResearch
ArtificialCreativitysimulationspermittheinvestigationofmanyinteresting
aspectsofcreativityincomputationalmodels.Thissubsectionwillexplore
someoftheseaspectsandhowcomputationalmodelscanbedevelopedto
gainabetterunderstandingofthem.
7.1. COMPUTATIONALLYSTUDYINGHISTORICALCREATIVITY
One of the most interesting possibilities supported by the currentlyimplemented system is the possibility to study H-creativity in artificial
societies.Runningsimilarsimulationsto thosedocumentedaboveforlong
periodsoftimeprovidesanopportunitytostudytherevisionofcreativity
over many generations. As noted by Boden (1990) the attribution of H-
creativityto individuals andtheir productsis oftenrevisedover time. We
canexpecttofindsimilarrevisionsinArtificialCreativitysimulations:we
would expect to observesimilarre-evaluations,e.g.the recognitionof the
creativityofindividualsbylatergenerationsthatwasnotrecognisedbytheir
peers.
7.2. THESITUATEDNESSOFARTIFICIALCREATIVITY
Thestudyofsituatednesshasbecomeapopulartopicofresearchinrecent
years in design computing (Gero& Reffat, 1997; Gero, 1998) anddesign
cognition(Suwa&Tversky,1997;Suwa,GeroandPurcell,1999).Artificial
Creativityprovidesnewopportunitiestostudysituatednesscomputationally.
Importantly, the computational modelling of the individual, field and
domainina singlesystempermitsthestudyofboththeinteractivenotions
of situatedness, and the cultural notions of situatedness. Interactive
situatedness has been studied computationally before, e.g. in models of
reflection-in-action(GeroandSaunders,2000).
The cultural situatedness of individuals is crucial to design as a
profession concerned with the socio-cultural change, yet it has been
neglectedincomputationalstudiesbecauseofthe difficultiesof situatinga
computational model in real world cultures. Some examples of programsthathaveinteractedwithcreativefieldsintherealworldareprogramsthat
havebeeninvolvedintheinventionofnewproducts,mostnotablediscovery
systemslikeEURISKO(Boden,1990).However,allofthesesystemshave
requiredhumaninterventionwhentomediateinteractionswiththerelevant
fields and domains. Artificial Creativity allows us to study socio-cultural
situatedness as an emergent property of communicating agents that are
individuallysituatedintheirhistoryofexperiences.
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PAPERTITLE 23
7.3. CREATIVEINDIVIDUALS
ArtificialCreativityprovidesuswithopportunitytostudythecharacteristics
ofcreativeindividuals.Severalimportantcharacteristicsof eachindividual
can be identified and adjusted by altering parameters. We have already
investigatedthebehaviourofagentswithdifferenthedonicfunctionsandwe
could extend these studies by adjusting different parameters or using
functions other than the Wundt curve. Other parameters that we could
experiment with include those that control the learning and the sensors,
effectors and interaction policies ofagents.For example,by changingtheparametersthatcontrolthesizeoftheSOMscurrentlyusedintheagentswe
can effectively experiment with individuals with longer and shorter
memories.
7.3.1. TheLifecycleofCreativeIndividuals
Thereissomedebateoverwhethercreativeindividualsdotheirbestwork
whentheyareyoungorwhethertheycontinuetobejustascreativeinlater
life (Simonton). The study of Artificial Creativity provides us with
opportunity to study the lifecycles of creative individuals and the
mechanismsbywhichthecreativepotentialofindividuals,withrespectto
society, increases or decreases as the agent develops. Adjusting the
parameters discussed above we can experiment with agents that havedifferent abilities and observe how their creative potential changes over
theirlifespan.Forexample,byusinganeuralnetworkthatlearnsquickest
whenyoungwecanstudyhowthecreativityofanagentisaffectedasthe
abilitytolearnistradedoffagainsttheknowledgegained.
7.3.2. TheEvolutionofCreativeIndividuals
18th Century theories of creativity suggested that it was an inherited
characteristic based on the observation that creative ability often ran in
families,implyingthatthegeneticmakeupofanindividualdeterminedtheir
creative ability. These theories have been superseded by more balanced
explanations of the roles of nature and nurture in the development of
creativeindividuals,butthisdoesnotmeanthatwecannotinvestigatetheevolutionof creativeindividualsthroughtheacquisitionofgenetictraitsin
Artificial Creativity simulations. To do so, we simply need to define a
genotypethat determines the values for the characteristics of individuals,
e.g.sizeandtypeofneuralnetwork,andselectindividualsforreproduction
based on their creative status. Interesting observations would include
whether or notthe genetic make-up ofcreativeindividualsconvergeupon
certainparametersoverseveralruns.Ifconvergencesofthissortcouldbe
found then we should be able to identify something like creative
personalitytraitsinindividuals.
