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Artificial life (2005)


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Artificial life (2005)

  1. 1. Artificial Life Miriam Ruiz
  2. 2. Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
  3. 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 INTRODUCTION > What is Life 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?
  4. 4. What is Artificial Life? • The study of man-made systems that exhibit behaviors characteristic of natural living INTRODUCTION > What is Artificial Life 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".
  5. 5. What is Artificial Life? Artificial life researchers have often been divided into two main groups: INTRODUCTION > What is Artificial Life • 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.
  6. 6. What is Artificial Life? • The goal of Artificial Life is not only to provide biological models but also to INTRODUCTION > What is Artificial Life investigate general principles of Life. • These principles can be investigated in their own right, without necessarily having to have a direct natural equivalent.
  7. 7. The Basis of Artificial Life • Artificial Life tries to transcend the limitation INTRODUCTION > The Basis of Artificial Life 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.
  8. 8. The Basis of Artificial Life • Synthetic Approach: Synthesis of INTRODUCTION > The Basis of Artificial Life 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.
  9. 9. The Basis of Artificial Life • Self-Organization: Spontaneous formation INTRODUCTION > The Basis of Artificial Life 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.
  10. 10. The Basis of Artificial Life • Levels of Organization: Life, as we INTRODUCTION > The Basis of Artificial Life know it on Earth, is organized into at least four levels of structure: – Molecular level. – Cellular level. – Organism level. – Population-ecosystem level.
  11. 11. The Basis of Artificial Life • We have to distinguish between the perspective of an observer looking at an creature and the INTRODUCTION > The Basis of Artificial Life 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.
  12. 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 INTRODUCTION > Linear Models 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.
  13. 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 INTRODUCTION > Non-Linear Models 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.
  14. 14. Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
  15. 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. EMERGENT PATTERNS > L-Systems • 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.
  16. 16. EMERGENT PATTERNS > L-Systems Lindenmeyer Systems
  17. 17. EMERGENT PATTERNS > L-Systems 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).
  18. 18. EMERGENT PATTERNS > L-Systems Lindenmeyer Systems
  19. 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 EMERGENT PATTERNS > DLA 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.
  20. 20. Diffusion Limited Aggregation (DLA) EMERGENT PATTERNS > DLA
  21. 21. Diffusion Limited Aggregation (DLA) EMERGENT PATTERNS > DLA
  22. 22. Diffusion Limited Aggregation (DLA) EMERGENT PATTERNS > DLA
  23. 23. Diffusion Limited Aggregation (DLA) EMERGENT PATTERNS > DLA
  24. 24. Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
  25. 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. CELLULAR AUTOMATA > Introduction • 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.
  26. 26. CELLULAR AUTOMATA > Wolfram CAs 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”.
  27. 27. CELLULAR AUTOMATA > Wolfram CAs Wolfram’s Cellular Automata
  28. 28. CELLULAR AUTOMATA > Wolfram CAs Wolfram’s Cellular Automata
  29. 29. 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. CELLULAR AUTOMATA > Wolfram CAs • 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.
  30. 30. CELLULAR AUTOMATA > Wolfram CAs Class IV CA Examples
  31. 31. CELLULAR AUTOMATA > Wolfram CAs 1-D CA Example: Seashells
  32. 32. CELLULAR AUTOMATA > Conway’s Game of Life 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 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 cell’s birth. • The result is a Turing-Complete system.
  33. 33. CELLULAR AUTOMATA > Conway’s Game of Life Conway’s Game of Life
  34. 34. CELLULAR AUTOMATA > Conway’s Game of Life Conway’s Game of Life
  35. 35. Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
  36. 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 AGENTS > Introduction 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).
  37. 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 AGENTS > Introduction 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.
  38. 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 AGENTS > Chimps 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.
  39. 39. AGENTS > Chimps Artificial Societies: Chimps
  40. 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 AGENTS > Chimps 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.
  41. 41. AGENTS > Chimps Artificial Societies: Chimps
  42. 42. Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
  43. 43. Distributed Intelligence • Complex behaviour patterns of a group, in which DISTRIBUTED INTELLIGENCE > Introduction 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.
  44. 44. Didabots • Experiment carried on in 1996, studying the collective behaviour of simple robots, DISTRIBUTED INTELLIGENCE > Didabots 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.
  45. 45. Didabots • Infrared sensors can be used to detect DISTRIBUTED INTELLIGENCE > Didabots 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.
  46. 46. DISTRIBUTED INTELLIGENCE > Didabots Didabots
  47. 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 DISTRIBUTED INTELLIGENCE > Didabots 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.
  48. 48. Social Insects • The main quality for the so-called social DISTRIBUTED INTELLIGENCE > Social Insects 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.
  49. 49. Bees • The distribution of brood and DISTRIBUTED INTELLIGENCE > Social Insects 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.
  50. 50. Bees • This is not the result of an individual bee DISTRIBUTED INTELLIGENCE > Social Insects 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.
  51. 51. Bees • Bees keep the thermal stability of the beehive DISTRIBUTED INTELLIGENCE > Social Insects 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 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 oppressed at the same time. That way, a thermal stability is achieved.
  52. 52. Ants • Ants are able to find the shortest path between a DISTRIBUTED INTELLIGENCE > Social Insects 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.
  53. 53. DISTRIBUTED INTELLIGENCE > Social Insects Ants
  54. 54. Boids (bird-oids) • They were invented in the mid-80s by the computer animator Craig Reynolds. DISTRIBUTED INTELLIGENCE > Boids • 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.
  55. 55. DISTRIBUTED INTELLIGENCE > Boids Boids (bird-oids)
  56. 56. Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
  57. 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 EVOLUTION > Self Replication 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.
  58. 58. Self Replicant Cellular Automata • In 1948, mathematician von Neumann approached the topic EVOLUTION > Self Replicant Cellular Automata 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 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 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.
  59. 59. EVOLUTION > Autómatas Celulares Langton Loops
  60. 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 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 EVOLUTION > Core War 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.
  61. 61. EVOLUTION > Genetic Evolution Genetic Evolution
  62. 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 EVOLUTION > Biomorphs 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.
  63. 63. EVOLUTION > Biomorphs Biomorphs
  64. 64. Karl Sims' Virtual Creatures • Developed by Karl Sims in 1994. • Sims evolves morphology and neural control. EVOLUTION > