Breeding the Evolutionary:

Interactive Emergence in Art and Education


By Dan Collins


This paper first given at the 4th Annual Digital Arts Symposium:  Neural Net{work}, University of Arizona, Tucson, AZ  April 11 – 12, 2002



Learning is not a process of accumulation of representations of the environment; it is a continuous process of transformation of behavior…

 --Humberto Maturana (1980)[1]

Art is not the most precious manifestation of life.  Art has not the celestial and universal value that people like to attribute to it.  Life is far more interesting. 

--Tristan Tzara, “Lecture on Dada” (1922)[2]





In August 2000, researchers at Brandeis University made headlines when they announced the development of a computerized system that would automatically generate a set of tiny robots—very nearly without human intervention.  “Robots Beget More Robots?,” asked the New York Times somewhat skeptically on its front page.  Dubbed the Golem project (Genetically Organized Lifelike Electro Mechanics) by its creators, this was the first time that robots had been designed by a computer and robotically fabricated.  While machines making machines is interesting in and of itself, the project went one step further:  the robot offspring were “bred” for particular tasks.  Computer scientists Jordan Pollack and colleague Hod Lipson had developed a set of artificial life algorithms—evolutionary instruction sets—that allowed them to “evolve” a collection of “physical locomoting machines” capable of goal oriented behavior.[3]


The Golem project is just one example of a whole new category of computer-based, creative research that seeks to mimic--somewhat ironically given its dependence on machines--the evolutionary processes normally associated with the natural world.  Shunning fixed conditions and idealized final states, the research is characterized by an interest in constant evolutionary change, emergent behaviors, and a more fluid and active involvement on the part of the user.    


The research suggests a new and evolving role for artists and designers working in computer aided design process and interactive media, as well as an expanded definition of the user/audience.  Instead of exerting total control over the process and product where “choices” are made with respect to every aspect of the creative process, the task of the artist/researcher becomes one of interacting effectively with machine-based systems in ways that supercharge the investigative process. Collaboration is encouraged: projects are often structured enabling others—fellow artists, researchers, audience members—to interact with the work and further the dialogue.  While the processes often depend on relatively simple sets of rules, the “product” of this work is complex, open-ended, and subject to change depending on the user(s), the data, and the context.


A healthy mix of interdisciplinary research investigating principles of interaction, computational evolution, and so-called emergent behaviors inform and deepen the work.  Artists are finding new partners in fields as diverse as educational technology, computer science, and biology.  There are significant implications for the way we talk about the artistic process and the ways we teach art.


As an introduction to this new territory, I will trace some of the conceptual and historical highlights of “evolutionary computing” and “emergent behavior.” I will then review some of the artists, designers, and research scientists who are currently working in the field of evolutionary art and design. Finally, I will consider some of the pedagogical implications of emergent art for our teaching practices in the arts.   




Most natural and living systems are both productive and adaptive.  They produce new material (e.g, blood cells, tissue, bone mass) even while adapting to a constantly changing environment.  While natural and living systems are "productive" in the sense of creating new "information," human-made machines that can respond with anything more than predictable binary "yes/no" responses are a relatively recent phenomenon. To paraphrase media artist Jim Campbell, most machines are simply "reactive," not interactive.[4]  To move beyond reactive mechanisms, the system needs to be smart enough to produce output that is patently new—that is, not already part of the system


"Intelligent" machines, being developed with the aid of "neural networks" and "artificial intelligence," can actual learn new behaviors and evolve their responses based upon user input and environmental cues. Over time, certain properties begin to "emerge" such as self-replication or patterns of self-organization and control. These so-called "emergent properties" represent the antithesis of the idea that the world is simply a collection of facts waiting for adequate representation. The ideal system is a generative engine that is simultaneously a producer and a product.  


In Steven Johnson’s recent book, Emergence, he offers the following explanation of emergent systems:


In the simplest terms (emergent systems) solve problems by drawing on masses of relatively stupid elements, rather than a single, intelligent “executive branch.”  They are bottom-up systems, not top-down.  They get their smarts from below.  In a more technical language, they are complex adaptive systems that display emergent behavior.  In these systems, agents residing on one scale start producing behavior that lies one scale above them:  ants create colonies; urbanites create neighborhoods; simple pattern-recognition software learns how to recommend new books.  The movement from low-level rules to higher-level sophistication is what we call emergence.[5]


In terms of the hard science underlying the concept of emergence, it should be said that many approaches to evolutionary design, artificial life, adaptive systems, evolutionary computation, share similar characteristics but have distinct operating principles that are beyond the scope of this essay to review.  For the purposes of this essay, the word “emergent” will refer to both the behaviors of emergent systems and the metamorphosed families of objects created through the use of evolutionary, emergent, or “artificial-life” principles.


