"Life
is computation. Every single living cell reads information from a memory,
re-writes it, receives data input (information about the state of its
environment), processes the data and acts according to the results of
all this computation. Globally, the zillions of cells populating the biosphere
certainly perform more computation steps per unit of time than all man
made computers put together."
T. Gram§, S. Bornholdt, M. Gro§, M. Mitchell, and T. Pellizzari
Molecular
Computation
"...the attempt to recreate biological phenomena in alternative media will result in not only better theoretical understanding of the phenomena under study, but also in practical applications of biological principles in the technology of computer hardware and software, mobile robots, spacecraft, medicine, nanotechnology, industrial fabrication and assembly, and other vital engineering projects. By extending the horizons of empirical research in biology beyond the territory currently circumscribed by life-as-we-know-it, the study of Artificial Life gives us access to the domain of life-as-it- could-be, and it is within this vastly larger domain that we must ground general theories of biology and in which we will discover practical and useful applications of biology in our engineering endeavors. -- Chris G. Langton
tom ray's hammer: emergence and excess in a-life art mitchell whitelaw
Flocking is a particularly evocative example of emergence: where complex global behavior can arise from the interaction of simple local rules. In the boids model (and related systems like the multi-agent steering behavior demos) interaction between simple behaviors of individuals produce complex yet organized group behavior. The component behaviors are inherently nonlinear, so mixing them gives the emergent group dynamics a chaotic aspect. At the same time, the negative feedback provided by the behavioral controllers tends to keep the group dynamics ordered. The result is life-like group behavior.
On-line limited version: Morph Lab
TURBULENCE, Jon McCormack, is a menagerie of synthesised forms, `evolved' within the computer using a process of artificial selection.
Animaland vy Alan Dorin
Iconica, by Troy Innocent. A living artificial world made of language.
Cellular Automata and Self-replicating Code
The 'artificial' in Artificial Life refers to the component parts, not the emergent processes. If the component parts are implemented correctly, the processes they support are genuine -- every bit as genuine as the natural processes they imitate. The big claim is that a properly organized set of artificial primitives carrying out the same functional roles as the biomolecules in natural living systems will support a process that will be 'alive' in the same way that natural organisms are alive. Artificial life will therefore be genuine life -- it will simply be made out of different stuff than the life that evolved here on earth.
Chris Langton quoted p. 15, 'Virtual Organisms' by Mark Ward
Artificial life represents a form of 'synthetic biology'. -- a biology which has come to reside in and be created from silicon(chips). Alife is at the meeting of various disciplines such as communications theory, computation, artificial intelligence, physics and mathematics of nonlinear systems, and theoretical biology. Naturally, all these areas have felt some impact from artificial life research: for example, Alife helps us say more meaningful things about life in general. Alife experiments first began with questions about how life arose in the first place. Theoretical biologist, Steve Kauffman, for example, conducted research to show how stable chemical compounds could arise out of earth's 'primal soup'. Other researchers, such as Everett Shock, have hypothesized that life arose around the chemical-rich undersea volcanic vents. Experiments with vent chemical have modeled evidence that supports this theory. However, none of this research has convincingly demonstrated how organised life came to exist. Alife researcher, Andrew Pargellis, on the other hand, has succeed in convincing 'life' to emerge from a 'pre-biotic soup' of computer code.
If you want to understand life - build it. If you want to prove a theory - use it. That's what separates artificial life (Alife) from other theories of life, it's about constant demonstration and experimentation. You'll find Alife can be applied to any system, from politics to blood.
Stewart Dean, Artificial Life - an Introduction
Alife has brought fresh insight to less obvious disciplines such as sociology, anthropology and history. A project at Santa Fe Institute examined why the sophisticated Anasazi Indians, the ancient occupants of lands now settled in part by the Peublo Indians, could have abandoned their lands and homes and all but disappeared within the space of fifty years. Based on archeological evidence, the lives of the Anasazi were modeled on computer within a synthetic landscape. Each of the digital Anasazi 'live' their lives engaged in analogues of same activities as their carbon-based, whilst researchers run weather and other simulations on their environment.
