Artificial life (2005)

Artificial Life

Miriam Ruiz

Contents
• Introduction
• Emergent Patterns
• Cellular Automata
• Agent-based modelling
• Distributed Intelligence
• Artificial Evolution
• Artificial Chemistry
• Examples
• Bibliography

What is Life?
• There is no generally accepted definition of life.
• In general, it can be said that the condition that
distinguishes living organisms from inorganic
objects or dead organisms growth through
metabolism, a means of reproduction, and
INTRODUCTION > What is Life

internal regulation in response to the
environment.
• Even though the ability to reproduce is considered
essential to life, this might be more true for species
than for individual organisms. Some animals
are incapable of reproducing, e.g. mules, soldier
ants/bees or simply infertile organisms. Does this
mean they are not alive?

What is Artificial Life?
• The study of man-made systems that exhibit
behaviors characteristic of natural living
INTRODUCTION > What is Artificial Life

systems .
• It came into being at the end of the ’80s
when Christopher G. Langton organized
the first workshop on that subject in Los
Alamos National Laboratory in 1987, with the
title: "International Conference on the
Synthesis and Simulation of Living Systems".

Artificial life researchers have often been
divided into two main groups:

• The strong alife position states that life is a
process which can be abstracted away from
any particular medium.
• The weak alife position denies the
possibility of generating a "living process"
outside of a carbon-based chemical
solution. Its researchers try instead to mimic
life processes to understand the appearance
of individual phenomena.

• The goal of Artificial Life is not only to
provide biological models but also to

investigate general principles of Life.
• These principles can be investigated in their
own right, without necessarily having to
have a direct natural equivalent.

The Basis of Artificial Life
• Artificial Life tries to transcend the limitation
INTRODUCTION > The Basis of Artificial Life

to Earth bound life, based beyond the
carbon-chain, on the assumption that life is
a property of the organization of matter,
rather than a property of the matter itself.

• Synthetic Approach: Synthesis of

complex systems from many simple
interacting entities.
• If we captured the essential spirit of ant
behavior in the rules for virtual ants, the
virtual ants in the simulated ant colony
should behave as real ants in a
real ant colony.

• Self-Organization: Spontaneous formation

of complex patterns or complex behavior
emerging from the interaction of simple
lower-level elements/organisms.
• Emergence: Property of a system as a
whole not contained in any of its
parts. Such emergent behavior results
from the interaction of the elements of such
system, which act following local, low-level
rules.

• Levels of Organization: Life, as we

know it on Earth, is organized into at
least four levels of structure:
– Molecular level.
– Cellular level.
– Organism level.
– Population-ecosystem level.

• We have to distinguish between the perspective of
an observer looking at an creature and the

perspective of the creature itself.
• In particular, descriptions of behavior from an
observer's perspective must not be taken as the
internal mechanisms underlying the described
behavior of the creature.
• The observed behavior of a creature is always the
result of a system-environment interaction. It
cannot be explained on the basis of internal
mechanisms only.
• Seemingly complex behavior does not necessarily
require complex internal mechanisms. Seemingly
simple behavior is not necessarily the results of
simple internal mechanisms.

Linear vs. Non-Linear Models
• Linear models are unable to describe many natural
phenomena.
• In a linear model, the whole is the sum of its
parts, and small changes in model parameters
INTRODUCTION > Linear Models

have little effect on
the behavior of the model.
• Many phenomena such as weather, growth of plants, traffic
jams, flocking of birds, stock market crashes, development
of multi-cellular organisms, pattern formation in nature (for
example on sea shells and butterflies), evolution,
intelligence, and so forth resisted any linearization; that is,
no satisfying linear model was ever found.

Linear vs. Non-linear Models
• Non-linear models can exhibit a number of features
not known from linear ones:
– Chaos: Small changes in parameters or initial conditions
INTRODUCTION > Non-Linear Models

can lead to qualitatively different outcomes.
– Emergent phenomena: Occurrence of higher level
features that weren’t explicitly modelled.
– As a main disadvantage, non-linear models typically
cannot be solved analytically, in contrast with Linear
Models. Nonlinear modeling became manageable only
when fast computers were available .
• Models used in Artificial Life are always non-
linear.