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24 A.AUTHOR,B.AUTHORANDC.AUTHOR
7.3.3. TheDiversificationofCreativeIndividuals
ArtificialCreativityisaboutmodellingcreativesocietiesandwhilewehave
restrictedourselvestomodellingproducersthereareseveralotherimportant
players in these societies that do not directly produce artefacts but are
involvedinthedistributionandevaluationofthem.
Tomodelmorecomplexsocialrelationsweshouldimplementmodelsof
otherindividuals,e.g.consumers,distributors,critics,etc.Eachwouldhave
their own role to play in simulations of creative societies and would
influence each others behaviour; consumers would evaluate interesting
works and pay credits to producers of interesting works, distributorscommunicatetheworksofproducerswidely,andcriticscommunicatetheir
evaluationsofworkswidely.TheserelationshipsareillustratedinFigure11.
Usingdifferenttypesofindividuals,wecanbegintomodelsomemore
of the complexity discussed by Csikszentmihalyi (1999). We could also
model more complexrelationshipsbetweenagents,such as those between
designerandclient,byallowingcommunicationofrequirements.Asavery
simpleexample,aclientcouldcommunicateanexampleworkandinstructa
producer tomake something like itandexpect the new workto still be
interesting, i.e. similar-yet-different to the example. Importantly, the
personalcreativitytestsplayacriticalroleindeterminingthebehaviourof
allofthedifferenttypesofindividualsandthesocio-culturalcreativitytest
isstillandemergentpropertyofthewholesociety.
ProducerandConsumer
Producer,CriticandConsumers
Producer,DistributorandConsumers
Producer
Consumer
Distributor
Critic
Design
Evaluation
Figure11:Threedifferenttypesofindividualswithdifferentabilitiesandtheirrolesinthecommunicationofdesignsandevaluationsincreativedesignsocieties.
7.4. DOMAINSOFCREATIVITY
Domainsplayacrucialroleincreativesocietiesbydefiningofthetypesof
interactions that can take place between individuals within a field and
betweendifferentfields.Therearemanypossiblescenariosforstudyingthe
relationship between domainsandthe creativityof individuals and fields.
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PAPERTITLE 25
The following discussion provides only a very brief outline of the
possibilities.
7.4.1. MultipleDomains
People that move from one field to another, or contribute to multiple
domains,are often considered some of the most creative participants.We
can investigate the mechanisms involved in the creativity of these
individuals by modelling multiple fields and their associated domains.
Fields are defined by the communications of the individuals within them,
creating separate fields means creating groups of individuals that arespecialised in the type of information that they communicate. Effectively
thiscreatesabarriertoentryfornon-membersofafield.Individualsthat
transferfromonedomaintoanothermustfindsomewaytoovercomethis
barrier. One possible isfor agent toapproach the design space ofa field
whilewithinanotherfield,in asimilarwaythathasalreadybeenobserved
incliques.Theagentmaythenbeallowedentrancetoafieldbecausethe
agent producesartefacts thatare appreciatedby themembers of thefield.
ThisprocessisillustratedinFigure12(a).
(a) (b)
Figure12:Bridgingfieldsanddomains.In(a)anagenttraversesadesignspacegetsclose
enoughtoanotherthedesignspaceofanotherfieldthatitcansuccessfullycommunicatewith
itsmembersandberewarded.In(b)anagentstraddlestwofieldsandreceivescreditfrom
membersofbothasittransfersartefactsbetweendomains.Solidarrowsrepresentthetransfer
ofartefacts,dashedlinesrepresentthetransferofevaluationsorcredit.
7.4.2. StaticDomains
Wecandefinestaticfieldsbydefiningthedesignspacetobeexploredbya
field as a fixed property of its domain. Communication between fields
exploring different domains will be limited to products that lie in the
intersection ofdomaindesign spaces.Bysettingup suchenvironmentswe
can study the spread of ideas through related fields as a consequence of
interests shared by individualsdifferent domains. Agents that are able to
locatethemselvesattheintersectionofmultipledomainshavethepotential
to benefit by addressing a larger audience and by transferring creative
artefacts from one domain to another. An individual situated at the
intersectionoftwodomainsisillustratedinFigure12(b).
