artificial life (2005)

Artificial Life

Miriam Ruiz

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

• There is no generally accepted definition of life.• In general, it can be said that the condition that

distinguishes living organisms from inorganicobjects or dead organisms growth throughmetabolism, a means of reproduction, andinternal regulation in response to theenvironment.

• Even though the ability to reproduce is consideredessential to life, this might be more true for speciesthan for individual organisms. Some animalsare incapable of reproducing, e.g. mules, soldierants/bees or simply infertile organisms. Does thismean they are not alive?

What is Life?IN

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• The study of man-made systems that exhibitbehaviors characteristic of natural livingsystems .

• It came into being at the end of the ’80swhen Christopher G. Langton organizedthe first workshop on that subject in LosAlamos National Laboratory in 1987, with thetitle: "International Conference on the Synthesis and Simulation of Living Systems".

What is Artificial Life?IN

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Artificial life researchers have often beendivided 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.


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• The goal of Artificial Life is not only toprovide biological models but also toinvestigate general principles of Life.

• These principles can be investigated in theirown right, without necessarily having tohave a direct natural equivalent.


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• Artificial Life tries to transcend the limitationto Earth bound life, based beyond thecarbon-chain, on the assumption that life isa property of the organization of matter, rather than a property of the matter itself.

The Basis of Artificial LifeIN

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• Synthetic Approach: Synthesis ofcomplex systems from many simple interacting entities.

• If we captured the essential spirit of antbehavior in the rules for virtual ants, thevirtual ants in the simulated ant colonyshould behave as real ants in a real ant colony.


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• Self-Organization: Spontaneous formationof complex patterns or complex behavioremerging from the interaction of simple lower-level elements/organisms.

• Emergence: Property of a system as a whole not contained in any of itsparts. Such emergent behavior resultsfrom the interaction of the elements of suchsystem, which act following local, low-levelrules.


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The Basis of Artificial Life• Levels of Organization: Life, as we

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

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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 anobserver's perspective must not be taken as theinternal mechanisms underlying the describedbehavior 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 necessarilyrequire complex internal mechanisms. Seeminglysimple behavior is not necessarily the results ofsimple internal mechanisms.


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• Linear models are unable to describe many natural phenomena.

• In a linear model, the whole is the sum of itsparts, and small changes in model parametershave little effect onthe behavior of the model.

• Many phenomena such as weather, growth of plants, trafficjams, flocking of birds, stock market crashes, developmentof multi-cellular organisms, pattern formation in nature (forexample 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 ModelsIN

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• Non-linear models can exhibit a number of featuresnot 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 onlywhen fast computers were available .

• Models used in Artificial Life are always non-linear.

Linear vs. Non-linear ModelsIN

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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 anaxiomatic theory on biological development.

• The basic idea underlaying L-Systems is rewriting: Components of a single object are replaced usingpredefined rewriting rules.

• Its main application field is realistic plantsmodelling 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 torepresent very complex models.EM

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Lindenmeyer Systems

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

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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 likemosses, seaweed or lichen, and chemicalprocesses such as electrolysis or the crystallization of certain products.

• A number of moving particles are freed inside anenclosure where we have already one or more particles fixed.

• Free particles keep moving in a Brownian motionuntil they reach a fixed particle nearby. In that case they fix themselves too.EM

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Diffusion Limited Aggregation (DLA)EM

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• 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 relativeto some specified, and does not change.

• Every cell has the same rule for updating, basedon the values in this neighbourhood.

• Each time the rules are applied to the whole grid a new generation is produced.

Cellular AutomataC

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Wolfram’s Cellular Automata

• Studied by Stephen Wolfram at the beginning ofthe ’80s.

• Unidimensional cellular automata with a neighbourhood of 1 cell around the one we’re studying.

• There are 256 elemental Wolfram CAm each ofthem with an associated “Wolfram Number”.C

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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). Patternsdisappear with time. Small changes in the initialconditions cause no change in final state.

• Class II (Stable or Periodic): Yields a sequence ofsimple stable or periodic structures (endless cycleof 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 aperiodicbehavior. 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 localizedstructures, some propagating. Pattern grows andcontracts with time. Small changes in the initialconditions cause irregular changes.

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Class IV CA ExamplesC

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1-D CA Example: SeashellsC

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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 itsneighbourhood.

– Death: A cell surrounded by other 4 or more dies ofoverpopulation. 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.

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Conway’s Game of LifeC

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• Computational model based in the analysis ofspecific individuals situated in an environment, for the study of complex systems.

• The model was conceptually developed at the endof 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 computationaldevices, and simulate them in parallel to be able tomodel the real phenomena that is being analysed.

