(c) by Thomas S. Ray (ray@hip.atr.co.jp, ray@udel.edu), ART
Human Information Processing Research Laboratories, 2-2
Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-02, Japan.
(Ripped from "Artificial Life -- an overview" by Christopher G.
Langton (ed.) MIT Press (1995), 340 pp., ISBN 0-262-12189-1 .
All typos entirely my own. Please buy the treekiller (=processed
cellulose cum pigments + additives) version, should it still be
available. Worthwhile to note, that one _can_ arrive at
diametrally differing conclusions while investigating the same
set of facts. Bitrate's too low (he's somewhat loquacious),
furthermore there is imo some amount of muddled thinkin'.
Nevertheless on the whole extremely readable. Paricularly the
3.1 passage is very vividly done).
Keywords -- evolution, ecology, synthesis, parallel computation,
multi-cellularity, complexity, diversity
Abstract -- Our concepts of biology, evolution and complexity
are constrained by having observed only a single instance of
life, life on earth. A truly comparative biology is needed to
extend these concepts. Because we cannot observe life on other
planets, we are left with the alternative of creating Artificial
Life forms on earth. I will discuss the approach of inoculating
evolution by natural selection into the medium of the digital
computer. This is not a physical/chemical medium; it is a
logical/informational medium. Thus, these new instances of
evolution are not subject to the same physical laws as organic
evolution (e.g. the laws of thermodynamics) and exist in what
amounts to another universe, governed by the "physical laws" of
the logic of the computer. This excercise gives us a broader
perspective on what evolution is and what it does.
An evolutionary approach to synthetic biology consists of
inoculating the process of evolution by natural selection into
an artificial medium. Evolution is then allowed to find the
natural forms of living organisms in the artificial medium.
These are not models of life, but independant instances of life.
This essay is intended to communicate a way of thinking about
synthetic biology that leads to a particular approach: to
understand and respect the natural form of the artificial
medium, to facilitate the process of evolution in generating
forms that are adapted to the medium, and to let evolution find
forms and processes that naturally exploit the possibilities
inherent to the medium. Examples are cited of synthetic biology
embedded in the computational medium, where in addition to being
an exercise in experimental comparative evolutionary biology, it
is also a possible means of harnessing the evolutionary process
for the production of complex computer software.
1 Synthetic Biology
Articial Life (AL) is the enterprise of understanding biology by
constructing biological phenomena out of artificial components,
rather than breaking natural life forms down into their
component parts. It is the synthetic rather than the
reductionist approach. I will describe an approach to the
synthesis of artificial living forms that exhibit natural
evolution.
The umbrella of AI is broad and covers three principal
approaches to synthesis: in hardware (e.g. robotics,
nanotechnology), in software (e.g., replicating and evolving
computer programs), and in wetware (e.g., replicating and
evolving organic molecules, nucleic acids, or others). This
essay will focus on software synthesis, although it is hoped
that the issues discussed will be generalizable to any synthesis
involving the process of evoluton.
I would like to suggest that software syntheses in AL could be
divided into two kinds: simulations and instantiations of life
processes. AL simulations represent an advance in biological
modeling, based on a bottom-up approach, which has been made
possible by the increase of available computational power. In
the older approaches to modeling of ecological or evolutionary
phenomena, systems of differential equations were set up that
expressed relationships between covarying quantities of entities
(i.e., genes, alleles, individuals, or species) in the
populations or communities.
The new bottom-up approach creates a population of data
structures, with each instance of the data structure
corresponding to a single entity. These structures contain
variables defining the state of an individual. Rules are defined
as to how the individuals interact with one another and with the
environment. As the simulation runs, populations of these data
structures interact according to local rules, and the global
behaviour of the system emerges from these interactions. Several
very good examples of bottom-up ecological models have appeared
in the AL literature [33,91]. However, ecologists have also
developed this same approach independently of the AL movement
and have called the approach "individual-based" models [19,39].
The second approach to software synthesis is what I have called
instantiation rather than simulation. In simulation, data
structures are created that contain variables that represent the
states of the entities being modeled. The important point is
that in simulation, the data in the computer is treated as a
representation of something else, such as a population of
mosquitoes or trees. In instantiation, the data in the computer
does not represent anything else. The data patterns in an
instantiation are considered to be living forms in their own
right and are not models of any natural life form. These can
form the basis of a comparative biology [57].