The Anasazi Project program is a sophisticated version of an older and much simpler program called 'cellular automata'.
The person to first suggest the possibility of cellular automata was John von Neumann. The brilliant mathematician had a long-standing interest in the connections between logic and biology that dates back to his work on ENIAC over 1944-5. In 1948 he attempted to demonstrate a mathematical theory which would allow biological and artificial information systems to be compared, in a series of lectures called 'General and Logical Theory of Automata'. Vopn Neumann's lectures contained several fundamental insights which have profoundly influences Alife research.
- 1. It is manipulation of information which keeps an organism alive and allows it to beat back entropy: Life is a process not a property. Mistakes in copying information lead to mutation which, if beneficial, lead to increased survival and reproduction rates.
- 2. A high level of complexity would be needed before a machine could be made that would be able to create machines more complex than itself.
- 3.
That one of the characteristics that separate machines from living things
is that machines must be built; if a machine was to be lifelike it would
have to be able to reproduce.
sourced from 'Virtual Organisms' by Mark Ward.
Whilst von Neumann saw his theorised self-replicating robot simply as a thought experiment, his colleague Stanislav Ulam saw a way for a reproducing automata to live after a fashion.
He suggested an array of square cells like a giant draughtsboard which was 'programmed' with laws that would make the system appear alive. Von Neumann used this starting point to produce a blueprint for a unwieldy cellular automata which he presented publicly in 1953. Von Neumann died before his blueprint could be implemented and tested. It wasn't until 1995, when a graduate student, Umberto Pesavento, had the idea to reproduce von Neumann's automata on computer that it was actually produced and tested.
Research into Alife and cellular automata (CA) languished until 1970 when mathematician John Conway turned his attention to von Neumann's proposal. One of the first things he observed was that it needed to be vastly simplified to be useful and accessible. In addition, Conway had long been fascinated by games and he wanted to see if a game could be made and played from cellular automata.
Conway's 'Game of Life' which he produced after two years of experimentation, is deceptively simple. If an empty or 'dead' square on a grid had three of its adjoining eight squares filled, then it comes alive'.
Cellular Automata includes flash version of LIFE.
The game is unlike most games in that the players have little part in determining the outcome beyond setting the initial conditions. What makes The Game of Life so compelling is its unpredictability; the simplest scattering of starter cells can generate surprising and complex forms. Some lifeforms never change, others are known as 'blinkers' or 'oscillators' because, although stable, they go through a persistent cycle of forms. Others are mobile and move across the grid. The classic example is the 'glider'.
Conway believed that the same rules determined what happened to generations of organisms in our universe; that recursive instructions reiterated since the beginning of time had given rise to ships and beehives in biological life as well as on the screen.
Taking the 'simplexity' principle one step further, one of the simplest cellular automata is 'Langton's Ant', named after its inventor, Christopher Langton. The 'ant' effects the state of the world each iteration by moving forwards one square.
- If it is currently on a black cell the ant turns 90 degrees to the left
- If it is currently on a white cell the ant turns 90 degrees to the right
- As it moves to the next square, the one that it is on changes colour from white to black, or visa versa
On the face of it Langton's ant would seem to be a simple animal - the rules are less than complex. In fact the ant displays behavior that is currently baffling mathematicians. The ant can spontaneously start to build a "highway", or display many interesting pattern forming behaviors.