Emergent behavior is the name given to the observed function of an entity, and is, generally speaking, unrelated to the underlying form or structure. These behaviors exist because of the nature of the interaction between the parts and their environment, and cannot be determined through an analytical reductionist approach.[6]


Novel forms generated using genetic algorithms exhibit “emergent properties” as well.  Instead of laying the stress on the function of the object, an “artificial-life” (“a-life”) designer will often focus on the form or changing morphology of discrete, synthetically derived entities.  To a certain extent, early “evolutionary art” emphasized the fantastic power of these techniques for unpredictable form generation.   Later interactive and emergent artworks have focused more on function—i.e., emergent behavior.


For evolution to occur, the system must be capable of reproduction, inheritance, variation, and selection. Further, unlike natural evolution, artificial evolution using evolutionary algorithms (EAs) also require three other important features:  intitalisation, evaluation, and termination.  EAs are “given a head start” by initialising (or seeding) them with solutions that have fixed structures, behaviors, or meanings, but random values (often generated by pseudo random number generators).  Evaluation in EAs is responsible for guiding evolution towards better solutions.   Finally, explicit termination criteria are built into the program to halt evolution, typically when a “best fit” or acceptable solution is achieved, or when a predetermined number of interations have been tested.[7]

Irrespective of its particular manifestation, the key concept—the thing that makes a phenomenon “emergent” —is that forms, self-sustaining behaviors, and meanings can evolve over time through a simple set of rules to produce completely non-predictable results.

History of Emergence as a Concept 


The historical foundations of the concept of “emergence” can be found in the work of John Stewart Mill in the 19th century who, in A System of Logic (1843), argued that a combined effect of several causes cannot be reduced to its component causes.  Early 20th century proponents of “emergent evolution” developed a notion of emergence that echoes Mill’s idea:  elements interact to form a complex whole which cannot be understood in terms of the elements; the whole has emergent properties that are irreducible to the properties of the elements.[8]


The first real statement of the possibility of linking machines to evolutionary principles was developed as a thought experiment by the mathematician John Von Neumann in the late 1940s who conceived of non-biological, kinematic self-reproducing machines.[9]


At first Von Neumann investigated mechanical devices floating in a pond of parts that would use the parts to assemble copies of themselves. Later he turned to a purely theoretical model. In discussions with Polish mathematician Stanislaw Ulam the idea of cellular automata was born. Just before his death in 1957, von Neumann designed a two-dimensional, 27-state cellular automaton—a self replicating machine--which carried code for constructing a copy of itself. His unpublished notes were edited by Arthur Burks who published them in 1966 as a book, The Theory of Self-Reproducing Automata.[10]


Other influential thinkers in the history of emergent systems and evolutionary art include Richard Dawkins who inadvertently founded the field of evolutionary art in 1985 when he wrote his now famous Blind Watchmaker algorithm[11] to illustrate the design power of Darwinian evolution.  The algorithm demonstrates very effectively how random mutation followed by non-random selection can lead to complex forms.  These forms, called “biomorphs,” are visual representations of a set of “genes.”


Each biomorph in the Blind Watchmaker applet has the following 15 genes:


Biomorph Reserve


The science of emergent behavior is closely related to the work done using machines to model evolutionary principles in the arts. Among the first “evolutionary artists” to understand the significance of Richard Dawkins’ work is the British sculptor, William Latham.   Initially, Latham focused on the form generation capabilities of evolutionary systems. 


Trained as a Fine Artist, Latham’s early drawings explored the growth and mutation of images of organic-looking shapes.  In the mid 80s, he became an IBM Fellow and began using computers to develop mutating images. This idea was applied to other areas like architecture and financial forecasting, where interesting mutations of scenarios could be selected and bred, with the user acting like a plant breeder.  Between the years 1987 – 1994 at IBM, Latham established his characteristic artistic style and began working with IBM mathematician Steven Todd. IBM’s sponsorship of this ground breaking work led to Todd developing the “Form Grow Geometry System” (aka FormGrow) which was designed to create the bizarre organic forms for which the artist is known.[12]



In 1992, Latham published a book, Evolutionary Art And Computers (with Stephen Todd)[13], documenting the development of his extraordinary form of art.  Latham set up programs on the basis of aesthetic choices in such a way that the parameters of style and content in the images are established but the final form is not predetermined. Much of the time, a fixed 'final form' may never materialize. Random mutation allows the artist to navigate through the space of the infinitely varied forms that are inherent in his program. His early FormGrow program provided rules through which the 'life-forms' are subject to the processes of 'natural selection.' The results of such "Darwinian evolution driven by human aesthetics" are fantastic organisms whose morphologies metamorphose in a sequence of animated images.[14]