Lindenmeyer Systems
• Lindenmayer Systems or L-systems are a
mathematical formalism proposed in 1968 by
biologist Aristid Lindenmayer as a basis for an
axiomatic theory on biological development.
EMERGENT PATTERNS > L-Systems

• The basic idea underlaying L-Systems is rewriting:
Components of a single object are replaced using
predefined rewriting rules.
• Its main application field is realistic plants
modelling and fractals.
• They’re based in symbolic rules that define the
graphic structure generation, starting from a
sequence of characters.
• Only as small amount of information is needed to
represent very complex models.

EMERGENT PATTERNS > L-Systems Lindenmeyer Systems

EMERGENT PATTERNS > L-Systems Lindenmeyer Systems

• Even though Lindenmeyer Systems do not directly
generate images but long sequences of symbols,
they can be interpreted in such a way that it is
possible to visualize them as Turtle Graphics
(Turtle Graphics were created by Seymour Papert
for the LOGO language).

Diffusion Limited Aggregation (DLA)
• "Diffusion limited aggregation, a kinetic critical
phenomena“, Physical Review Letters, num. 47,
published in 1981.
• It reproduces the growth of vegetal entities like
mosses, seaweed or lichen, and chemical
EMERGENT PATTERNS > DLA

processes such as electrolysis or the
crystallization of certain products.
• A number of moving particles are freed inside an
enclosure where we have already one or more
particles fixed.
• Free particles keep moving in a Brownian motion
until they reach a fixed particle nearby. In that case
they fix themselves too.

Diffusion Limited Aggregation (DLA)
EMERGENT PATTERNS > DLA

Cellular Automata
• Discrete model studied in computability theory and
mathematics.
• It consists of an infinite, regular grid of cells,
each in one of a finite number of states.
CELLULAR AUTOMATA > Introduction

• The grid can be in any finite number of dimensions.
• Time is also discrete, and the state of a cell at time
t is a function of the state of a finite number of cells
called the neighborhood at time t-1.
• The neighbourhood is a selection of cells relative
to some specified, and does not change.
• Every cell has the same rule for updating, based
on the values in this neighbourhood.
• Each time the rules are applied to the whole grid a
new generation is produced.

CELLULAR AUTOMATA > Wolfram CAs Wolfram’s Cellular Automata

• Studied by Stephen Wolfram at the beginning of
the ’80s.
• Unidimensional cellular automata with a
neighbourhood of 1 cell around the one we’re
studying.
• There are 256 elemental Wolfram CAm each of
them with an associated “Wolfram Number”.

CELLULAR AUTOMATA > Wolfram CAs Wolfram’s Cellular Automata

Wolfram’s four Classes of CA
• Class I (Empty): Tends to spatially homogeneous
state (all cells are in the same state). Patterns
disappear with time. Small changes in the initial
conditions cause no change in final state.
CELLULAR AUTOMATA > Wolfram CAs

• Class II (Stable or Periodic): Yields a sequence of
simple stable or periodic structures (endless cycle
of same states). Point attractor or periodic attractor.
Small changes in the initial conditions cause
changes only in a region of finite size.
• Class III (Chaotic): Exhibits chaotic aperiodic
behavior. Pattern grows indefinitely at a fixed rate.
Small changes in the initial conditions cause
changes over a region of ever-increasing size.
• Class IV (Complex): Yields complicated localized
structures, some propagating. Pattern grows and
contracts with time. Small changes in the initial
conditions cause irregular changes.

