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ARTIFICIAL LIFE

ARTIFICIAL LIFE. Gourab Mukhopadhyay. What is life . There is no generally accepted definition of life .

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ARTIFICIAL LIFE

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  1. ARTIFICIAL LIFE Gourab Mukhopadhyay

  2. What is life There is no generally accepted definition of life. In general, it can be saidthattheconditionthatdistinguishes living organismsfrominorganicobjectsordeadorganismsgrowththroughmetabolism, a means of reproduction, and internalregulation in response totheenvironment. Eventhoughtheabilityto reproduce isconsideredessentialtolife, thismight be more true forspeciesthanfor individual organisms. Someanimalsare incapable of reproducing, e.g. mules, soldierants/beesorsimplyinfertileorganisms. Doesthis mean they are notalive?

  3. What is Artificial Life? Artificial liferesearchershaveoftenbeendividedintotwomaingroups: • 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.

  4. Thestudy of man-madesystemsthatexhibitbehaviorscharacteristic of natural livingsystems . • Itcameintobeing at theend of the ’80swhenChristopher G. Langtonorganizedthefirstworkshoponthatsubject in Los Alamos National Laboratory in 1987, withthetitle: "International Conference on the Synthesis and Simulation of Living Systems

  5. Basic of Artificial life • Artificial Life tries totranscendthelimitationtoEarthboundlife, basedbeyondthecarbon-chain, ontheassumptionthatlifeis a property of theorganization of matter, ratherthan a property of thematteritself. • Ifwecapturedtheessentialspirit of antbehavior in the rules for virtual ants, the virtual ants in thesimulatedantcolonyshouldbehave as real ants in a real antcolony. • Synthesisof complexsystemsfrommany simple interactingentities. • Property of a system as a wholenotcontained in any of itsparts. Suchemergentbehaviorresultsfromtheinteraction of theelements of suchsystem, whichactfollowinglocal, low-level rules.

  6. The Goal of Artificil life • Thegoal of Artificial Lifeisnotonlytoprovidebiologicalmodelsbutalsotoinvestigate general principles of Life. • Theseprinciples can be investigated in theirownright, withoutnecessarilyhavingtohave a direct natural equivalent

  7. Linear vs. Non-Linear Models • Linear models are unableto describe many natural phenomena. • In a linear model, thewholeisthe sum of itsparts, and smallchanges in modelparametershavelittleeffectonthebehavior of themodel. • Manyphenomenasuch as weather, growth of plants, trafficjams, flocking of birds, stock marketcrashes, development of multi-cellularorganisms, patternformation in nature (forexampleon sea shells and butterflies), evolution, intelligence, and so forthresistedanylinearization; thatis, no satisfying linear modelwaseverfound

  8. Non-linear models can exhibit a number of featuresnotknownfrom linear ones: • Chaos: Small changes in parameters or initial conditions can lead to qualitatively different outcomes. • Emergentphenomena: Occurrence of higherlevelfeaturesthatweren’texplicitlymodelled. • As a maindisadvantage, non-linear modelstypicallycannot be solvedanalytically, in contrastwith Linear Models. Nonlinearmodelingbecamemanageableonlywhenfastcomputerswereavailable . • Modelsused in Artificial Life are always non-linear.

  9. LindenmeyerSystems • LindenmayerSystemsor L-systems are a mathematicalformalismproposed in 1968 bybiologistAristidLindenmayer as a basisforanaxiomatictheoryonbiologicaldevelopment. • Thebasic idea underlaying L-Systemsisrewriting: Components of a single object are replacedusingpredefinedrewriting rules. • Itsmainapplicationfieldisrealisticplantsmodelling and fractals. • They’rebased in symbolic rules that define thegraphicstructuregeneration, startingfrom a sequence of characters. • Only as smallamount of informationisneededtorepresentverycomplexmodels

  10. DiffusionLimitedAggregation (DLA) • "Diffusionlimitedaggregation, a kineticcriticalphenomena“, PhysicalReviewLetters, num. 47, published in 1981. • Itreproduces thegrowth of vegetal entitieslikemosses, seaweedorlichen, and chemicalprocessessuch as electrolysis or the crystallization of certain products. • A number of movingparticles are freedinsideanenclosurewherewehavealreadyoneor more particlesfixed. • Free particleskeepmoving in a Brownianmotionuntiltheyreach a fixedparticlenearby. In that case theyfixthemselvestoo.

  11. Agent-basedModelling • Computationalmodelbased in theanalysis of specificindividualssituated in anenvironment, forthestudy of complexsystems. • Themodelwasconceptuallydeveloped at theend of the ’40s, and hadtowaitforthearrival of computersto be abletodeveloptotally. • The idea istobuildtheagents, orcomputationaldevices, and simulatethem in parallelto be abletomodelthe real phenomenathatisbeinganalysed. • Theresultingprocessistheemergencyfromlowerlevels of the social system (micro) towardstheupperlevels (macro).

  12. Simulationsbased in agentshavetwoessentialcomponents: • Agents • Environment • Theenvironment has a certainautonomyfromtheactions of theagents, althoughit can be modifiedbytheirbehaviour. • Theinteractionbetweentheagentsissimulated, as well as theinteractionbetweentheagents and theirsurroundingenvironment.

