Computational Creativity Research: Towards Creative Machines

417 Pages·2015·12.263 MB·English

by  Tarek R. Besold, Marco Schorlemmer, Alan Smaill (eds.)

Atlantis Thinking Machines
Series Editor: K.-U. Kühnberger
Tarek R. Besold
Marco Schorlemmer
Alan Smaill
Editors
Computational
Creativity Research:
Towards Creative
Machines
Atlantis Thinking Machines
Volume 7
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Kai-Uwe Kühnberger, Osnabrück, Germany
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Tarek R. Besold Marco Schorlemmer
(cid:129)
Alan Smaill
Editors
Computational Creativity
Research: Towards
Creative Machines
Editors
TarekR. Besold Alan Smaill
Instituteof CognitiveScience CISA,School ofInformatics
Universityof Osnabrück Universityof Edinburgh
Osnabrück Edinburgh
Germany UK
MarcoSchorlemmer
IIIA-CSIC
Bellaterra, Barcelona
Spain
ISSN 1877-3273
Atlantis ThinkingMachines
ISBN 978-94-6239-084-3 ISBN 978-94-6239-085-0 (eBook)
DOI 10.2991/978-94-6239-085-0
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Foreword
How Computational Creativity Began
Computationalcreativity(CC,forshort)istheuseofcomputerstogenerateresults
thatwouldberegardedascreativeifproducedbyhumansalone.Strictlyspeaking,
this includes not only art, but also innovative scientific theories, mathematical
concepts,andengineeringdesigns.Butthetermisoftenused—asIshalldo,here—
to apply mainly to results having artistic interest.
CCwasglimpsedonthehorizonover170yearsago,whenAdaLovelacesaidof
CharlesBabbage’sAnalyticalEnginethatit“mightcomposeelaborateandscientific
piecesofmusicofanydegreeofcomplexityorextent”[41,p.270].Indeed,thegeneral
principlehadbeenglimpsedintheearlyeleventhcentury,whenGuidod’Arezzo—
whoalsoinventedthebasisoftonicsolfaandoftoday’smusicalnotation—wrotea
system offormal rules for composing hymns. But whereas d’Arezzo had not had
computingmachinesinmind,Lovelacedid.
Acenturylater,AlanTuringwasproducing(asajoke)programmedlove-letters
on Manchester’s MADM computer [37]; and haikus would soon be generated on
Cambridge’s EDSAC machine (see below). Even more to the point (or so it might
seem), “creativity” was identified as one of the chief goals in the document plan-
ningtheDartmouthSummerSchoolof1956[42].Thatmeetingwaswhereartificial
intelligencewasofficiallynamed,andwherehopesforcomputationalmodelingfirst
reached beyond a tiny coterie.
By the 1950s, then, there was no shortage of heralds.
Heralds Unheard
Buttheirtrumpetingdidnotannounceatriumphantprocessionenteringthehallsof
fame. The forefronting of creativity in the Dartmouth document did not establish
CCasaresearchproject.Forseveraldecades,creativitywaslargelyignoredbythe
very people who had declared it a prime goal for AI.
v
vi Foreword
Thatisnotquitefair:problem-solvingingeneralinvolvescreativethinking,and
AI soon abounded with intriguing attempts to model that. The pioneering Logic
Theorist was first run in the final week at Dartmouth, followed by its cousin GPS
shortly afterwards [54–56]. Arthur Samuel’s checkers-player was even earlier: it
had been implemented in 1949, was learning how to beat its human opponents by
1955, and had appeared on TV in February 1956—a full six months before the
Dartmouth meeting [64]. Moreover, when Simon [65], 40 years later, sought to
convince sceptics that AI is respectable as a science, he took creative problem-
solving as his core topic—defiantly subtitling his paper “AI on the Topics of
Intuition, Insight, and Inspiration”.
But if creative thinking was arguably a key research theme of professional
AI right from the start, there was more emphasis on the “thinking” than on the
“creative”. Relatively few of the early AI workers focussed on what is normally
thoughtofascreativity.Amarel[1]wroteafascinatingpapershowingthatcreating
new representations of a given problem-space could make problem-solving
increasingly efficient—but he could not say how those novel representations
themselves were generated. Evans [21] wrote an interesting model of analogical
thinking, applied to problems typically included in IQ tests. Gelernter’s [24]
geometry-program apparently came up with a highly insightful proof about isos-
celes triangles (only apparently: the achievement here was much less striking than
itseemed—[5,pp.116–123]).Inthe1970s,scientificdiscoverywasmodelledbya
team led by Simon [35, 36]. And story-writing was attempted too, albeit with
painfully crude results [33, 51, 52].
