Artificial Life

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Artificial Life

• Miriam Ruiz

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Contents

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

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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
  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?

INTRODUCTION gt What is Life
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What is Artificial Life?

• The study of man-made systems that exhibit
  behaviors characteristic of natural living
  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".

INTRODUCTION gt What is Artificial Life
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What is Artificial Life?

• 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.

INTRODUCTION gt What is Artificial Life
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What is Artificial Life?

• 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.

INTRODUCTION gt What is Artificial Life
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The Basis of Artificial Life

• Artificial Life tries to transcend the limitation
  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.

INTRODUCTION gt The Basis of Artificial Life
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The Basis of Artificial Life

• Synthetic Approach Synthesis ofcomplex 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.

INTRODUCTION gt The Basis of Artificial Life
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The Basis of Artificial Life

• 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.

INTRODUCTION gt The Basis of Artificial Life
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The Basis of Artificial Life

• 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.

INTRODUCTION gt The Basis of Artificial Life
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The Basis of Artificial Life

• 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.

INTRODUCTION gt The Basis of Artificial Life
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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 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.

INTRODUCTION gt Linear Models
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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 can lead to qualitatively different
  outcomes.
• Emergent phenomena Occurrence of higher level
  features that werent 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.

INTRODUCTION gt Non-Linear Models
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Contents

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

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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.
• 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.
• Theyre 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 gt L-Systems
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Lindenmeyer Systems
EMERGENT PATTERNS gt L-Systems
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Lindenmeyer Systems
EMERGENT PATTERNS gt L-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).

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Lindenmeyer Systems
EMERGENT PATTERNS gt L-Systems
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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 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.

EMERGENT PATTERNS gt DLA
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Diffusion Limited Aggregation (DLA)
EMERGENT PATTERNS gt DLA
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Diffusion Limited Aggregation (DLA)
EMERGENT PATTERNS gt DLA
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Diffusion Limited Aggregation (DLA)
EMERGENT PATTERNS gt DLA
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Diffusion Limited Aggregation (DLA)
EMERGENT PATTERNS gt DLA
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Contents

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

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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.
• 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 gt Introduction
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Wolframs Cellular Automata
CELLULAR AUTOMATA gt Wolfram CAs

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

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Wolframs Cellular Automata
CELLULAR AUTOMATA gt Wolfram CAs
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Wolframs Cellular Automata
CELLULAR AUTOMATA gt Wolfram CAs
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Wolframs 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.
• 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 gt Wolfram CAs
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Class IV CA Examples
CELLULAR AUTOMATA gt Wolfram CAs
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1-D CA Example Seashells
CELLULAR AUTOMATA gt Wolfram CAs
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Conways 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 cells birth.
• The result is a Turing-Complete system.

CELLULAR AUTOMATA gt Conways Game of Life
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Conways Game of Life
CELLULAR AUTOMATA gt Conways Game of Life
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Conways Game of Life
CELLULAR AUTOMATA gt Conways Game of Life
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Contents

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

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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
  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).

AGENTS gt Introduction
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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 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.

AGENTS gt Introduction
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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 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 gt Chimps
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Artificial Societies Chimps
AGENTS gt Chimps
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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 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.

AGENTS gt Chimps
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Artificial Societies Chimps
AGENTS gt Chimps
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Contents

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

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Distributed Intelligence

• Complex behaviour patterns of a group, in which
  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.

DISTRIBUTED INTELLIGENCE gt Introduction
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Didabots

• Experiment carried on in 1996, studying the
  collective behaviour of simple robots, 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.

DISTRIBUTED INTELLIGENCE gt Didabots
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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 gt Didabots
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Didabots
DISTRIBUTED INTELLIGENCE gt Didabots
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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.

DISTRIBUTED INTELLIGENCE gt Didabots
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Social Insects

• The main quality for the so-called social
  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.

DISTRIBUTED INTELLIGENCE gt Social Insects
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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.