In the last few years, Latham has moved beyond the creation of images into the world of interactive gaming. One of his newest applications is a computer game called Evolva. Released in early 2000, Evolva enacts the process of evolution -- but it is the game warriors themselves who evolve. Picture this scenario:  sometime in the future, the human race has mastered the art of genetic engineering and created the ultimate Darwinian warrior -- the Genohunter. A Genohunter kills an enemy, analyses its DNA, and then mutates, incorporating any useful attributes—strength, speed, bionic weapons—possessed by the victim.[15]


In the area of robotics, the artist David Rokeby saw the potential of emergent properties to mitigate the “closed determinism” of some interactive robotic artwork several years ago.  In 1996 he wrote an essay which referenced the work of robot artist, NormanWhite. One of White's robots, Facing Out, Laying Low, interacts with its audience and environment, but, if bored or over-stimulated, it will become deliberately anti-social and stop interacting.  Rokeby writes:

This kind of behaviour may seem counter-productive, and frustrating for the audience. But for White, the creation of these robots is a quest for self-understanding. He balances self-analysis with creation, attempting to produce autonomous creatures that mirror the kinds of behaviours that he sees in himself. These behaviours are not necessarily willfully programmed; they often emerge as the synergistic result of experiments with the interactions between simple algorithmic behaviours. Just as billions of simple water molecules work together to produce the complex behaviours of water (from snow-flakes to fluid dynamics), combinations of simple programmed operations can produce complex characteristics, which are called emergent properties, or self-organizing phenomena.[16]

Interactive Emergence


Educational technologist Ellen Wagner defines interaction as "… reciprocal events that require at least two objects and two actions. Interactions occur when these objects and events mutually influence one another."[17]


High levels of “interactivity” are achieved in human/human and human/machine couplings that enable reciprocal and mutually transforming activity.  Interactivity—particularly the type that harnesses emergent forms of behavior—requires that both parties—human users or machines—be engaged in open-ended cycles of productive feedback and exchange.  Beyond simply providing an on/off switch or a menu of options leading to “canned” content, users should be able to interact intuitively with a system in ways that produce new information.  Interacting with a system that produces emergent phenomena is what I am calling “interactive emergence.”


Concrete examples from art and technology research illustrate how different individuals, groups, and communities are engaging in interactive emergence—from the locally controlled parameters characteristic of the video game and the LAN bash, to large scale interactions involving distributed collaborative networks over the Internet.  Artists and Scientists such as Eric Zimmerman (game designer, theorist, and artist); John Klima (artist and webgame designer); Hod Lipson and Jordan B. Pollack (The Golem Project), Pablo Funes (computer scientist and EvoCAD inventor); Christa Sommerer, and Laurent Mignonneau (Interactive systems); Ken Rinaldo (Artificial Life); Yves Amu Klein (Living Sculpture); are doing pioneering work in an area that could be called “evolutionary art and design”.  Other artists approaching using evolutionary and emergent principles in their work include Jeffrey Ventrella (Gene Pool), The Emergent Art Lab, David Rokeby / very nervous systems, Thomas Ray, Jon McCormack, Bill Vorn and Louis-Phillipe Demers, Simon Penny, Erwin Driessens and Maria Verstappen, Steven Rooke, Nik Gaffney, Troy Innocent, and Ulrike Gabriel.  What differentiates the work of these artists from more traditional practices?  What educational background, perceptual skills, and conceptual orientations are required of the artist—and of the viewer/participant?  What systems, groups, or individuals are acknowledged and empowered by these new works?


Creating an experience for a participant in an interactive emergent artwork must take into account that interactions are, by definition, not "one-way" propositions. Interaction depends on feedback loops[18]that include not just the messages that preceded them, but also the manner in which previous messages were reactive. When a fully interactive level is reached, communication roles are interchangeable, and information flows across and through intersecting fields of experience that are mutually supportive and reciprocal. The level of success at which a given interactive system attains optimal levels of reciprocity could offer a standard by which to critique interactive artwork in general.  Interactive emergence could be gauged by the degree to which the parties achieved optimal reciprocity (sex is an appropriate analogy here) along with the degree to which the system was “productive” of new content, not predicted by the contents of its memory.


Many artists have developed unique attempts at interactive emergence exploring different forms of display, user control processes, navigation actions, and system responses. Different works have varying levels of audience participation, different ratios of local to remote interaction, and different levels of emergent phenomemona. Moreover, the range of artistic attempts at interactive emergence provide us with a variety of approaches that could be used for diverse kinds of learners in a variety of educational settings. Understanding experiments with interactive emergence in an art context may help us to better understand its use in pedagogical settings.