CELLULAR AUTOMATA > Wolfram CAs Class IV CA Examples

CELLULAR AUTOMATA > Wolfram CAs 1-D CA Example: Seashells

CELLULAR AUTOMATA > Conway’s Game of Life Conway’s Game of Life
• Invented by english mathematician John Conway and
published by Martin Gardner in Scientific American in 1970.
• Bidimensional board, in each cell can be one or none live
cells (binary).
• The neighbourhood is the 8 surrounding cells.
• Very simple rule set:
– Survival: A cell survives if there are 2 or 3 live cells in its
neighbourhood.
– Death: A cell surrounded by other 4 or more dies of
overpopulation. If it is surrounded by one or none, dies of isolation.
– Birth: An empty place surrounded by exactly three cells gives place
to a new cell’s birth.
• The result is a Turing-Complete system.

CELLULAR AUTOMATA > Conway’s Game of Life Conway’s Game of Life

Agent-based Modelling
• Computational model based in the analysis of
specific individuals situated in an environment,
for the study of complex systems.
• The model was conceptually developed at the end
of the ’40s, and had to wait for the arrival of
computers to be able to develop totally.
• The idea is to build the agents, or computational
AGENTS > Introduction

devices, and simulate them in parallel to be able to
model the real phenomena that is being analysed.
• The resulting process is the emergency from
lower levels of the social system (micro)
towards the upper levels (macro).

Agent-based Modelling
• Simulations based in agents have two
essential components:
– Agents
– Environment
• The environment has a certain autonomy
from the actions of the agents, although it
AGENTS > Introduction

can be modified by their behaviour.
• The interaction between the agents is
simulated, as well as the interaction
between the agents and their surrounding
environment.

Artificial Societies: Chimps
• Charlotte Hemelrijk has investigated (1998) the emergence
of structure in societies of primates in the real world and in
simulation.
• Her creatures were able to move and to see each other. If
creatures perceived someone nearby, they engaged in
dominance interactions.
• The effects of losing (and winning) are self-reinforcing:
after losing a fight the chance to loose the next fight is larger
(even if the opponent is weak). The winner effect is the
converse.
• If they were not engaged in dominance interactions, they
followed rules of moving and turning, that kept them
AGENTS > Chimps

aggregated (because real primates are group-living).
• It is unnecesary to consider the representation of a
hierarchical structure in the individual minds of the
chimps, because it appears spontaneously as an
emergent structure of the group.

AGENTS > Chimps Artificial Societies: Chimps

Artificial Societies: Chimps
• Interactions among these artificial chimps are just triggered
by the proximity of others not by record keeping or other
strategic considerations.
• A dominance hierarchy arose, and a social-spatial
structure, with dominants in the center and subordinates
at the periphery, similar to what has been described for
several primate species.
• For an external observer, support in fights appeared to be
repaid, despite the absence of a motivation to support or
keep records of them.
• This was a consequence of the occurrence of a series of
cooperation that consisted of two creatures alternatively
AGENTS > Chimps

supporting each other to chase away a third.
• These originated because by fleeing from the attack range
of one opponent the victim ended up in the attack range of
the other opponent. This typically ended when the spatial
structure had changed such that one of both cooperators
attacked the other.

Distributed Intelligence
• Complex behaviour patterns of a group, in which
DISTRIBUTED INTELLIGENCE > Introduction

there is no central command.
• It arises from “emergent behaviour”.
• It appears in a group as a whole, but is no
explicitly programmed in none of the individual
members of the group.
• Simple behaviour rules in the individual members
of the group can cause a complex behaviour
pattern of the group as a whole.
• The group is able to solve complex problems a
partir only local information.
• Examples: Social insects, immunological system,
neural net processing.

Didabots
• Experiment carried on in 1996, studying the
collective behaviour of simple robots,
DISTRIBUTED INTELLIGENCE > Didabots

called Didabots.
• The main idea is to verify that apparently
complex behaviour patterns can be a
consequence of very simple rules that
guide the interactions between the entities
and the environment.
• This idea has been successfully applied for
example to the study of social insects.

Didabots
• Infrared sensors can
be used to detect

proximity up to about
5 cm.
• Programmed
exclusively for
avoiding obstacles.
• Sensorial stimulation
of the left sensor
makes the bot turn a
bit to the right, and
viceversa.