The object of an AL instantiation is to introduce the natural
form and process of life into an artificial medium. This results
in an AL form in some medium other than carbon chemistry and is
not a model of organic life forms. The approach discussed in
this essay involves introducing the process of evolution by
natural selection into the computatonal medium. I consider
evolution to be the fundamental process of life and the
generator of living form.
2 Recognizing Life
Most approaches to defining life involve assembling a short set
of properties of life and then testing candidates on the basis
of whether or not they exhibit the properties on the list. The
main problem with this approach is that there is disagreement as
to what should be on the list. My private list contains only two
items: self-replication and open-ended evolution. However, this
reflects my biases as an evolutionary biologist.
I prefer to avoid the semantic argument and take a different
approach to the problem of recognizing life. I was led to this
view by contemplating how I would regard a machine that
exhibited conscious intelligence at such a level that it could
participate as an equal in a debate like this. The machine would
meet neither of my two main criteria as to what life is, yet I
don't feel that I could deny that the process it contained was
alive.
This means that there are certain properties that I consider to
be unique to life and whose presence in a system signify the
existance of life in that system. This suggests an alternative
approach to the problem. Rather than create a short list of
minimal requirements and test whether a system exhibits all
items on the list, one could create a long list of properties
unique to life and test whether a system exhibits _any_ item on
the list.
To this softer, more pluralistic approach to recognizing life,
the objective is not to determine if the system is alive or not
but to determine if the system exhibits a "genuine" instance of
some property that is a signature of living systems (e.g.,
self-replication, evolution, flocking, consciousness).
Whether we consider a system living because it exhibits some
property that is unique to life amounts to a semantic issue.
What is more important is that we recognize that it is possible
to create disembodied but genuine instances of specific
properties of life in artificial systems. This capability is a
powerful research tool. By separating the property of life that
we choose to study from the many other complexities of natural
living systems, we make it easier to manipulate and observe the
property of interest. The objective of the approach advocated in
this paper is to capture genuine evolution in an artificial
system.
3 What Natural Evolution Does
Evolution by natural selection is a process that enters into a
physical medium. Through iterated replication with selection of
large populations through many generations, it searches out the
possibilities inherent in the "physics and chemistry" of the
medium in which it is embedded. It exploits any inherent
self-organizing properties of the medium in which it is
embedded. It exploits any inherent self-organization of the
medium and flows into natural attractors realizing and fleshing
out their structure.
Evolution never escapes from its ultimative imperative:
self-replication. However, the mechanisms that evolution
discovers for achieving this ultimative goal gradually become so
convoluted and complex that the underlying drive can seem to
become superfluous. Some philosophers have argued that the
evolutionary theory as expressed by the phrase "survival of the
fittest" is tautological, in that the fittest are defined as
those that survive to reproduce. In fact, fitness is achieved
through innovation in engineering of the organism [81]. However,
there remains something peculiarly self-referential about the
whole enterprise. There is some sense in which life may be a
natural tautology.
Evolution is both a defining characteristic and the creative
process of life itself. The living condition is a state that
complex physical systems naturally flow into under certain
conditions. It is a self-organizing, self-perpetuating state of
autocatalytically increasing complexity. The living component of
the physical system quickly becomes the most complex part of the
system, such that it reshapes the medium in its own image. Life
then evolves adaptations predominantly in relation to the living
components of the system, rather than the nonliving components.
Life evolves adaptations to itself.
3.1 Evolution in Sequence Space
Think of organisms as occupying a "genotype space" consisting of
all possible lengths of the elements of genetic system (i.e.,
nucleotids or machine instructions). When the first organism
begins replicating, a single self-replicating creature, with a
single sequence of certain length, occupies a single point in
the genotype space. However, as the creature replicates in the
environment, a population of creatures forms, and errors cause
genetic variation, such that the population will form a cloud of
points in the genotypes centered around the original point.
Because the new prototypes that form the cloud are formed by
random processes, most of them are completely inviable and die
without reproducing. However, some of them are capable of
reproduction. These new genotypes persist, and, because some of
them are affected by mutation, the cloud of points spreads
further. However, not all of the viable genomes are equally
viable. Some of them discover tricks to replicate more
efficiently. These genotypes increase in frequency, causing the
population of the creatures at the corresponding points of the
genotype space to increase.