Many projects in recent years exhibit various levels of interaction.  But the capability of exhibiting or tracking "emergent properties" is seen by the author as a future hallmark of high level interactive systems. With projects that enable these heightened levels of interactivity, we may begin to see the transformation of the discrete and instrumental character of “information” into a broad—and unpredictable-- “knowledge base” that honors the contexts and connections essential to global understanding and exchange.


With these challenges in mind, I would like to discuss briefly the work of an artist team, an independent artist, an animator, and a computer scientist who are exploring “emergent systems” in their research:  Christa Sommerer and Laurent Mignonneau, Ken Rinaldo, Karl Sims, Pablo Funes.


Christa Sommerer and Laurent Mignonneau: Interactive Plant Growing (1993)

Austrian-born Christa Sommerer and French-born Laurent Mignonneau teamed up in 1992, and now work at the ATR Media Integration and Communications Research Laboratories in Kyoto, Japan.  In nearly a decade of collaborative work, Sommerer and Mignonneau have built a number of unique virtual ecosystems, many with custom viewer/machine interfaces. Many of their early projects allow audiences to create new plants or creatures and influence their behavior by drawing on touch screens, sending e-mail, moving through an installation space, or by touching real plants wired to a computer.

Artist's rendering of the installation showing the five pedestals with plants and the video screen.

Interactive Plant Growing is an example of one such project. The installation connects actual living plants, which can be touched or approached by human viewers, to virtual plants that are grown in real-time in the computer. In a darkened installation space, five different living plants are placed on 5 wooden columns in front of a high-resolution video projection screen. The plants themselves are the interface. They are in turn connected to a computer that sends video signals from its processor to a high-resolution video data projection system. Because the plants are essentially antennae hard-wired into the system, they are capable of responding to differences in the electrical potential of a viewer's body. Touching the plants or moving your hands around them alters the signals sent through the system. Viewers can influence and control the virtual growth of more than two dozen computer-based plants.

Screen shot of the video projection during one interactive session.

Viewer participation is crucial to the life of the piece. Through their individual and collective involvement with the plants, visitors decide how the interactions unfold and how their interactions are translated to the screen. Viewers can control the size of the virtual plants, rotate them, modify their appearance, change their colors, and control new positions for the same type of plant. Interactions between a viewer's body and the living plants determine how the virtual three-dimensional plants will develop. Five or more people can interact at the same time with the five real plants in the installation space. All events depend exclusively on the interactions between viewers and plants.

The artificial growing of computer-based plants is, according to the artists, an expression of their desire to better understand the transformations and morphogenesis of certain organisms (Sommerer et al, 1998).

Sommerer and Mignonneau: Verbarium (1999)

In a more recent project the artists have created an interactive "text-to-form" editor available on the Internet. At their Verbarium web site, on-line users are invited to type text messages into a small pop up window. Each of these messages functions as a genetic code for creating a visual three-dimensional form. An algorithm translates the genetic encoding of text characters (i.e., letters) into design functions. The system provides a steady flow of new images that are not pre-defined but develop in real-time through the interaction of the user with the system. Each different message creates a different organic form. Depending on the composition of the text, the forms can either be simple or complex. Taken together, all text images are used to build a collective and complex three-dimensional image. This image is a virtual herbarium, comprised of plant forms based on the text messages of the participants. On-line users help to not only create and develop this virtual herbarium, but also have the option of clicking on any part of the collective image to de-code earlier messages sent by other users.

Screen shot of the Verbarium web page showing the collaborative image created by visitors to the site.
The text to form algorithm translated the author’s phrase "purple people eater" into the image at the upper left. This image was subsequently collaged into the collective "virtual herbarium."

In the following passage, the artists discuss future goals for the work:

We anticipate that as more users participate, increasingly complex image structures will emerge over time…While our prototype system succeeds in modeling some of the features of complex system, future updates of the systems should include the modeling of genetic exchange of information (text characters) between forms, creating offspring forms through standard genetic crossover and mutation operations as we have used them in the past. The potential benefit of such an extended system will be the expansion of diversity, the reaction to neighbors and to external control, exploration of their options, and replication, basically the remaining features that are commonly associated with complex adaptive systems, as described by Crutchfield. Another further update of the system should also include the capacity to simultaneously display all messages in the browser's window; this should make it possible for users to retrieve all messages ever sent and to follow the whole evolution of interaction history.[19]



Ken Rinaldo: Autopoiesis (2000)

Overview of all fifteen robotic arms of the Autopoiesis installation.
Photo credit: Yehia Eweis.