DISTRIBUTED INTELLIGENCE > Didabots Didabots

Didabots
• Initially the cubes are randomly distributed.
• Over time, a number of clusters start to form. In the end,
there are only two clusters and a number of cubes along

the walls of the arena.
• These experiments were performed many times and the
result is very consistent.
• Apparently Didabots are cleaning the arena, grouping
blocks into clusters, from an external observer point of view.
• The robots were only programmed to avoid obstacles.
• This happens because when there is a cube right in front of
the Didabot, it is not able to detect it, and thew Didabot
pushes the cube until it collides with another cube. The
cube being pushed is slightly moved and it enters the
perception space of one of the sensors. The Didabot turns a
bit then and leaves the cube.

Social Insects
• The main quality for the so-called social
DISTRIBUTED INTELLIGENCE > Social Insects

insects, ants or bees, is to form part of a self-
organised group, whose key aspect is
“simplicity”.
• These insects solve their complex problems
through the sum of simple interactions of
every individual insect.

Bees
• The distribution of brood and

nourishment in the comb of honey bees is
not random, but forms a regular pattern .
• The central brooding region is close to a
region containing pollen and one containing
nectar (providing protein and carbohydrates
for the brood).
• Due to the intake and outtake of pollen and
nectar, the pattern is changing all the time on
a local scale, but it stays stable if observed
from a more global scale.

Bees
• This is not the result of an individual bee

being aware of the global pattern of brood-
and food-distribution in the comb, but of
three simple local rules, which each
individual bee follows:
– Deposit brood in cells next to cells already
containing brood.
– Deposit nectar and pollen in discretionary cells
but empty the cells closest to the brood first.
– Extract more pollen than nectar.

Bees
• Bees keep the thermal stability of the beehive

through a decentralised mechanism in which every
bee acts subjectively and locally.
• If the temperature is too high, worker bees start
feeling oppressed and flutter to throw the warm air
out of their nest. They also feel oppressed when it’s
too cold, in which case they crowd together and
warm the beehive with the sum of their bodies.
• A typical colony comes from a single mother (the
queen), but from very different fathers (between 10
and 30) and thus the genetics of the colony varies
widely, and it won’t happen that all the bees feel
oppressed at the same time. That way, a thermal
stability is achieved.

Ants
• Ants are able to find the shortest path between a

food source and their anthill without using visual
references.
• They are also able to find a new path, the shortest
one, when a new obstacle appears and the old
path cannot be used any more.
• Even though an isolated ant moves randomly, it
prefers to follow a pheromone-rich path. When
they are in a group, then, they are able to make
and maintain a path through the pheromones they
leave when they walk.
• Ants who select the shortest path get to their
destination sooner. The shortest path receives then
a higher amount of pheromones in a certain time
unit. As a consequence, a higher number of ants
will follow this shorter path.

DISTRIBUTED INTELLIGENCE > Social Insects Ants

Boids (bird-oids)
• They were invented in the mid-80s
by the computer animator Craig
Reynolds.
DISTRIBUTED INTELLIGENCE > Boids

• Their behavior is controlled by very
simple local rules:
– Collision avoidance. Only position of the
other boids is taken into account, not their
velocity.
– Velocity matching. In this case only their
velocity is taken into account.
– Flock centering makes a boid want to be
near the center of the perceived flockmates.
if the boid is at the periphery, flock centering
will cause it to deflect towards the center.

DISTRIBUTED INTELLIGENCE > Boids Boids (bird-oids)

Self Replication
• Self Replication is the process in which
something makes copies of itself.
• Biological cells, in an adequate environment, do
replicate themselves through cellular division.
• Biological viruses reproduce themselves by using
EVOLUTION > Self Replication

the reproductive mechanisms of the cells they
infect.
• Computer virus reproduce themselves by using the
hardware and software already present in
computers.
• Memes do reproduce themselves using human
mind as their reproductive machinery.