  13. DistributedIntelligence • Complexbehaviourpatterns of a group, in whichthereis no central command. • Itarisesfrom “emergentbehaviour”. • Itappearsin a group as a whole, butisno explicitlyprogrammed in none of the individual members of thegroup. • Simple behaviour rules in the individual members of thegroup can cause a complexbehaviourpattern of thegroup as a whole. • Thegroupisabletosolvecomplexproblems a partir onlylocal information. • Examples: Social insects, immunologicalsystem, neural net processing

  14. Didabots • Experimentcarriedon in 1996, studyingthecollectivebehaviour of simple robots, calledDidabots. • Themain idea istoverifythatapparentlycomplexbehaviourpatterns can be a consequence of very simple rulesthat guide theinteractionsbetweentheentities and theenvironment. • This idea has beensuccessfullyappliedforexampletothestudy of social insects.

  15. Infraredsensors can be usedtodetectproximity up toabout 5 cm. • Programmedexclusivelyforavoidingobstacles. • Sensorial stimulation of theleft sensor makesthebotturn a bit totheright, and viceversa

  16. Social Insects • Themainqualityforthe so-called social insects, antsorbees, istoformpart of a self-organisedgroup, whosekeyaspectis “simplicity”. • Theseinsectssolvetheircomplexproblemsthroughthe sum of simple interactions of every individual insect

  17. Bees • Thedistribution of brood and nourishment in thecomb of honeybeesisnotrandom, butforms a regular pattern . • The central broodingregioniscloseto a regioncontainingpollen and onecontainingnectar (providingprotein and carbohydratesforthebrood). • Duetotheintake and outtake of pollen and nectar, thepatternischangingallthe time on a local scale, butitstaysstableifobservedfrom a more global scale.

  18. Thedistribution of brood and nourishment in thecomb of honeybeesisnotrandom, butforms a regular pattern . • The central broodingregioniscloseto a regioncontainingpollen and onecontainingnectar (providingprotein and carbohydratesforthebrood). • Duetotheintake and outtake of pollen and nectar, thepatternischangingallthe time on a local scale, butitstaysstableifobservedfrom a more global scale.

  19. Ants • Ants are abletofindtheshortestpathbetween a foodsource and theiranthillwithoutusing visual references. • They are alsoabletofind a new path, theshortestone, when a new obstacleappears and theoldpathcannot be usedany more. • Eventhoughanisolatedantmovesrandomly, itpreferstofollow a pheromone-richpath. Whenthey are in a group, then, they are abletomake and maintain a paththroughthepheromonestheyleavewhentheywalk. • Antswhoselecttheshortestpathgettotheirdestinationsooner. Theshortestpathreceivesthen a higheramount of pheromones in a certain time unit. As a consequence, a highernumber of antswillfollowthisshorterpath.

  20. SelfReplication • SelfReplicationistheprocess in whichsomethingmakes copies of itself. • Biologicalcells, in anadequateenvironment, do replicatethemselvesthroughcellulardivision. • Biologicalviruses reproduce themselvesbyusingthereproductivemechanisms of thecellstheyinfect. • Computer virus reproduce themselvesbyusingthe hardware and software alreadypresent in computers. • Memes do reproduce themselvesusing human mind as theirreproductivemachinery.

  21. SelfReplicantCellularAutomata • In 1948, mathematician von Neumann approached the topic of self-replication fromanabstractpoint of view. He usedcellularautomata and pointedoutforthefirst time thatitwasnecessarytodistinguishbetween hardware and software. • Unfortunately, Von Neumann’sselfreproductiveautomataweretoobig (80x400 cells) and complex (29 states) to be implemented. • In 1968, E. F. Coddloweredthenumber of neededstatesfrom 29 to 8, introducingthe concept of ‘sheaths’: twolayers of a particular stateenclosing a single ‘wire’ of informationflow. • In 1979, C. Langtondevelopsanautomatawithselfreproductivecapacity. He realisedthatsuch a structureneednot be capable of universal constructionlikethosefrom von Neumann and Codd. Itjustneedsto be ableto reproduce itsownstructure.

  22. Biomorphs • Createdby Richard Dawkins in thethirdchapter of hisbook “TheBlindWatchmaker”. • Theprogramisableto show thepower of micromutactions and accumulativeselection. • BiomorphViewerletstheusermovethroughthegeneticspace (of 9 dimensions in this case) and keepselectingthedesiredshape. • User’seyetakethe role of natural selection.

  23. Biomorphs

  24. EvolutiveAlgorithms • GeneticAlgorithms: Themostcommonform of evolutivealgorithms. Thesolutionto a problemissearch as a textor a bunch of numbers (usuallybinary), aplyingmutation and recombinationoperatorsand performing a selectiononthepossiblesolutions.

  25. GeneticProgramming: Solutions in this case are computerprograms, and theirfitnessisdeterminedbytheirabilitytosolve a computationalproblem

  26. Artificial Chemistry • Artificial Chemistryisthecomputersimulation of chemicalprocesses in a similar waytothatfound in real world. • It can be thefoundation of an artificial lifeprogram, and in that case usuallysomekind of organicchemistryissimulated

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