DISTRIBUTED INTELLIGENCE gt Social Insects
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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.

DISTRIBUTED INTELLIGENCE gt Social Insects
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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 its 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 wont happen that all the
  bees feel oppressed at the same time. That way, a
  thermal stability is achieved.

DISTRIBUTED INTELLIGENCE gt Social Insects
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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 gt Social Insects
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Ants
DISTRIBUTED INTELLIGENCE gt Social Insects
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Boids (bird-oids)

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

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

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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
  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.

EVOLUTION gt Self Replication
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Self Replicant Cellular Automata

• In 1948, mathematician von Neumann approached the
  topic 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 Neumanns 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 gt Self Replicant Cellular Automata
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Langton Loops
EVOLUTION gt Autómatas Celulares
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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 computers memory fighting
  each other.
• It is executed in a virtual machine called MARS
  (Memory Array Redcode Simulator).
• Inspired in Creeper, a useless program that
  replicated itself inside the computers 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 opponents code.
• There are no mutations.

EVOLUTION gt Core War
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Genetic Evolution
EVOLUTION gt Genetic Evolution
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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 move through the
  genetic space (of 9 dimensions in this case) and
  keep selecting the desired shape.
• Users eye take the role of natural selection.

EVOLUTION gt Biomorphs
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Biomorphs
EVOLUTION gt Biomorphs
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Karl Sims' Virtual Creatures

• Developed by Karl Sims in 1994.
• Sims evolves morphology and neural control.
• 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 gt Karl Sims Virtual Creatures
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Karl Sims' Virtual Creatures
EVOLUTION gt Karl Sims Virtual Creatures
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Karl Sims' Virtual Creatures
EVOLUTION gt Karl Sims Virtual Creatures
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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 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 gt Evolutive Algorithms
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Genetic Algorithms
EVOLUTION gt Genetic Algorithms
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Genetic Programming
EVOLUTION gt Genetic Programming
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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).
• 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.

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

EVOLUTION gt Tierra
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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.
• These organisms do not have any self-replication
  code of their own but they use the code inside
  the longer ones instead.Theyre a kind of
  parasites.

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

EVOLUTION gt Tierra
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Tierra
EVOLUTION gt Tierra

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

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Tierra
EVOLUTION gt Tierra

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

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Tierra
EVOLUTION gt Tierra

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

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Tierra
EVOLUTION gt Tierra

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

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

EVOLUTION gt Avida
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AVida
EVOLUTION gt Avida
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Physis

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

EVOLUTION gt Physis
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Contents

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

82
Artificial Chemistry

• Artificial Chemistry is the computer simulation
  of chemical processes in a 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.

ARTIFICIAL CHEMISTRY gt Introduction
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Contents

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

84
SimLife
EXAMPLES gt Games gt SimLife
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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 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 gt Games gt SimLife
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Creatures
EXAMPLES gt Games gt Creatures
87
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 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 Creatures life.

EXAMPLES gt Games gt Creatures
88
The Sims
EXAMPLES gt Games gt The Sims
89
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 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 theyre given.
  Even though most of the Sims are able to survive
  on their own, they need lots of cares from the
  person whos 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 gt Games gt The Sims
90
Galapagos
EXAMPLES gt Galapagos
91
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 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 gt Galapagos
92
FramSticks
EXAMPLES gt FramSticks
93
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 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.

EXAMPLES gt FramSticks
94
Contents

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

95
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/galapag
  os/
• Wikipedia www.wikipedia.org
• Course on Artificial Life by University of
  Zurich http//ailab.ch/teaching/classes/2003ss/a
  life
• 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-li
  nux/howto/html/ai.html
• Matrem www.phys.uu.nl/romans/

96
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/l
  is_projekt/proj_gamelife/ConwayScientificAmerican.
  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/si
  ggraph97-course/cwr87/
• Creatures Artificial Life Autonomous Software
  Agents for Home Entertainment http//mrl.snu.ac.k
  r/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/