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Artificial Life

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Title: Artificial Life


1
Artificial Life
  • Miriam Ruiz

2
Contents
  • Introduction
  • Emergent Patterns
  • Cellular Automata
  • Agent-based modelling
  • Distributed Intelligence
  • Artificial Evolution
  • Artificial Chemistry
  • Examples
  • Bibliography

3
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
4
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
5
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
6
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
7
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
8
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
9
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
10
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
11
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
12
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
13
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
14
Contents
  • Introduction
  • Emergent Patterns
  • Cellular Automata
  • Agent-based modelling
  • Distributed Intelligence
  • Artificial Evolution
  • Artificial Chemistry
  • Examples
  • Bibliography

15
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
16
Lindenmeyer Systems
EMERGENT PATTERNS gt L-Systems
17
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).

18
Lindenmeyer Systems
EMERGENT PATTERNS gt L-Systems
19
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
20
Diffusion Limited Aggregation (DLA)
EMERGENT PATTERNS gt DLA
21
Diffusion Limited Aggregation (DLA)
EMERGENT PATTERNS gt DLA
22
Diffusion Limited Aggregation (DLA)
EMERGENT PATTERNS gt DLA
23
Diffusion Limited Aggregation (DLA)
EMERGENT PATTERNS gt DLA
24
Contents
  • Introduction
  • Emergent Patterns
  • Cellular Automata
  • Agent-based modelling
  • Distributed Intelligence
  • Artificial Evolution
  • Artificial Chemistry
  • Examples
  • Bibliography

25
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
26
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.

27
Wolframs Cellular Automata
CELLULAR AUTOMATA gt Wolfram CAs
28
Wolframs Cellular Automata
CELLULAR AUTOMATA gt Wolfram CAs
29
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
30
Class IV CA Examples
CELLULAR AUTOMATA gt Wolfram CAs
31
1-D CA Example Seashells
CELLULAR AUTOMATA gt Wolfram CAs
32
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
33
Conways Game of Life
CELLULAR AUTOMATA gt Conways Game of Life
34
Conways Game of Life
CELLULAR AUTOMATA gt Conways Game of Life
35
Contents
  • Introduction
  • Emergent Patterns
  • Cellular Automata
  • Agent-based modelling
  • Distributed Intelligence
  • Artificial Evolution
  • Artificial Chemistry
  • Examples
  • Bibliography

36
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
37
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
38
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
39
Artificial Societies Chimps
AGENTS gt Chimps
40
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
41
Artificial Societies Chimps
AGENTS gt Chimps
42
Contents
  • Introduction
  • Emergent Patterns
  • Cellular Automata
  • Agent-based modelling
  • Distributed Intelligence
  • Artificial Evolution
  • Artificial Chemistry
  • Examples
  • Bibliography

43
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
44
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
45
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
46
Didabots
DISTRIBUTED INTELLIGENCE gt Didabots
47
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
48
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
49
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
50
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
51
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
52
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
53
Ants
DISTRIBUTED INTELLIGENCE gt Social Insects
54
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
55
Boids (bird-oids)
DISTRIBUTED INTELLIGENCE gt Boids
56
Contents
  • Introduction
  • Emergent Patterns
  • Cellular Automata
  • Agent-based modelling
  • Distributed Intelligence
  • Artificial Evolution
  • Artificial Chemistry
  • Examples
  • Bibliography

57
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
58
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
59
Langton Loops
EVOLUTION gt Autómatas Celulares
60
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
61
Genetic Evolution
EVOLUTION gt Genetic Evolution
62
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
63
Biomorphs
EVOLUTION gt Biomorphs
64
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
65
Karl Sims' Virtual Creatures
EVOLUTION gt Karl Sims Virtual Creatures
66
Karl Sims' Virtual Creatures
EVOLUTION gt Karl Sims Virtual Creatures
67
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
68
Genetic Algorithms
EVOLUTION gt Genetic Algorithms
69
Genetic Programming
EVOLUTION gt Genetic Programming
70
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
71
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
72
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
73
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
74
Tierra
EVOLUTION gt Tierra
  • Many hosts (red)
  • Some parasites appear (yellow)

75
Tierra
EVOLUTION gt Tierra
  • Parasites have increased a lot.
  • Hosts are lowering.
  • The first immune creatures (blue) appear

76
Tierra
EVOLUTION gt Tierra
  • Parasites are spacially displaced.
  • Non-immunte hosts lower even more.
  • Immune creatures keep increasing and diplace the
    parasites.

77
Tierra
EVOLUTION gt Tierra
  • Parasites are even more scarce.
  • Non-immune hosts keep lowering.
  • Immune creatures are the domintant life form.

78
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
79
AVida
EVOLUTION gt Avida
80
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
81
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
83
Contents
  • Introduction
  • Emergent Patterns
  • Cellular Automata
  • Agent-based modelling
  • Distributed Intelligence
  • Artificial Evolution
  • Artificial Chemistry
  • Examples
  • Bibliography

84
SimLife
EXAMPLES gt Games gt SimLife
85
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
86
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/
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