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26 A.AUTHOR,B.AUTHORANDC.AUTHOR
7.4.3. DynamicDomains
We can alsodefine dynamicfields byallowingindividuals to modify the
designspaceofthedomain,orbybasingthedesignspaceofadomainon
the products of another field. Allowing individuals to modify the design
spaceof a domain wouldallow a designspacetoshiftinthe directionof
interesting works communicated from other fields. We would be able to
study a sort of social curiosity as the field moved collectively in the
directionofnewandinterestingpossibilities.
D3
D1 D2
Figure13:Themovementofdesignspacesasaconsequenceof'collectivecuriosity'.
7.4.4. HierarchiesofDomains
Basingthedomainofonefieldonthedomainofanother,wouldallowthe
investigation of the effects of technological innovation in one domainopeningupnewcreativepossibilitiesinanother.Theexpansionofdesign
spaces in this way is illustrated in Figure 14 wherethe expansion of the
design space for domain D2 opens up new possibilities and expands the
designspaceofdomainD1.
D2
D1 D1
D2
Figure14:Theexpansionofadesignspaceasaconsequenceofchangesinasubordinate
domainsdesignspace.
7.4.5. EmergentDomains
Oneway to study theemergence of newdomains would betoimplement
permit the subdivision of a domain when subgroups of its field are
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PAPERTITLE 27
sufficiently different that they no longer share a common understanding.
This process of domain subdivision is reminiscent of speciation in the
naturalworld.Speciationisaprocesswherebyagroupoforganismsbecome
differentiatedastheyadapttotheirenvironment.Thedefinitionofaspecies
isagroupoforganismsthathavebecomesowelladaptedtoeachotherthat
theycanonlyreproducewithothermembersofthesamegroup.
Similarly, a field can be defined as a group of individuals that have
becomesospecialisedthattheyarelimitedtocommunicatingcreativeideas
toothermembersofthefield.Thedivisionofafieldintotwonewfieldsis
illustratedinFigure16.Thenewfieldsemergeasthecommunicationlinksbetween two subgroups weaken until they constitute two separate fields.
Thealreadyobservedformationofcliquescanbeseenasaprecursorofthe
emergenceofnewfieldsanddomains.
(a) (b) (c) (d)
Figure15:Thedivisionofafieldintotwonewfields.
Speciation is an important topic of research in Artificial Life and by
adapting some of the mechanisms studied there to model the splitting of
domainswemaybeabletostudytheemergenceofnewdesignschoolsand
possiblydesigndisciplinesinartificialsocieties.
7.5. CREATIVESOCIETIES
Primarily,ArtificialCreativityisconcernedwiththestudyof theemergent
behaviour of creative societies. The followingstudies are concernedwith
the possibilities for experimenting with social conventions, e.g.
communicationprotocols,toinvestigatetheiraffectsoncreativity.
7.5.1. CommunicationModels
ArtificialCreativitysimulationsprovidethenecessarymechanismsforbothdirect and indirect communication. Direct communication is supported by
thetransmission ofproductsandevaluationsas messages betweenagents.
Indirect communication is supported by the storage and retrieval of
symbolic representationsin a domain.Usingthis framework wecan study
the affects that different communication mechanisms have on the
developmentofcreativesocieties.
Oneinterestingpossibilitywouldbetosimulatetheintroductionofnew
communication technologies and see their affect on the creativity of
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28 A.AUTHOR,B.AUTHORANDC.AUTHOR
individualsandsocieties.Forexample,wecanaskthequestion:Whatare
affects of the introduction of global communication technologies like the
telephone,e-mailandtheWorldWideWeboncreativefieldslikedesign?
Eachtechnologyopensupnewpossibilitiesforthecommunicationofideas
orthecommunicationofdifferenttypesofartefacts.Wewouldanswerthis
questionbysimulatingsometypicalcommunicationanddomaininteraction
policiesofindividualsbeforeandaftertheintroductionofglobaldirectand
indirect communication and observe the behavioural changes of the
simulatedsociety.
7.5.2. TheEconomicsofCreativity
Creativefieldsareconstructedthroughthesocialinteractionsofindividuals
determined by their communication policies. The communication policy
implementedin allof thecreativeagentsin thecurrentsystemimplements
communication of creditfor producing interesting artworks but the credit
hasnoworthinsidethesimulation,itisimportantonlytoobserverswhocan
monitorthe creativityof individualsbywatching theircreditratings.This
doesntreflectthestatusofbeingconsideredcreativeintherealworld.For
instance, being considered creative imbues a status that can be used to
attract financial resources. Also, being creative costs, in both time and
money,andthisforcesanindividualtoconsiderthebenefitsofsearchingfor
radicallycreativesolutionsagainstthecostincurred.