Tierra, designed by Tom Ray, is an artificial life world where a population of programs, some of them with the ability to self-replicate, compete for memory space and CPU time on their host machine. The ones that manage to replicate more, get more of these resources, thus guaranteeing their survival. Various design features of the programs may change during replication, which results in their being subjected to an evolutionary process, giving rise to various ecological phenomena, such as parasitism among the programs. Diverse ecological communities have emerged. These digital communities have been used to experimentally examine ecological and evolutionary processes: e.g., competitive exclusion and coexistence, host/parasite density dependent population regulation, the effect of parasites in enhancing community diversity, evolutionary arms race, punctuated equilibrium, and the role of chance and historical factors in evolution. This evolution in a bottle may prove to be a valuable tool for the study of evolution and ecology.
Tierra is a major landmark in artificial life research because of what it has been able to demonstrate regarding evolution in an artificial medium. Tierra is very likely the most widely known artificial life system in the world. Tom Ray is a tropical biologist who, for more than 20 years, studied the evolution and ecology of a variety of organisms inhabiting rain forests, mostly in Costa Rica, where he is actively engaged in rain forest conservation. Although having no formal training in computer science, he taught himself C programming and started to pursue his interest in synthetic life, which culminated with the development of Tierra. Ray notes that
"Evolution just naturally comes up with useful things". He argues for the proposal in the following way: you couldn't imagine a silk worm, even if you could, you couldn't guide evolution to make it. But evolution did make it, we can take it, cross breed it, neuter it, delete its poisonous properties, domesticate it."
(a)
The Digital Environment: Self-replicating computer programs (colored geometric
objects) occupy the RAM memory of the computer (orange background). Mutations
(lightning) cause random changes in the code. Death (the skull) eliminates
old or defective programs.
(b)
The Ancestral Program, a self-replicating program that is used to start
up the system. It consists of three ``genes'' (green solid objects). The
CPU (green sphere) is executing code in the first gene, which causes the
program to measure itself.
(c)
A Parasite (blue, two piece object) uses its CPU (blue sphere) to execute
the code in the third gene of a neighboring host organism (green) to replicate
itself, producing daughter parasite (two-piece wire frame object).
(d)
A Hyper-parasite (red, three piece object) steals the CPU from a parasite
(blue sphere). Using the stolen CPU, and its own CPU (red sphere) it is
able to produce two daughters (wire frame objects on left and right) simultaneously.
Figure 1: A visualization of Tierra (text and picture credits to Anti-Gravity Workshop)
The same principle of profound effects from simple programming led to Craig Reynold's development of 'Boids'
"The motion of a flock of birds is... simple in concept yet is so visually complex it seems randomly arrayed and yet is magnificently synchronous. Perhaps most puzzling is the strong impression of intentional centralised control. Yet all evidence indicates that flock motion must be merely the aggregate result of the actions of individual animals, each acting solely on the basis of its local perception of the world." - Craig Reynolds
Reynold's theory suggested that flocking could be modeled by allowing each individual in the simulation to apply a few simple rules. After spending several hours in a local cemetery watching flock of blackbirds, Reynolds came up with three primary rules':
- 1.A CLUMPING force that kept the flock together
- 2. An ability to MATCH VELOCITY so that the birds in the flock would move at the same speed
- 3.A SEPARATION force that prevented birds from getting too close to each other
He implemented these rules for creatures in his computer which he named "boids". The observations and actions of these boids were entirely local. As they flew, they would notice what their neighbors were doing - as though they were cells in a cellular automaton - and apply that information to their own actions in the next time step. Reynolds used his program in a colleague's animation project to control the behavior of flocking birds and schooling fish. The boids, operating solely by those three simple rules, could flock in large configurations so convincingly that ornithologists, feeling that real birds might be performing the same algorithms as Reynolds boids, began calling the animator to find out his rules.
Applet showing Boids in motion
At the beginning of each run, the boids, which appeared like line drawings of paper planes, would quickly move together. A stable configuration would be maintained at the centre of the flock, which the boids at the edges regulated their speed to stay within the group.
Then Reynolds tried some runs in which the flock encountered obstacles in the form of thick cylinders resembling Grecian columns. The flock simply parted at the column, with the boids turning well before they reached the obstacle - a remarkable response, with which they were not programmed. After veering around the cylinder, the flock would reunite -- a genuine emergent property.