A work by American artist Ken Rinaldo was recently exhibited in Finland as part of "Outoaly, the Alien Intelligence Exhibition 2000," curated by media theorist Erkki Huhtamo.[20]  Rinaldo, who has a background in both computer science and art, is pursuing projects influenced by current theories on living systems and artificial life. He is seeking what he calls an "integration of organic and electro-mechanical elements" that point to a "co-evolution between living and evolving technological material."

Rinaldo's contribution to the Finnish exhibition was an installation entitled Autopoiesis[21], which translates literally as "self making." The work is a computer-based installation consisting of fifteen robotic sound sculptures that interact with the public and modify their behaviors over time. These behaviors change based on feedback from infrared sensors that determine the presence of the participant/viewers in the exhibition, and the communication between each separate sculpture.

The series of robotic sculptures--mechanical arms that are suspended from an overhead grid--"talk" with each other (exchange audio messages) through a computer network and audible telephone tones. The interactivity engages the participants who in turn affect the system's evolution and emergence. This interaction, according to the artist, creates a system evolution as well as an overall group sculptural aesthetic. The project presents an interactive environment that is immersive, detailed, and able to evolve in real time by utilizing feedback and interaction from audience members.

Karl Sims

One of the most innovative creators of “artificial life” is Karl Sims. Sims’s cross disciplinary background at MIT in Biology and computers gave him insights into how computers could be used to model genetic systems that evolved "creatures" -- objects with bodies and the capacity to develop evolving intelligent behaviors over time. In earlier experiments with growing "plants," he specified instructions for evolving the development of the organisms.  With his creatures, he added movements and intelligent behaviors that could also evolve. Removing himself from the selection of the "fittest" from one generation to the next, Sims established goals, then let the computer determine how well offspring in a generation met the fitness criteria, and produce the next generation.

In an interview with the late visionary theorist, Steven Holtzman,  Sims stated: "I've always been interested in getting computers to do the work. While I worked in Hollywood, I was doing animations with details that no one would ever want to do by hand. For example, I used a computer to create an animated waterfall, when I could never have designed each drop of water for each frame in the animation. Instead, I created it with a completely procedural method." -- that is, he created a general description of the waterfall and then let the computer generate the waterfall itself.

Sims soon began playing with relationships between his creatures. Holtzman described one such project in detail: 

… competing creatures were positioned at opposite ends of an open space and a single cube was placed in the middle with the goal of creating competition between the two creatures. Whichever creature got to and maintained control over the cube first won. What was most interesting was how the species developed strategies to counter an opponent's behavior. Some creatures learned to push their opponent away from the cube, while others moved the cube away from their opponents. One of the more humorous approaches was a large creature that would simply fall on top of the cube and cover it up, so its opponent couldn't get to it. Some counterstrategies took advantage of a specific weakness in the original strategy but could be foiled easily in a few generations by adaptations in the original strategy. Others permanently defeated the original strategy.[22]


The power of Sims's artificial evolution is its ability to come up with solutions we couldn't otherwise imagine.  Sims states: "When you witness the process, you get to see how things slowly evolve, then quickly evolve, get stuck, and then get going again. Mutation, selection, mutation, selection, mutation, selection -- the process represents the ability to surpass the complexity that we can handle with traditional design techniques. Using the computer, you can go past the complexity we could otherwise handle; you can go beyond equations we can even understand." Sims’s work explores both the concept of evolving emergent “forms” as well as emergent behaviors.[23] 


In more recent work, Sims has introduced the possibility for audience members to interact with the organisms of his world.  In Galapagos, Computer simulated organisms in abstract forms display themselves on twelve monitors. Participants select an organism and consciously choose to let it continue to exist, copulate, mutate and reproduce itself by pressing sensor- equipped foot pedals located in front of the monitors. This is a work in which virtual "organisms" undergo an interactive Darwinian evolution.

Galapagos (1997)

The process in this exhibit is a collaboration between human and machine. The visitors provide the aesthetic information by selecting which animated forms are most interesting, and the computers provide the ability to simulate the genetics, growth, and behavior of the virtual organisms. But the results can potentially surpass what either human or machine could produce alone. Although the aesthetics of the participants determine the results, they are not designing in the traditional sense. They are rather using selective breeding to explore the "hyperspace" of possible organisms in this simulated genetic system. Since the genetic codes and complexity of the results are managed by the computer, the results are not constrained by the limits of human design ability or understanding.[24]

Pablo Funes:  EvoCAD

According to computer scientist Pablo Funes, the new field of Evolutionary Design may open up an unexpected creative role for the computer in CAD (computer aided design).  In a CAD system designed using evolutionary design principles, Funes maintains that  “not only can designs can be drawn (as in CAD), or drawn and simulated (as in CAD+simulation), but (they can also be) designed by the computer following guidelines given by the operator.”  His EvoCAD program successfully combines the theory of evolutionary design with the practical outcomes associated with CAD.