Points in the genotype space occupied by greater populations of
individuals will spawn larger number of mutant offspring; thus
the density of the cloud of points in the genotype space will
shift gradually in the direction of the more fit genotypes. Over
time, the cloud of points will percolate through the genotype
space, either expanding outward as a result of random drift or
by flowing along fitness gradients.
Most of the volume of this space represents completely inviable
sequences. These regions of the space may be momentarily and
sparsely occupied by inviable mutants, but the cloud will never
flow into the inviable regions. The cloud of genotypes may
bifurcate as it flows into habitable regions in different
directions, and it may split as large genetic changes spawn
genotypes in distant but viable regions of the space. We may
imagine that the evolving population of creatures will take the
form of wispy clouds flowing through this space.
Now imagine for a moment the situation that there was no
selection. This implies that every sequence is replicated at an
equal rate. Mutation will cause the cloud of points to expand
outward, eventually filling the space uniformly. In this
situation, the complexity of the structure of the cloud of
points does not increase through time, only the volume that it
occupies. Under selection by contrast, through time the cloud
will take on an intrinsicate structure as it flows along fitness
gradients and percolates by drift through narrow regions of
viability in a largely uninhabitable space.
Consider that the viable region of the genotype space is a very
small subset of the total volume of the space, but that it
probably exhibits a very complex shape, forming tendrils and
sheets sparsely permeating an otherwise empty space. The complex
structure of this cloud can be considered to be a product of
evolution by natural selection. This thought experiment appears
to imply that the intricate structure that the cloud of
genotypes may assume through evolution is fully deterministic.
Its shape is predefined by the physics and chemistry and the
structure of the environment, in much the same way that the form
of the Mandelbrot set is predetermined by its defining equation.
The complex structure of this viable space is inherent in the
medium and is an example of "order for free" [44].
No living world will ever fill the entire viable subspace,
either at a single moment of time, or even cumulatively over its
entire history. The region actually filled will be strongly
influenced by the original self-replicating sequence and by
stochastic forces that will by chance push the cloud down a
subset of possible habitable pathways. Furthermore, coevolution
and ecological interactions imply that certain regions can only
be occupied when certain other regions are also occupied. This
concept of the flow of genotypes through the genotype space is
essentially the same as that discussed by Eigen [22] in the
context of "quasispecies". Eigen limited his discussion to
species of viruses, where it is also easy to think of sequence
spaces. Here, I am extending the concept beyond the bounds of
the species to include entire phylogenies of species.
3.2 Natural Evolution in an Artifical Medium
Until recently, life has been known as a state of matter,
particularly combinations of the elements carbon, hydrogen,
oxygen, nitrogen, and smaller quantities of many others.
However, recent work in the field of AL has shown that the
natural evolutionary process can proceed with great efficacy in
other media, such as the informational medium of the digital
computer [1,3,5,15,16,20,24,42,43,50,52,53,67,68,70-73,76,77,
80,88,90,96]. \footnote{In ref. I, Adami has used the
input-output facilities of the new Tierra languages to feed data
to creatures, and select for responses that result from single
computations, not contained in the seed genome. In ref. 7,
Brooks has created his own Tierra-like system, which he calls
Sierra. In his implementation, each machine instruction consists
of an opcode and an operand. Succesive instructions overlap such
that the operand of one instruction is interpreted as the opcode
of the next instruction. In ref. 88, "Tierra-like systems are
being explored for their potential applications in solving the
problem of predicting the dynamics of consumption of a single
energy carrying resource."}
These new natural evolutions in artificial media are beginning
to explore the possibilities inherent in the "physics and
chemistry" of those media. They are organizing themselves and
constructing self-generating complex systems. While these new
living systems are still so young that they remain in their
primordial state, it appears that they have embarked on the same
kind of journey taken by life on earth and presumably have the
potential to evolve into levels of complexity that could lead to
sentient and eventually intelligent beings.
If natural evolution in artifical media leads to sentient or
intelligent beings, they will likely be so alien that they will
be difficult to recognize. The sentient properties of plants are
so radically different from those of animals hat they are
generally unrecognized or denied by humans, and plants are
merely in another kingdom of the one great tree of organic life
on earth [69,74,87]. Synthetic organisms evolving in other
media, such as the digital computer, are not only not a part of
the same philogeny, but they are not even of the same physics.
Organic life is based on conventional materal physics, whereas
digital life exists in a logical, not material, informational
universe. Digital intelligence will likely be vastly different
from human intelligence, forget the Turing test.
[..to be continued...]
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