Such an approach has paid big dividends in animating effects for cinema. For example, Jurassic Park used a similar flocking algorithm to simulate the movement of a stampede of small dinosaurs. Simple properties attached to a digital object provided the flight path for the feather at the beginning of Forrest Gump.
Simple rules also generated Richard Dawkin's The Blind Watchmaker. These rules are called genetic algorithms. Genetic algorithms are based on Darwinian models. Invention happens after initial design. Surprises often result. Genetic algorithms are designed to take advantage of lucky mutations. They are serendipity engines.
On-line limited version: Morph Lab
Genetic algorithms have also been shown to enhance the nonlinear design subprocesses of "explore, evaluate, and refine," for architectural design and art.
The virtual creatures of Karl Sims are the most celebrated examples from the growing zoology of artificial life entities entering the scene. Sims has developed a comprehensive model for defining a creature genetically, using the genetic programming paradigm. Sims's virtual creatures incorporate deep physics and 3D shaded modeling.
Images from Panspermia 1990
Images from Gal‡pagos 1997. Both images above show a "parent" in the upper left corner, and the remaining 11 are "offspring" from that parent. Mutations cause various differences between the offspring and their parents. .
See also Australian artists Jon McCormack, Alan Dorin and Troy Innocent.
Steering Behaviors For Autonomous Characters Craig W. Reynolds
Real Time Virtual Humans Norman I. Badler, Rama Bindiganavale, Juliet Bourne, Jan Allbeck, Jianping Shi, and Martha Palmer
Future?
digital actors
Genetic algorithms may be good candidates as aids for prototyping in the design of animated motion.
A new form of animated character is emerging, not from within the film industry but from within various research labs. Add to Disney's "Illusion of Life," the "Simulation of Life," and witness a new technology - one which is compatible with the goals of Virtual Reality - future cyberspaces in which characters are not just animated, they are autonomous, reactive agents as well. These characters might achieve their behaviors, in all their complexity and subtlety, by adapting to their environments and each other, 'designing themselves,' not through animation scripting, but through reproduction, mutation, and natural or aesthetic selection.
Digital actors have applications to video games, movies, simulation and training, manufacturing, animated web pages, etc.
See, for example, Jack: Jack is a software package developed at the Center for Human Modeling and Simulation at the University of Pennsylvania. Jack provides a 3D interactive environment for controlling articulated figures. It features a detailed human model and includes realistic behavioral controls, anthropometric scaling, task animation and evaluation systems, view analysis, automatic reach and grasp, collision detection and avoidance, and many other useful tools for a wide range of applications.
Smaller computers
CA may help computer processors smaller, more powerful and faster. Currently the smallest components on a computer chip are about 0.35 microns across. Whilst chip-makers predict that they will be able to go down to about 0.1 micron, this is the absolute limit. Smaller than this and quantum effects start making themselves felt. Now some researchers are suggesting the use of quantum cellular automata to encode information in protons. The basic operating principle of QCA is identical to that of ordinary CA: the state of the four aluminum dots that make up the QCA basic element, determine what happens next. QCAs can mimic all the operations of Boolean logic used in programming.
Conclusion
I'll leave the last word to Simon Penny
Of course, you can breed anything, a physics, a joke, if you establish the payoffs and taxes, the right rugged fitness landscape and manipulate your mutation rate to avoid becoming stranded on local fitness peaks. You could have a self evolving user interface, in which the environment it adapted to was the habit and interests of the individual user. Such an interface might hide little used functions in sub-menus or mutate a new floating palette. Use of the palette would reward that mutation, allowing its progeny a better rate of survival in the next generation. This could be fun, you'd never know quite what you interface would look like, though I guess the mutation would be fairly conservative, given the trained and habit bound nature of user activity. Radically novel mutations would be nipped in the bud because the user wouldn't understand them.