In its initial iteration, the EvoCAD system takes the form of a mini-CAD system to design 2D Lego structures. Some success has also been demonstrated with fully 3D Lego structures (see “Table” structure below, fig. B).  His application allows the user to manipulate Lego structures, and test their gravitational resistance using a simplified structural simulator. It also interfaces to an evolutionary algorithm that combines user-defined goals with simulation to evolve possible solutions for user-defined design problems. The results of the evolutionary process set in motion are sent back to the CAD front-end to allow for further re-design until the desired solution is obtained.

Boiled down to its basics, the process combines a particular genetic representation with various fitness functions in order to create physical simulations that “solve” hypothetical problems. These elements are in turn regulated by a “plain steady-state” genetic algorithm. Funes describes the process of meeting certain design objectives as follows:


To begin an evolutionary run, a starting structure is first received, consisting of one or more bricks, and "reverse-compiled" into a genetic representation that will seed the population. Mutation and crossover operators are applied iteratively to grow and evolve a population of structures. The simulator is run on each new structure to test for stability and load support, needed to calculate a fitness value. The simulation stops when all objectives are satisfied or when a timeout occurs.[25]


This set of techniques allows Funes to create various evolving structures in simulated form. By altering the fitness functions, Fune’s team has successfully evolved and built many different structures, such as bridges, cantilevers, cranes, trees and tables.  While these are not intended as “art” per se, they are highly expressive and non-predictable structures that bring a new twist to the old adage, “form follows function.”  They provide an important benchmark for artists and designers interested in using evolutionary design principles in realizing a new class of graphic and sculptural objects.

Pablo Funes, Evolved Lego structures: cantilevered bridge (a); table, an example of 3D evolution (b); crane, early and final stages (c, d); tree, internal representation and built structure (e, f)



The pendulum has swung to a conservative extreme of late with a reemphasis of high stakes testing and the development of curricular “standards” for educational policy makers.  This is not the place to develop an argument against standards in schools; suffice it to say that demonstrations of competencies resulting from “schooling” are a far cry from real learning. (footnote:  for persuasive essay on what’s wrong with standards in schools see Elliot Eisner. (1998).[26]  Learning is an evolving process that, at its essence, is proven by the ability of the learner to apply received knowledge in multiple and unrelated settings.  Demonstrating this level of competency involves “knowledge transfer”—the concept of transfer being an effective measure of real knowledge—and significant interaction with content, teachers, peers, and discipline specialists.

Among the quotations at the lead of this essay is one by Humberto Maturana, Biologist, Cybernetician, Scientist – and the creator of the  theory of autopoiesis--the concept of continual self renewal (see also Rinaldo above).  For Maturana, autopoiesis is the process by which an organism continously reorganizes its own structure..   This has implications for not only how we interact with our environment, but how we learn. 

Maturana wrote in 1980 that:

"Learning is not a process of accumulation of representations of the environment; it is a continuous process of transformation of behavior through continuous change in the capacity of the nervous system to synthesize it. Recall does not depend on the indefinite retention of a structural invariant that represents an entity (an idea, image or symbol), but on the functional ability of the system to create, when certain recurrent demands are given, a behavior that satisfies the recurrent demands or that the observer would class as a reenacting of a previous one."[27]

Put another way, learning is not about fixed reactions to a static environment, but about the ability of the organism to evolve behaviors that meet the challenges of a constantly changing environment. 

Is it possible for artists and educators to provide students with rich, evolving content—translated into “living curricula” that not only convey essential skills for success, but evolve to meet the ever changing needs of students attempting to develop higher order cognitive skills that can transfer into a variety of settings?