Self Replicant Cellular Automata
• In 1948, mathematician von Neumann approached the topic
EVOLUTION > Self Replicant Cellular Automata

of self-replication from an abstract point of view. He used
cellular automata and pointed out for the first time that it
was necessary to distinguish between hardware and
software.
• Unfortunately, Von Neumann’s self reproductive automata
were too big (80x400 cells) and complex (29 states) to be
implemented.
• In 1968, E. F. Codd lowered the number of needed states
from 29 to 8, introducing the concept of ‘sheaths’: two layers
of a particular state enclosing a single ‘wire’ of information
flow.
• In 1979, C. Langton develops an automata with self
reproductive capacity. He realised that such a structure
need not be capable of universal construction like those
from von Neumann and Codd. It just needs to be able to
reproduce its own structure.

EVOLUTION > Autómatas Celulares Langton Loops

Core War
• It is a game published in May 1984 in Scientific
American, in which two or more programs, written
in an special assembler language called Redcode,
try to conquer all the computer’s memory fighting
each other.
• It is executed in a virtual machine called MARS
(Memory Array Redcode Simulator).
• Inspired in Creeper, a useless program that
EVOLUTION > Core War

replicated itself inside the computer’s memory and
was able to displace more useful programs (it might
be called a virus) and Reaper, created to seek and
destroy copies of Creeper.
• The fighting programs reproduce themselves and
try to corrupt the opponent’s code.
• There are no mutations.

EVOLUTION > Genetic Evolution Genetic Evolution

Biomorphs
• Created by Richard Dawkins in
the third chapter of his book
“The Blind Watchmaker”.
• The program is able to show the
power of micromutactions and
accumulative selection.
• Biomorph Viewer lets the user
EVOLUTION > Biomorphs

move through the genetic space
(of 9 dimensions in this case)
and keep selecting the desired
shape.
• User’s eye take the role of
natural selection.

EVOLUTION > Biomorphs Biomorphs

Karl Sims' Virtual Creatures
• Developed by Karl Sims in 1994.
• Sims evolves morphology and neural control.
EVOLUTION > Karl Sims’ Virtual Creatures

• Sims was one of the first to use a 3-D world
of simulated physics in the context of virtual
reality applications.
• Simulating physics includes considerations of
gravity, friction, collision detection, collision
response, and viscous fluid effects (e.g. in
simulated water).
• Because of the simulated physics, these
agents interact in many unexpected ways
with the environment.

EVOLUTION > Karl Sims’ Virtual Creatures Karl Sims' Virtual Creatures

Evolutive Algorithms
• Genetic Algorithms: The most common
form of evolutive algorithms. The solution to
a problem is search as a text or a bunch of
EVOLUTION > Evolutive Algorithms

numbers (usually binary), aplying mutation
and recombination operators and
performing a selection on the possible
solutions.
• Genetic Programming: Solutions in this
case are computer programs, and their
fitness is determined by their ability to solve
a computational problem.

EVOLUTION > Genetic Algorithms Genetic Algorithms

EVOLUTION > Genetic Programming Genetic Programming

Tierra
• Developed by biologist Thomas Ray, inspired by
the game of competing computer programs called
“Core Wars”.
• The creatures are composed of a sequence of
instructions from a limited set of assembly
language operands.
• The universe for these things is the domain of the
computer, competing for space (computer
memory) and energy (CPU cycles).
EVOLUTION > Tierra

• The virtual machine that executed the programs
was designed to allow a small error rate, which
allows mutations while copying, in an analogous
way to natural mutation.
• A `reaper' program was included to kill some of the
organisms, with an artificial nod and wink to
natural catastrophes.

Tierra
• The universe was seeded with a single
organism (hand coded by Ray), which just
had the ability to reproduce. It had a length
of 80 instructions and it took over 800
instruction cycles to replicate.
• Once the space was filled by 80%, the
organism started competing for space and
CPU cycles.
EVOLUTION > Tierra

• Soon mutations only 79 instructions
long proliferated - after a while even shorter
organisms. Evolution had begun
optimising the code.