One great value of Alife to artists, as I see it, is that it offers an alternative to the current all too deterministic paradigm of interactivity as pre-set responses to user navigation through an ossified database. This paradigm is firmly within the Top-Down camp. Emergent interactive behavior would not be derived from a map of preset responses. Rather, as in the Subsumption architecture of Rodney Brooks or more radically in the PDL system of Luc Steels, behaviors would arise from the context the system found itself in. In robotic terms, no mapping would occur, the world would be the map. In subsumption architecture, as in the child psychological examples cited by Hendriks Jansen, and the behaviors of termites described by Grassˇ, behaviors arise through what we might characterize as a contingent and unconnected chain of triggers. In the case of the termites, highly complex building behavior arises when a simple behavior produces an action or a product which then triggers a higher level behavior. [19] This is a new paradigm of interactivity, radically different from the notion of a pre-linked database. Simply regarding this method as a possibility points up the presence of deterministic top down strategies in current interactive art practice.
New scientific ideas powerfully inform the value systems and world view of our culture. New technologies are almost always clad in utopian rhetoric. Any technology which is trumpeted as a 'liberation' should be examined extra-carefully. Historically, those technologies have transpired to be the most oppressive. Artists must be careful not to unconsciously endorse the paradigms hidden in scientific discourses, where they often lie hidden, disguised as axioms.
'Virtual Organisms' by Mark Ward.
MESHWORKS, HIERARCHIES AND INTERFACES by Manuel De Landa
An Introduction to the Science of Artificial Intelligence
Craig Reynold's Homepage has great links for a variety of other sources of information, software and projects about flocking behaviors.
Macintosh
Artificial Life Software for download:
apart from the fact that many of the download links don't currently work,
this is a great site for Alife shareware programs
Kasprzyk's
Alife Page Artificial Life for the Macintosh
another
great site for Alife shareware from the writer of many notable examples
including Concha
Artificial Life Games Homepage.
Other On-line CA-derived artworks
The sea
animals in A-Volve
by Christa Sommerer and Laurent Mignonneau have an autonomous existence,
too. In A-Volve the visitors create little sea creatures with which they
can then interact in a large water basin. The individual virtual creatures
react very differently to the hand movements of the visitors. Some can
be attracted, others try to flee from the hands. As their modes of behavior
are very difficult to find out, they create free play for the visitors
who start to ascribe individual characteristics to the various animals.
The interface Sommerer and Mignonneau worked with has completely lost
its technoid character. This idea was already employed by the artists
in their 1992 work Interactive Plant Growing. Here the reaching for real
plants causes the growth of computer-generated plants on a projection
screen. Sommerer and Mignonneau draw the consequences from the increasing
control computer technology has over our environment. To them the so-called
artificial and the natural world do not oppose each other, but are closely
interconnected areas. In dealing with these areas a sensibility is required
that has to be partly re-learned, partly found anew.
Home
Page Christa Sommerer & Laurent Mignonneau
"Concha"
- an application by Alex Kasprzyk based on an idea outlined by Richard
Dawkins. Concha is a simple program which enables shell-like images to
be evolved. By selecting a "shell", mutant offsprings will be created
using the parent shell's genetic code. This genetic code consists of four
parameters: * the rate at which the spire expands * the distance from
the centre of the spire to the inner margin of the tube of the shell *
the vertical displacement of one spire above another * the diameter of
the spire A wide variety of shapes can be formed by adjusting these values
over time through selective breeding. The results are often amazingly
beautiful images which bear striking resemblances to shells commonly found
in nature.
Kasprzyk's
Alife Page Artificial Life for the Macintosh
Evolutionary
3-D Images As described in their book Evolutionary Art and Computers,
William Latham and Stephen Todd have developed a system where a family
of hand-designed images are interactively evolved into new shapes and
organic forms.
Evolutionary
3-D Images
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