Imagine the ability to interact with a system that not only provided a real time mirror of performance levels (the cognitive equivalent to a computerized rowing machine), but also provided timely feedback.  Now tie this vision of a “smart system” into a distributed network of “learning nodes” that harnessed the power of multiple CPUs and provided opportunities for interactions among affiliates of “learning communities”.  John Laird, at the University of Michigan, an expert in the intersection of artificial life and gaming, has proposed new avenues for gaming that may provide part of the answer.  Laird writes:

Although we have found computer games to be a rich environment for research on human-level behavior representation, we do not believe that the future of AI in games lies in creating more and more realistic arenas for violence. Better AI in games has the potential for creating new game types in which social interactions,not violence, dominate. The Sims9 provides an excellent example of how social interactions can be the basis for an engaging game. Thus, we are pursuing further research within the context of creating computer games that emphasizes the drama that arises from social interactions between

humans and computer characters.[28]


And another example, again referencing artists Christa Sommerer and Laurent Mignonneau who have developed a number of projects expressly for the internet.  They write: 


The Internet nowadays contains more than a billion documents, and the amount of text, image and sound data increases by the minute. One could even argue that the Internet itself is one of the best examples of a complex system. It provides an ideal platform for knowledge discovery, data mining and data retrieval and systems that make use of a dynamic and constantly evolving data base.[29]


In both the localized computer installations and web-based projects realized by Sommerer and Mignonneau, the interaction between multiple participants operating through a common interface represents a reversal of the topology of information dissemination. The pieces are enabled and realized through the collaboration of many participants remotely connected by a computer network.


In an educational setting, this heightened sense of interaction needs to be understood as crucial. Students and instructors alike are capable of both sending and receiving messages across a myriad of pathways. Many educators continue to be stuck in a method of teaching that echoes the structure of the one-way "broadcast"--a concept that harks back to broadcast media such as radio. In the typical lecture the teacher as "source" transmits information to passive "receivers." This notion of a "one-to-many" model that reinforces a traditional hierarchical top-down approach to teaching is at odds with truly democratic exchange. Everyone is a transmitter and a receiver, a publisher and a consumer. In the new information ecology, traditional roles may become reversed--or abandoned. Audience members become active agents in the creation of the new networked/artwork learning community. Teachers spend more time facilitating and "receiving" information than lecturing. Students exchange information with their peers and become adept at disseminating knowledge.  Participant/learners interacting with such systems are challenged to understand that cognition is less a matter of absorbing ready made "truths" and more a matter of finding meaning through iterative cycles of inquiry and interaction. Ironically, this may be what good teaching has always done.


So would we be justified in building a "machine for learning" that does essentially the same thing that good teachers do? One argument is that by designing such systems we are forced to look critically at the current manner in which information is generated, shared, and evaluated. Further, important questions are surfaced such as "who can participate"; "who has access to the information;" and "what kinds of interactions are enabled?" The traditional "machine for learning" (the classroom) with its single privileged source of authority (the teacher) is hardly a perfect model. Most of the time, it is not a system that rewards boundary breaking, the broad sharing of information, or the generation of new ideas. It IS a system that, in general, reinforces the status quo. Intelligent machines such as Rinaldo's Autopoiesis, or Sommerer’s and Mignonneau’s Verbarium can help us to draw connections between multiple forms of inquiry, enable new kinds of interactions between disparate users, and increase a sense of personal agency and self-worth. While intelligent machines will surely be no smarter than their programmers, pedagogical models can be more easily shared and replicated. Curricula (programs for interactions) can be modified or expanded to meet the special demands of particular disciplines or contexts. Most importantly, users are free to interact through the system in ways that are suited to particular learning styles, personal choices, or physical needs.


The power of the networked computing coupled with our understanding of emergent systems and evolutionary computing holds real promise.  The unpredictable nature of the outcomes provides an ideational basis for both art making and art teaching that is less deterministic, less bound in inherited style and method, less totalizing in its aesthetic vision and, perhaps, less about the ego of the individual artist/teacher.  In addition to the mastery of materials and harnessing the powers of the imagination that we expect of the professional artist, our new breed of artist—call her an evolutionary—is equally adept at developing new algorithms, envisioning useful and beautiful interfaces, and managing/collaborating with machines and/or humans exhibiting non-deterministic and emergent behaviors.  Like a horticulturalist who optimizes growing conditions for particular species but is alert to the potential beauty of mutations in evolutionary strains, the evolutionary works to prepare and optimize the conditions for conceptually engaging and aesthetic outcomes.  In order to do this, this new breed of artist must have a fuller understanding of interactivity, an healthy appreciation of evolutionary theory, and a gift for setting into motion emergent behavior.

Because what we are doing is modeling processes and behaviors that more closely approximate the complexity of “real life”—seen as such, we put ourselves in a position of appreciation rather than agents of domination and control.  Interacting in collaboration with our environment and seeking out unexpected outcomes through systems of emergence provide new models for living on a tightly packed but richly diverse planet.

There is no question that the uses of technology outlined here need to be held against the darker realities of life in a hi-tech society. The insidious nature of surveillance and control, the assault on personal space and privacy, the commodification of aesthetic experience, and the ever-widening "digital divide" between the technological “haves and have nots” are constant reminders that technology is a double edged sword.