7.5.3. ThePoliticsofCreativity
Givingdifferentcommunicationabilitiestodifferenttypesofagentsmakes
theselectionofwhichagentstocommunicatedwithanimportantfactorin
self-promotion of creative individuals. As Csikszentmihalyi (1999) points
out,convincingotherpeoplethatyouvehadacreativeideaisoftenharder
thanhavingtheideainthefirstplace.Insocietieswithinunequalstatusof
individuals,thequestionofwhichindividualstocommunicatewithbecomes
importantforindividualsseekingtherecognitionfrompeers.
Agentsinfluencethe behaviour ofotheragentsbycommunicatingtheir
artworks and their evaluations of those artworks. The current simulation
modelsaperfectegalitariansocietywherealloftheagentsarecreatedequalandnoagenthasmoreinfluencethananyoftheothers.Wearenotlimited
to modelling such utopian societies inArtificial Creativity andwith some
smalladjustmentswecanmodelsocietieswheresomearemoreequalthan
others.
By weighting evaluations according to the creativity of the individual
sendingit,alreadycreativeagentscanpromotethecreativestatureofother
agentsmore quicklythanuncreative agents.Recognitionbyan established
creative agent would have a significant impact on the status of young
agentstryingtogainrecognition.
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PAPERTITLE 29
8. Conclusions
ThecomputationalworkpresentedinthispaperhasillustratedtheArtificial
Creativityapproachtodevelopingmodelsofcreativesocieties.Byadapting
Liusdualgenerate-and-testmodelofcreativitywehaveproducedamodel
of creative societies that can be used to study socio-cultural creative
behaviour as an emergent propertyarising fromthe creative behaviour of
individuals. The implemented system models the evolution of notions of
creativitywithinanartificialsocietyovertimeasindividualscomeandgo,
thefieldchangesincomposition,andthedomainisaltered.The emergence of social behaviour, e.g. The Law of Novelty, and
dynamicsocialstructures,e.g.cliques;suggestthattheArtificialCreativity
approach to developing models of creative societies may contribute new
insightsintothenatureofcreativedesigninsocio-culturalsituations.Figure
14 illustrates the different levels at which creativity may be studied as a
pyramid ofemergentproperties.Eachlevelrepresentsa differentaspectof
creativity that is emergent from the ones below it. Layers that represent
processes are coloured darker than those that represent products, process
andproductlayersalternateupthepyramid;propertiesofcreativeproducts
are a consequence of the processes that created them and the behaviour
higher-levelprocessesareaffectedbytheproductsthattheyoperateupon.
Eachlevelinfluencesthelayersbelowthembyprovidingasituationfortheproducts andprocesses. Forexample, emergent social structures influence
the communication of products and the products communicated influence
thebehaviourofindividualsthatsendandreceivethem.
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30 A.AUTHOR,B.AUTHORANDC.AUTHOR
Individual
Processes
Individual
Products
Individual
Behaviours
Communicated
Products
Social
Behaviour
CulturalProducts
Emergence Influence
Figure16:Apyramidofcreativity.
Thefoundationsofthecreativepyramidaretheprocessesinternaltothecreative agent that allows it to generate-and-test ideas. The result of
executing these processes is the creative products. Traditionally,
computational research has concentrated on thesetwo levels byencoding
processesthoughtto be important increativityina pieceof software and
getting experts to examine the results of running those processes to
determine whether the processesare creative. Intraditionalcomputational
models,thehigherlevelsofthepyramidarenotmodelledinthesoftware
andareprovidedbypeople.
ArtificialCreativitysuggestsa differentapproach;insteadofevaluating
the products of a piece of software to determine its creativity, it focuses
uponthebehavioursofagentsandartificialsocieties.ArtificialCreativityis
concerned with modelling the creative behaviours of individuals, e.g.curiosity,andstudyingtheemergentsocialbehaviourswhenindividualsare
puttogether.BecauseindividualsinanArtificialCreativitysimulationmust
beabletoevaluatethecreativityofcommunicatedproductsandhenceother
individuals,thedetailsoftheproductsofindividualsbecomelessimportant.
More important in the study of Artificial Creativity are the socio-cultural
structuresthatemergeasaconsequenceof thecommunicationofproducts
andevaluations.
The Artificial Creativity approach permits the computational study of
highestlevelsofcreativityillustratedinFigure14withouthavingtodevelop
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PAPERTITLE 31
agents that can integrate, and achieve creative status, in human society.
ArtificialCreativity simulations permit theexperimentationwith creativity
in artificial societies thatwouldbeimpossible in therealworld,allowing
thestudyofcreativity-as-it-isinthecontextofcreativity-as-it-could-be.
Acknowledgements
Tocome
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