• The resulting process is the emergency fromlower levels of the social system (micro) towards the upper levels (macro).

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• Simulations based in agents have twoessential components:– Agents– Environment

• The environment has a certain autonomyfrom the actions of the agents, although itcan be modified by their behaviour.

• The interaction between the agents issimulated, as well as the interactionbetween the agents and their surroundingenvironment.

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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 theconverse.

• 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 thechimps, because it appears spontaneously as anemergent structure of the group.

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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-spatialstructure, with dominants in the center and subordinatesat the periphery, similar to what has been described forseveral 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.

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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 membersof the group can cause a complex behaviourpattern 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.

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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 apparentlycomplex behaviour patterns can be a consequence of very simple rules thatguide the interactions between the entitiesand the environment.

• This idea has been successfully applied forexample to the study of social insects.

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Didabots• Infrared sensors can

be used to detectproximity up to about5 cm.

• Programmedexclusively foravoiding obstacles.

• Sensorial stimulationof the left sensormakes the bot turn a bit to the right, andviceversa.

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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 alongthe walls of the arena.

• These experiments were performed many times and theresult is very consistent.

• Apparently Didabots are cleaning the arena, groupingblocks 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 Didabotpushes 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.

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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 problemsthrough the sum of simple interactions ofevery individual insect.

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Bees• The distribution of brood and

nourishment in the comb of honey bees isnot random, but forms a regular pattern .

• The central brooding region is close to a region containing pollen and one containingnectar (providing protein and carbohydratesfor the brood).

• Due to the intake and outtake of pollen andnectar, the pattern is changing all the time ona local scale, but it stays stable if observedfrom a more global scale.

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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 ofthree simple local rules, which eachindividual 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.

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Bees• Bees keep the thermal stability of the beehive

through a decentralised mechanism in which everybee acts subjectively and locally.

• If the temperature is too high, worker bees startfeeling oppressed and flutter to throw the warm airout 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 (thequeen), but from very different fathers (between 10 and 30) and thus the genetics of the colony varies widely, and it won’t happen that all the bees feel oppressed at the same time. That way, a thermal stability is achieved.

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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 shortestone, when a new obstacle appears and the oldpath cannot be used any more.

• Even though an isolated ant moves randomly, itprefers to follow a pheromone-rich path. Whenthey are in a group, then, they are able to makeand maintain a path through the pheromones theyleave when they walk.

• Ants who select the shortest path get to theirdestination sooner. The shortest path receives thena higher amount of pheromones in a certain time unit. As a consequence, a higher number of antswill follow this shorter path.D

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Boids (bird-oids)• They were invented in the mid-80s

by the computer animator CraigReynolds.

• Their behavior is controlled by verysimple local rules:– Collision avoidance. Only position of the

other boids is taken into account, not theirvelocity.

– Velocity matching. In this case only theirvelocity 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 centeringwill cause it to deflect towards the center.D

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Boids (bird-oids)D

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• Self Replication is the process in whichsomething makes copies of itself.

• Biological cells, in an adequate environment, do replicate themselves through cellular division.

• Biological viruses reproduce themselves by usingthe reproductive mechanisms of the cells theyinfect.

• Computer virus reproduce themselves by using thehardware and software already present in computers.

• Memes do reproduce themselves using human mind as their reproductive machinery.

Self ReplicationEV

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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 usedcellular automata and pointed out for the first time that itwas necessary to distinguish between hardware andsoftware.

• 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 statesfrom 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 selfreproductive capacity. He realised that such a structureneed not be capable of universal construction like thosefrom von Neumann and Codd. It just needs to be able toreproduce its own structure.EV

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Core War• It is a game published in May 1984 in Scientific

American, in which two or more programs, writtenin 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 thatreplicated 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 andtry to corrupt the opponent’s code.

• There are no mutations.

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Genetic EvolutionEV

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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 thepower of micromutactions andaccumulative selection.

• Biomorph Viewer lets the usermove through the genetic space(of 9 dimensions in this case) and keep selecting the desiredshape.

• User’s eye take the role of natural selection.

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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 ofgravity, friction, collision detection, collisionresponse, and viscous fluid effects (e.g. in simulated water).

• Because of the simulated physics, theseagents interact in many unexpected wayswith the environment.

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Karl Sims' Virtual CreaturesEV

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• Genetic Algorithms: The most commonform of evolutive algorithms. The solution toa problem is search as a text or a bunch ofnumbers (usually binary), aplying mutationand recombination operators andperforming a selection on the possiblesolutions.

• Genetic Programming: Solutions in thiscase are computer programs, and theirfitness is determined by their ability to solvea computational problem.

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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 ofinstructions from a limited set of assemblylanguage operands.