The most convincing early computer models of creativity were built by people
outside mainstream AI. For instance, Lejaren Hiller, a professional chemist who
was also a Master of Music, initiated the Illiac suite in the 1950s [27]. The Illiac
musicwasgeneratedfromrulesdefiningvariousstyles(includingsixteenth-century
counterpoint and 12-tone music, and a variety of dynamics and rhythms), some-
times combined with tone-pairs chosen by chance. In the early 1960s, the avant-
garde artists of IRCAM, in Paris, started experimenting with computer music—
thoughfocussingmoreoninstrumentationthancomposition.Andin1968,theIFIP
meeting in Edinburgh included a competition for computer-composed music—
which led to the founding of the Computer Arts Society (CAS).
AI workers in general did not identify with these ventures. Nor—despite the
mention of “creativity”, “invention”, and “discovery” in the Dartmouth proposal,
plus occasional asides about “creative” problem-solving—did they see their own
work as at all relevant. That is why in the early 1970s, when I was writing a book
on the field [2], my AI colleagues were bewildered on being told that I was
devoting one whole chapter to creativity. Several complained: “But there isn’t any
workoncreativity!”(Myanswerwastwofold:(1)IfAIisrelevanttointelligencein
general,then there shouldbe!,and (2) much oftheexisting AI work wouldinfact
turnouttoberelevant,onceresearchersstartedthinkingseriouslyaboutcreativity.)
The difference in attitude between the AI professionals and the artists (whether
professional or amateur) is partly explained by differences in motivation. Like AI
itself, CC can be driven by two distinct motivations. Individual workers may be
Foreword vii
excitedbyboth,butveryoftenareinterestedonlyinone.WithinAIingeneral,the
goalmaybescientificortechnological:understandingthemindorproducinguseful
tools.Analogously,CCpractitionersmayaimeithertoilluminatethepsychological
processesunderlying creativity ortoproduce aesthetically interestingresults. Most
professional computer artists have scant interest in the first of those goals. Rather,
theywanttoproduceartworks.ThepioneersofAIignoredart-focusedCCbecause
they were interested in modelling mental processes. (Even when they hoped to
build useful tools, they typically started from some psychological theory or intro-
spection about how people think.)
Early Efforts in CC Art
AfewexamplesofveryearlyCCartwereindividuallyexhibitedinthemid-1960s.
Thefirstofthese—in1963—wereJoanShogren’scomputer-generateddrawingsat
San Jose, California [53, p. 66]. Others, which received more attention, were by
GeorgNees,MichaelNoll,andFriederNake,andshowninStuttgartandNewYork
in 1965. They were highly abstract, quasi-geometrical, designs, generated by pro-
grams written not by professional artists but by mathematicians.
A wide range of early CC work was featured soon afterwards at London’s
seminalCyberneticSerendipityexhibitionin1968[59].Thispresentedavarietyof
experiments with the new computer technologies, both analogue and digital. They
included kinetic sculptures by Gordon Pask and Edward Ihnatowicz, algorithmic
graphics by the people mentioned above, and haikus generated by Margaret
Masterman’s group in Cambridge [49, 50]. The show aroused a good deal of
excitement—some of which was largely unmerited. (Consider this haiku, for
instance:Allgreenintheleaves/Ismelldarkpoolsinthetrees/Crashthemoonhas
fled; its “success” is deceptive, being due to the reader’s well-practised habit of
projecting meaning into obscure poetry.)
By the following year, CC was being initiated not only by scientists and
mathematicians,whohappenedtohaveaccesstocomputers(forexample,Manfred
Mohr at the Paris Institute of Meteorology), but also by aspiring—and some
already-established (see below)—artists. Several of these were directly influenced
by the Cybernetic Serendipity exhibition. For instance, this event inspired two
young painters who were already committed to modernism—Ernest Edmonds and
Paul Brown—to turn to computers to produce their artworks. (For modernists, the
veryimpersonalityofcomputerswasanadvantage.)Edmondssooncomplemented
his actual artwork by discussing the nature of computer art in general terms [20].
Some art-inspired CC efforts came from architects. The pioneer here was John
Frazer(influencedbyPask),whosework—asastudent—onCADand“intelligent”
environments won an Architectural Association prize as early as 1969. With his
wife Julia Frazer, he went on to provide more elaborate computer-generated
(andeventuallyinteractivelyevolved)designsforbuildingsandurbancentres[23].
viii Foreword
Other architects wrote “shape grammars” that could generate designs for Palladian
villas or Prairie Houses [26, 34, 67].
Two highly visible, and highly influential, CC artists of the early-1980s were
Harold Cohen in the graphic arts and David Cope in music. Both men had
embarked on a decades-long—and still continuing—project in the late 1960s,
wherein their CC-oeuvre grew increasingly impressive [11–14, 16–19, 44]. And
bothhadtolearntodotheirowncoding,usingequipmentthatwasstateoftheartat
the time, but seems hugely primitive today. Cope even had to invent an early
equivalent of the MIDI code for transferring musical notation to the computer.
Cohen was remarkable not least for the fact that he abandoned a hugely
successful career as a pots-and-brushes painter for CC art. He felt that this would
enable him to understand more clearly just why he was making certain aesthetic
decisions rather than others. Encouraged by the AI scientist Edward Feigenbaum,
he moved to Stanford—and then to San Diego—to use the computing facilities
there.