Tierra
• An organism of only 45 instructions was born
and started doing very well soon. This is
confusing: 45 instructions is certainly not
enough for self replication.
• These organisms coexist with organisms of
more than 70 instruccions.
• The number of the longer and shorter
organisms seemed to be linked.
EVOLUTION > Tierra

• These organisms do not have any self-
replication code of their own but they use
the code inside the longer ones
instead.They’re a kind of parasites.

Tierra
• A very long organism that had developed immunity to the
parasites emerged. It could `hide' from them.
• Soon the parasites evolved into a 51 instruction
long parasite, which could find the immune organism, and
so the evolutionary arms race continued.
• Hyperparasites evolved which could exploit the parasites.
• These hyperparasites could be seen to “cooperate”, this
means that they would exploit each other leading to the
evolution of “social cheaters”, which would exploit them
EVOLUTION > Tierra

both.
• The system continued with its evolution of competing
and cooperating self-replicating organisms

EVOLUTION > Tierra Tierra

• Many hosts (red)
• Some parasites appear (yellow)


• Parasites have increased a lot.
• Hosts are lowering.
• The first immune creatures (blue) appear


• Parasites are spacially displaced.
• Non-immunte hosts lower even more.
• Immune creatures keep increasing and diplace the parasites.


• Parasites are even more scarce.
• Non-immune hosts keep lowering.
• Immune creatures are the domintant life form.

AVida
• Avida is an auto-adaptive genetic system
designed primarily for use as a platform in
Digital or Artificial Life research.
• Digital world in which simple computer
programs mutate and evolve.
• Adds Genetic Programming to the virtual
world.
EVOLUTION > Avida

• It’s similar to Tierra, but:
– Has a virtual CPU for each program.
– Creatures can evolve for more than just
reproduction. Configurable fitness function.

Physis
• Physis goes a step further:
– 1st Phase: Building the processor’s structure and
instruction set according to the description in the
genoma.
– 2nd Phase: Executing the code with the newly built
processor.
EVOLUTION > Physis

Artificial Chemistry
• Artificial Chemistry is the computer
simulation of chemical processes in a
ARTIFICIAL CHEMISTRY > Introduction

similar way to that found in real world.
• It can be the foundation of an artificial life
program, and in that case usually some kind
of organic chemistry is simulated.

EXAMPLES > Games > SimLife SimLife

SimLife
• One of the first examples of entertainment
software announced as based in Artificial Life
investigation was SimLife by Maxis,
published in 1993.
• In essence, SimLife lets the user observe
EXAMPLES > Games > SimLife

and interact with a simulated ecosystem
with a variable terrain and climate, and a
great variety of species of plants, plant
eaters and carnivores.
• The ecosystem is simulated using cellular
automata techniques, and makes very little
use of autonomous agents.

EXAMPLES > Games > Creatures Creatures

Creatures
• Creatures is a game made in 1996 for Windows 95 and
Macintosh, that offers the possibility of getting in touch with
Artificial Life technologies.
• Creatures generates a simulated environment in which a
number of synthetic agents coexist, and with which the
user can interact in real-time. Agents, which are called
EXAMPLES > Games > Creatures

Creatures, try to be a kind of “virtual pets”.
• Internal architecture of the Creatures is inspired by
animal biology. Every Creature had a neural network
responsible for the motor-sensorial coordination and for its
behaviour, and an artificial biochemical system that
simulates a simple energetic metabolism and an hormonal
system that interacts with the neural network. A learning
mechanism allows the neural network to keep adapting
during Creature’s life.

EXAMPLES > Games > The Sims The Sims

The Sims
• The Sims, created by Maxis, is probably one of the best
examples of Artificial Life and Artificial Intelligence based in
fuzzy state machines in the videogames’ industry at the
moment.
• The game let the user design small virtual buildings and
their neighbourhood and populate them with virtual
EXAMPLES > Games > The Sims

residents ("Sims"). Every Sim can be created with a great
diversity of personalities and physical traits.
• Sims behaviour depends on their environment as well at the
personality traits they’re given. Even though most of the
Sims are able to survive on their own, they need lots of
cares from the person who’s playing to improve.
• Objects inside the virtual world (which is called "smart
terrain" by its designer Will Wright) incorporate inside them
all the possible behaviours and actions related to that
object. That makes adding new objects to the game easier.

