The idea of human artifacts being given life has fascinated humankind
for as long as people have been recording their myths and stories.
Whether Pygmalion or Frankenstein, humanity has been fascinated with the idea of artificial life.
Pre-computer
Automatons were quite a novelty. In the days before computers and electronics, some were very sophisticated, using pneumatics, mechanics, and hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization.
Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system.
The duck could reportedly eat and digest, drink, quack, and splash in a
pool. It was exhibited all over Europe until it fell into disrepair.
However, it wasn't until the invention of cheap computing power that artificial life
as a legitimate science began in earnest, steeped more in the
theoretical and computational than the mechanical and mythological.
1950s–1970s
One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann. At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California
in the late 1940s, von Neumann delivered a lecture titled "The General
and Logical Theory of Automata." He defined an "automaton" as any
machine whose behavior proceeded logically from step to step by
combining information from the environment and its own programming, and
said that natural organisms would in the end be found to follow similar
simple rules. He also spoke about the idea of self-replicating machines. He postulated a machine – a kinematic automaton
– made up of a control computer, a construction arm, and a long series
of instructions, floating in a lake of parts. By following the
instructions that were part of its own body, it could create an
identical machine. He followed this idea by creating (with Stanislaw Ulam)
a purely logic-based automaton, not requiring a physical body but based
on the changing states of the cells in an infinite grid – the first cellular automaton.
It was extraordinarily complicated compared to later CAs, having
hundreds of thousands of cells which could each exist in one of
twenty-nine states, but von Neumann felt he needed the complexity in
order for it to function not just as a self-replicating "machine", but
also as a universal computer as defined by Alan Turing. This "universal constructor"
read from a tape of instructions and wrote out a series of cells that
could then be made active to leave a fully functional copy of the
original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.
Homer Jacobson
illustrated basic self-replication in the 1950s with a model train set –
a seed "organism" consisting of a "head" and "tail" boxcar could use
the simple rules of the system to consistently create new "organisms"
identical to itself, so long as there was a random pool of new boxcars
to draw from.
Edward F. Moore
proposed "Artificial Living Plants", which would be floating factories
which could create copies of themselves. They could be programmed to
perform some function (extracting fresh water, harvesting minerals from
seawater) for an investment that would be relatively small compared to
the huge returns from the exponentially growing numbers of factories. Freeman Dyson
also studied the idea, envisioning self-replicating machines sent to
explore and exploit other planets and moons, and a NASA group called the
Self-Replicating Systems Concept Team performed a 1980 study on the
feasibility of a self-building lunar factory.
University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s. He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.
1970s–1980s
Philosophy scholar Arthur Burks,
who had worked with von Neumann (and indeed, organized his papers after
Neumann's death), headed the Logic of Computers Group at the University of Michigan. He brought the overlooked views of 19th century American thinker Charles Sanders Peirce
into the modern age. Peirce was a strong believer that all of nature's
workings were based on logic (though not always deductive logic). The
Michigan group was one of the few groups still interested in alife and
CAs in the early 1970s; one of its students, Tommaso Toffoli
argued in his PhD thesis that the field was important because its
results explain the simple rules that underlay complex effects in
nature. Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.
Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC
mainframes for a hospital. He became enthralled by Conway's Game of
Life, and began pursuing the idea that the computer could emulate living
creatures. After years of study (and a near-fatal hang-gliding
accident), he began attempting to actualize Von Neumann's CA and the
work of Edgar F. Codd,
who had simplified Von Neumann's original twenty-nine state monster to
one with only eight states. He succeeded in creating the first
self-replicating computer organism in October 1979, using only an Apple II
desktop computer. He entered Burks' graduate program at the Logic of
Computers Group in 1982, at the age of 33, and helped to found a new
discipline.
Langton's official conference announcement of Artificial Life I
was the earliest description of a field which had previously barely
existed:
Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.
Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.
Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett.
This group created a computer especially designed to execute cellular
automata, eventually reducing it to the size of a single circuit board.
This "cellular automata machine" allowed an explosion of alife research
among scientists who could not otherwise afford sophisticated
computers.
In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity
displayed by one-dimensional CAs, and showed how they applied to
natural phenomena such as the patterns of seashells and the nature of
plant growth.
Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.
Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program
in 1987 to animate groups of boids. With no top-down programming at
all, the boids produced lifelike solutions to evading obstacles placed
in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies
attempt to find more realistic and inexpensive ways to animate natural
forms such as plant life, animal movement, hair growth, and complicated
organic textures.
J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum
was leaving. Farmer and Norman Packard chaired a conference in May
1985 called "Evolution, Games, and Learning", which was to presage many
of the topics of later alife conferences.
2000s
On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s resulting in theories of kin selection, reciprocity, multilevel
selection and cultural group selection) was re-introduced via artificial
life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory
has been utilized in similar investigation, however, that approach was
deemed to be rather limiting in its amount of possible strategies and
debatable set of payoff rules. The alife model designed here, instead,
is based upon Conway's Game of Life but with much added complexity (there are over 101000
strategies that can potentially emerge). Most significantly, the
interacting agents are characterized by external phenotype markers which
allows for recognition amongst in-group members. In effect, it is shown
that given the capacity to perceive these markers, agents within the
system are then able to evolve new group behaviors under minimalistic
assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.
For the setup, this two-dimensional artificial world is divided
into cells, each empty or containing a resource bundle. An empty cell
can acquire a resource bundle with a certain probability per unit of
time and lose it when an agent consumes the resource. Each agent is
plainly constructed with a set of receptors, effectors (the components
that govern the agents' behavior), and neural net which connect the two.
In response to the environment, an agent may rest, eat, reproduce by
division, move, turn and attack. All actions
expend energy taken from its internal energy storage; once that is
depleted, the agent dies. Consumption of resource, as well as other
agents after defeating them, yields an increase in the energy storage.
Reproduction is modeled as being asexual while the offspring receive
half the parental energy. Agents are also equipped with sensory inputs
that allow them to detect resources or other members within a parameter
in addition to its own level of vitality. As for the phenotype markers,
they do not influence behavior but solely function as indicator of
'genetic' similarity. Heredity is achieved by having the relevant
information be inherited by the offspring and subjected to a set rate of
mutation.
The objective of the investigation is to study how the presence
of phenotype markers affects the model's range of evolving cooperative
strategies. In addition, as the resource available in this 2D
environment is capped, the simulation also serves to determine the
effect of environmental carrying capacity on their emergence.
One previously unseen strategy is termed the "raven". These
agents leave cells with in-group members, thus avoiding intra-specific
competition, and attack out-group members voluntarily. Another strategy,
named the 'starling', involves the agent sharing cells with in-group
members. Despite individuals having smaller energy storage due to
resource partitioning, this strategy permits highly effective defense
against large invaders via the advantage in numbers. Ecologically
speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.
In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality
by showing that within the alife framework it is possible to "model not
only how one strategy displaces another, but also the very process by
which new strategies emerge from a large quantity of possibilities".
Work is also underway to create cellular models of artificial life.
Initial work on building a complete biochemical model of cellular
behavior is underway as part of a number of different research projects,
namely Blue Gene which seeks to understand the mechanisms behind protein folding.