His early introduction to the then young AI community was partly responsible
forhisvisibility.Butaboveallwastheaestheticmeritofhisartwork.Heproduced
(and is still producing) increasingly striking versions of his program AARON,
which gradually became more able to represent 3D scenes. At first, AARON’s
images hinted at three-dimensionality, containing objects interpretable as arbi-
trarily-shaped rocks. Later, they represented foliage, and increasingly realistic
human figures and faces. From the mid-1990s, his main focus has been on colour.
The later versions of his program can generate colours with extraordinary success
(“I am a first-rate colourist. AARON is a world-class colourist”—Cohen p.c.). But
even when AARON was able to produce only black-and-white drawings of
shapeless rocks, Cohen’s work was being shown in major galleries around the
world.
Cope, also, was a recognized practising artist: namely, a composer (and also a
professorofmusicologyatUCSantaCruz).Hisprogram,“Emmy”,composednew
pieces in many different styles—from Western to Thai, baroque fugues to modern
jazz, Bach to Joplin; and it could produce new styles by mixing existing styles
together. The program explored generative structures (formally defined by Cope
qua musicologist), as well as recombining specific musical motifs.
In building the data-base, Cope first selected brief “signatures” as being char-
acteristic of the human composer concerned. These exemplified melody, harmony,
metre, and ornament. Next, Emmy used statistical techniques to identify the core
features of the signatures. Finally, the program—guided by general musicological
principlesdefinedbyCope—usedthese features tobuild newstructures. Theearly
versionsofEmmywereimplementedusingthetraditionalmethodsofsymbolicAI;
from 1990, Cope added a connectionist network, giving increased flexibility.
Whereas Cohen’s AARON, at least after the early days, never produced an
artwork that was aesthetically unacceptable, Cope’s Emmy sometimes did (for
examples, see [18, pp. 182–3, 385–390]). Nevertheless, it more often produced
highly convincing compositions (the 1,000 “best” are listed in [19, pp. 385–9]).
These impressed even sceptical critics who heard them (one reviewer invited to
Foreword ix
Emmy’sfirstpublicconcertin1989actuallyrefusedtohearthem,soupsetwashe
by the very idea of computer-generated “pastiches”: [19, p. 345])—including
DouglasHofstadter,whohadlongcomplainedthatsymbolicAIworkoncreativity
was shallow and trivial [29, pp. 55–193]. (In fact, Hofstadter was over-impressed.
He reported playing one of Emmy’s “Chopin” mazurkas and finding it “unmis-
takably Chopin-like in spirit, and… not emotionally empty” [28, p. 39]. Here, he
was making the same mistake as those over-enthused by the haikus at Cybernetic
Serendipity:theemotionwasbeingprojectedontothescorebyHofstadterhimself.)
In the late 1980s, new types of computing became available. These included
PDP connectionism [43, 63] and evolutionary programming. Evolutionary com-
puting had been outlined over twenty years earlier by John Holland, who formally
definedthefirstgeneticalgorithmintheearly-1970s[31,32].Buthisalgorithmhad
not been implemented, because of lack of computer power. By the 1990s, the
necessary machines existed. Both these new AI methodologies attracted interest
from CC artists.
Forexample,RichardBrown’sStarfish,exhibitedattheturnofthecentury,used
connectionism to build what appeared to be a huge multi-coloured starfish trapped
inside a translucent marble table. Whenever someone approached it, or made a
movementorasoundnearby,thetrappedstarfishmoved—andinamazinglylifelike
ways. In fact, this was an illusion: the image of the starfish was projected down-
wards onto the (empty) table from the ceiling. It was represented as a neural
network,whichrespondedtosoundsandmovementsinthevicinitybyalteringthe
shape of the starfish’s limbs.
As for evolutionary CC art, early examples were produced by Sims [66] and
William Latham [68]—and others soon followed [45, 46, 70]. Even Cohen even-
tually experimented with evolutionary techniques, to produce a constantly varying
range of coloured imagery. One way in which the individual examples varied was
the extent to which they allowed relatively radical mutations to happen. Sims, for
instance, allowed one image-generating mini-program to be nested into, or con-
catenated with, another. In consequence, the visual transformations observed were
sometimes so great as to destroy the family resemblance even between neigh-
bouring generations. Latham, by contrast, allowed only relatively superficial
mutations—such as changing one numeral into another. His CC artworks were,
therefore, instantly recognizable as “Lathams”, and even as falling within a par-
ticular Latham style.
Even those CC artists who kept faith with the more traditional (symbolic) AI
methodscouldtakeadvantageoftherecentincreasesincomputerpower.Edmonds,
for example, produced increasingly ambitious interactive works integrating colour
and music. And Paul Brown, who had been working with cellular automata for
many years, was able to define increasingly complex CA systems.