But there is at least an equal chance that a clearer understanding of the concepts of interaction, evolution, and emergence—enabled by technology—will yield a broader palette of choices from which human beings can come together to create meaning. In watching these processes unfold, educators will surely find new models for learning.


References and Notes

[1]See Pangaro, Paul.


[2]Brecht, George (1989/1966) “Chance Operations,” Esthetics Contemporary, R. Kostelanetz, ed., Buffalo:  Prometheus Books, p. 103.


[3]See  More on the Golem project:  Combining automatic manufacturing techniques with evolutionary computing, the two scientists had found ingenious ways to harness the mathematical principles at the core of “a-life” (artificial life) to a series of computerized mechanical processes.  With the exception of humans snapping motors into locations determined by the computer, the collection of machines utilized for the project performed “as a single manufacturing robot” capable of creating other robots. Over the two plus year course of the project, multiple computers were harnessed to perform the thousands of iterative calculations necessary to find the best configurations for the required tasks.  At one point, over 9000 users had logged into the researcher’s website and were acting as beta testers—each running still more variations on the basic kit of parts and behaviors at the core of the project.  The software generated different designs and methods of movement, creating traits that worked and failed.  Mimicking natural selection’s mantra, “survival of the fittest,” the most promising designs survived and passed their success to future generations. Finally, hundreds of generations later, three robots were manufactured by a rapid prototyping machine.  These machines, eight inch long robots—had evolved surprising and largely unpredictable kinds of locomotive behaviors that enabled them to achieve their genetically programmed destiny:  pulling themselves across a table top or ratcheting their way across a bed of sand. In all computational evolution one or more “parents” (derived from virtually any media) are mutated and/or crossbred to produce a number of "children", which are then selected again. The more advanced systems allow the researcher to assign a "goodness" or “fitness” factor to each child.  The results of this "selection" are then used to produce the next "generation".  While there are countless evolutionary dead-ends in any selection process (be it natural…or unnatural), surprisingly robust and creative outcomes are often achieved.


[4]Lineberry, Heather, Jim Campbell: Transforming Time, Electronic Works 1990-1999, Arizona State University Museum catalogue, 1999.


[5]Johnson, Stephen (2001).  Emergence: The Connected Lives of Ants, Brains, Cities, and Software, New York:  Scribner; ISBN: 068486875X, p. 18.


[6]Hodson, J.R.


[7]Bentley, Peter J. (1999) Evolutionary Design by Computers, Morgan Kaufmann Publishers; ISBN: 155860605X; p. 26.


[8]Whitelaw, Mitchell.




[10]Rieksts, Oskars J.


[11]Dawkins, Richard, The Blind Watchmaker.  See URL:




[13]Todd, Stephen and Latham, William (1992), Evolutionary Art And Computers, Academic Press.






[16]Rokeby, David.


[17]Wagner, E. (1994). In support of a functional definition of interaction. The American Journal of Distance Education, 8(2).


[18]"The feedback loop is perhaps the simplest representation of the relationships between elements in a system, and these relationships are the way in which the system changes. One element or agent (the 'regulator' or control) sends information into the system, other agents act based upon their reception/perception of this information, and the results of these actions go back to the first agent. It then modifies its subsequent information output based on this response, to promote more of this action (positive feedback), or less or different action (negative feedback). System components (agents or subsystems) are usually both regulators and regulated, and feedback loops are often multiple and intersecting (see:  full citation:  Clayton, 1996, Batra, 1990)." See Morgan, Katherine Elizabeth (1999). A systems analysis of education for sustainability and technology to support participation and collaboration. Unpublished Master's Thesis at the University of British Columbia.


[19] Sommerer, Christa and Mignonneau, Laurent, “Modeling the Emergence of Complexity: Complex Systems and Origin of Life Theories Applied to Interactive On-Line Art.” First published in M.A. Bedau, J.S. McCaskill, N.H. Packard and S. Rasmussen, eds., Artificial Life VII: Proceedings of the Seventh International Conference (Cambridge, MA:  MIT Press, 2000).   See also:  URL:


[20]Huhtamo, Eriiki, URL to Finnish exhibition. Huhtamo, Eriiki, URL to Finnish Museum of Contemporary Art:


[21]Rinaldo, Ken: Autopoiesis (2000).


[22]Holtzman, Stephen.






[25]Funes, Pablo. EvoCAD site:


[26]Eisner, Elliot. (1998) “Standards for American Schools:  Help or Hindrance?” The kind of schools we need.  New York:  Heinemann.  Chapter 14:  pp. 175 – 187.






[29]Sommerer, Christa and Mignonneau, Laurent, op. cit.



copyright 2002  DAN COLLINS