• The universe for these things is the domain of thecomputer, competing for space (computermemory) and energy (CPU cycles).

• The virtual machine that executed the programswas designed to allow a small error rate, whichallows mutations while copying, in an analogousway to natural mutation.

• A `reaper' program was included to kill some of theorganisms, with an artificial nod and wink tonatural catastrophes.EV

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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%, theorganism started competing for space andCPU cycles.

• Soon mutations only 79 instructionslong proliferated - after a while even shorterorganisms. Evolution had begunoptimising the code.

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Tierra• An organism of only 45 instructions was born

and started doing very well soon. This isconfusing: 45 instructions is certainly notenough for self replication.

• These organisms coexist with organisms ofmore than 70 instruccions.

• The number of the longer and shorterorganisms seemed to be linked.

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

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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, andso 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

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

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

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

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

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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 computerprograms 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.

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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 builtprocessor.

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• Artificial Chemistry is the computersimulation of chemical processes in a similar way to that found in real world.

• It can be the foundation of an artificial lifeprogram, and in that case usually some kindof organic chemistry is simulated.

Artificial ChemistryA

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SimLifeEX

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SimLife• One of the first examples of entertainment

software announced as based in Artificial Lifeinvestigation was SimLife by Maxis, published in 1993.

• In essence, SimLife lets the user observe and interact with a simulated ecosystemwith a variable terrain and climate, and a great variety of species of plants, planteaters and carnivores.

• The ecosystem is simulated using cellularautomata techniques, and makes very littleuse of autonomous agents.

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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 withArtificial Life technologies.

• Creatures generates a simulated environment in which a number of synthetic agents coexist, and with which theuser can interact in real-time. Agents, which are calledCreatures, try to be a kind of “virtual pets”.

• Internal architecture of the Creatures is inspired by animal biology. Every Creature had a neural networkresponsible for the motor-sensorial coordination and for itsbehaviour, and an artificial biochemical system thatsimulates a simple energetic metabolism and an hormonal system that interacts with the neural network. A learningmechanism allows the neural network to keep adaptingduring Creature’s life.

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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 andtheir neighbourhood and populate them with virtual residents ("Sims"). Every Sim can be created with a greatdiversity of personalities and physical traits.

• Sims behaviour depends on their environment as well at thepersonality 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 "smartterrain" by its designer Will Wright) incorporate inside themall the possible behaviours and actions related to thatobject. That makes adding new objects to the game easier.

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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 anevolution of the creatures in which new breeds ofcreatures make different ecological niches araise.

• In this simulation the creatures lives on a height landscapecontaining water, sand, soil, rocks, grass, trees etc.

• All creatures are landborn four legged and have a numberof genes determining their physical properties, such as how well they can digest different forms of food, the length andsize of different body parts, etc.

• Their genome also includes a simple but flexible fuzzybehaviour based AI brain that allows the creatures toevolve different behaviours.

• Simulations typically start out as dumb grasseater with a high mortality but after a while the creatures split up intodifferent evolutionary paths and creatures such as carrioneaters and carnivores emerge.EX

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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 oforganisms, physical structure (body) andneural 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.EX

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Bibliography• Tierra: www.his.atr.jp/~ray/tierra/• Avida: http://dllab.caltech.edu/avida/• Physis: http://physis.sourceforge.net/• Galapagos: http://www.lysator.liu.se/~mbrx/galapagos/• Wikipedia: www.wikipedia.org• Course on Artificial Life by University of Zurich:

http://ailab.ch/teaching/classes/2003ss/alife• Course on Artificial Life:

http://www.ifi.unizh.ch/groups/ailab/teaching/AL00.html• Vida artificial, Un enfoque desde la Informática Teórica:

http://members.tripod.com/~MoisesRBB/vida.html• Digitales Leben:

http://homepages.feis.herts.ac.uk/~comqdp1/Studienstiftung/tierra_avida_hysis.ppt

• GNU/Linux AI & Alife HOWTO: http://zhar.net/gnu-linux/howto/html/ai.html

• Matrem: www.phys.uu.nl/~romans/

Bibliography• Diffusion-Limited Aggregation:

http://classes.yale.edu/fractals/Panorama/Physics/DLA/DLA.html• DLA - Diffusion Limited Aggregation:

http://astronomy.swin.edu.au/~pbourke/fractals/dla/• John Conway's solitaire game "life“: http://ddi.cs.uni-

potsdam.de/HyFISCH/Produzieren/lis_projekt/proj_gamelife/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/siggraph97-course/cwr87/

• Creatures: Artificial Life Autonomous Software Agents for HomeEntertainment: 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/

artificial life (2005)

Technology