EXAMPLES > Galapagos Galapagos

Galapagos
• Galapagos is an Artificial Life simulation project in which a
number of creatures evolve over time.
• By implementing mutations and crossovers and the implicit
natural selection in the simulation the overall result is an
evolution of the creatures in which new breeds of
creatures make different ecological niches araise.
• In this simulation the creatures lives on a height landscape
containing water, sand, soil, rocks, grass, trees etc.
• All creatures are landborn four legged and have a number
EXAMPLES > Galapagos

of genes determining their physical properties, such as how
well they can digest different forms of food, the length and
size of different body parts, etc.
• Their genome also includes a simple but flexible fuzzy
behaviour based AI brain that allows the creatures to
evolve different behaviours.
• Simulations typically start out as dumb grasseater with a
high mortality but after a while the creatures split up into
different evolutionary paths and creatures such as carrion
eaters and carnivores emerge.

EXAMPLES > FramSticks FramSticks

FramSticks
• The objective of these experiments is to
study evolution capabilities of creatures in
simplified Earth-like conditions.
• This conditions are: a three-dimensional
environment, genotype representation of
organisms, physical structure (body) and
EXAMPLES > FramSticks

neural network (brain) both described in
genotype, stiumuli loop (environment –
receptors – brain – effectors – environment),
genotype reconfiguration operations
(mutation, crossing over, repair), energetic
requirements and balance, and
specialization.

Bibliography
• Tierra: www.his.atr.jp/~ray/tierra/
• Avida: http://dllab.caltech.edu/avida/
• Physis: http://physis.sourceforge.net/
• Galapagos: http://www.lysator.liu.se/~mbrx/galapagos/
• Wikipedia: www.wikipedia.org
• Course on Artificial Life by University of Zurich:
http://ailab.ch/teaching/classes/2003ss/alife
• Course on Artificial Life:
http://www.ifi.unizh.ch/groups/ailab/teaching/AL00.html
• Vida artificial, Un enfoque desde la Informática Teórica:
http://members.tripod.com/~MoisesRBB/vida.html
• Digitales Leben:
http://homepages.feis.herts.ac.uk/~comqdp1/Studienstiftung/tierra_avida
_hysis.ppt
• GNU/Linux AI & Alife HOWTO: http://zhar.net/gnu-
linux/howto/html/ai.html
• Matrem: www.phys.uu.nl/~romans/

Bibliography
• Diffusion-Limited Aggregation:
http://classes.yale.edu/fractals/Panorama/Physics/DLA/DLA.html
• DLA - Diffusion Limited Aggregation:
http://astronomy.swin.edu.au/~pbourke/fractals/dla/
• John Conway's solitaire game "life“: http://ddi.cs.uni-
potsdam.de/HyFISCH/Produzieren/lis_projekt/proj_gamelife/ConwaySci
entificAmerican.htm
• Boids, background and update, by Craig Reynolds:
http://www.red3d.com/cwr/boids/
• Flocks, Herds, and Schools: A Distributed Behavioral Model:
http://www.cs.toronto.edu/~dt/siggraph97-course/cwr87/
• Creatures: Artificial Life Autonomous Software Agents for Home
Entertainment:
http://mrl.snu.ac.kr/CourseSyntheticCharacter/grand96creatures.pdf
• Evolving Virtual Creatures:
http://www.genarts.com/karl/papers/siggraph94.pdf
• Core War, artículos escaneados de A.K. Dewdney:
http://www.koth.org/info/sciam/
• FramSticks: http://www.frams.alife.pl/
• StarLogo: http://education.mit.edu/starlogo/

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