[PDF] - Artificial Life Contents Introduction Emergent Patterns PDF Document

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Artificial Life

Miriam Ruiz

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What is Life?

INTRODUCTION > What is Life

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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".

What is Artificial Life?

INTRODUCTION > What is Artificial Life

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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"

utside of a carbon-based chemical
solution. Its researchers try instead to mimic

life processes to understand the appearance

f individual phenomena.

What is Artificial Life?

INTRODUCTION > What is Artificial Life

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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
wn right, without necessarily having to

have a direct natural equivalent.

What is Artificial Life?

INTRODUCTION > What is Artificial Life

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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.

The Basis of Artificial Life

INTRODUCTION > The Basis of Artificial Life

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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.

The Basis of Artificial Life

INTRODUCTION > The Basis of Artificial Life

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Self-Organization: Spontaneous formation
f 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.

The Basis of Artificial Life

INTRODUCTION > 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 > The Basis of Artificial Life

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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
bserver'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.

The Basis of Artificial Life

INTRODUCTION > The Basis of Artificial Life

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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

f 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

INTRODUCTION > Linear Models

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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 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.

Linear vs. Non-linear Models

INTRODUCTION > Non-Linear Models

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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.

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

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Lindenmeyer Systems

EMERGENT PATTERNS > L-Systems

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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).

EMERGENT PATTERNS > L-Systems

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Lindenmeyer Systems

EMERGENT PATTERNS > 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 > DLA

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Diffusion Limited Aggregation (DLA)

EMERGENT PATTERNS > DLA

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Diffusion Limited Aggregation (DLA)

EMERGENT PATTERNS > DLA

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Diffusion Limited Aggregation (DLA)

EMERGENT PATTERNS > DLA

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Diffusion Limited Aggregation (DLA)

EMERGENT PATTERNS > DLA

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Cellular Automata

CELLULAR AUTOMATA > Introduction

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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

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Wolfram’s Cellular Automata

CELLULAR AUTOMATA > Wolfram CAs

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Wolfram’s Cellular Automata

CELLULAR AUTOMATA > Wolfram CAs

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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.

Class II (Stable or Periodic): Yields a sequence of

simple stable or periodic structures (endless cycle

f 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

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Class IV CA Examples

CELLULAR AUTOMATA > Wolfram CAs

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1-D CA Example: Seashells

CELLULAR AUTOMATA > Wolfram CAs

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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

verpopulation. 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

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Conway’s Game of Life

CELLULAR AUTOMATA > Conway’s Game of Life

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Conway’s Game of Life

CELLULAR AUTOMATA > Conway’s Game of Life

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Agent-based Modelling

AGENTS > Introduction

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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.

Agent-based Modelling

AGENTS > Introduction

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Artificial Societies: Chimps

Charlotte Hemelrijk has investigated (1998) the emergence
f 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 > Chimps

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Artificial Societies: Chimps

AGENTS > 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
f 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 > Chimps

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Artificial Societies: Chimps

AGENTS > Chimps

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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
f 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 > 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 > 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
f the left sensor

makes the bot turn a bit to the right, and viceversa.

DISTRIBUTED INTELLIGENCE > Didabots

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Didabots

DISTRIBUTED INTELLIGENCE > 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 > 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-

rganised group, whose key aspect is

“simplicity”.

These insects solve their complex problems

through the sum of simple interactions of every individual insect.

DISTRIBUTED INTELLIGENCE > 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 > 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 > 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

ut 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

ppressed at the same time. That way, a thermal

stability is achieved.

DISTRIBUTED INTELLIGENCE > 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
ne, 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

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Ants

DISTRIBUTED INTELLIGENCE > 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

ther 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

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Boids (bird-oids)

DISTRIBUTED INTELLIGENCE > Boids

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Self Replication

EVOLUTION > Self Replication

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Self Replicant Cellular Automata

In 1948, mathematician von Neumann approached the topic
f 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

f 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 > Self Replicant Cellular Automata

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Langton Loops

EVOLUTION > 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 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

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 > Core War

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Genetic Evolution

EVOLUTION > 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.

User’s eye take the role of

natural selection.

EVOLUTION > Biomorphs

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Biomorphs

EVOLUTION > 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
f 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

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Karl Sims' Virtual Creatures

EVOLUTION > Karl Sims’ Virtual Creatures

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Karl Sims' Virtual Creatures

EVOLUTION > Karl Sims’ Virtual Creatures

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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.

Evolutive Algorithms

EVOLUTION > Evolutive Algorithms

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Genetic Algorithms

EVOLUTION > Genetic Algorithms

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Genetic Programming

EVOLUTION > 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
rganisms, with an artificial nod and wink to

natural catastrophes.

EVOLUTION > Tierra

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Tierra

The universe was seeded with a single
rganism (hand coded by Ray), which just

had the ability to reproduce. It had a length

f 80 instructions and it took over 800

instruction cycles to replicate.

Once the space was filled by 80%, the
rganism started competing for space and

CPU cycles.

Soon mutations only 79 instructions

long proliferated - after a while even shorter

rganisms. Evolution had begun
ptimising the code.

EVOLUTION > 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
rganisms 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.They’re a kind of parasites.

EVOLUTION > 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 > Tierra

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Tierra

Many hosts (red)
Some parasites appear (yellow)

EVOLUTION > Tierra

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Tierra

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

EVOLUTION > Tierra

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Tierra

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

EVOLUTION > Tierra

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Tierra

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

EVOLUTION > Tierra

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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.

It’s similar to Tierra, but:

– Has a virtual CPU for each program. – Creatures can evolve for more than just

reproduction. Configurable fitness function.

EVOLUTION > Avida

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AVida

EVOLUTION > Avida

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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

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Artificial Chemistry

ARTIFICIAL CHEMISTRY > Introduction

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SimLife

EXAMPLES > Games > 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 > Games > SimLife

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Creatures

EXAMPLES > Games > Creatures

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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 Creature’s life.

EXAMPLES > Games > Creatures

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The Sims

EXAMPLES > Games > The Sims

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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 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

bject. That makes adding new objects to the game easier.

EXAMPLES > Games > The Sims

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Galapagos

EXAMPLES > Galapagos

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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
f 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 > Galapagos

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FramSticks

EXAMPLES > FramSticks

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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

rganisms, 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 > FramSticks

SLIDE 94

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/

SLIDE 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/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/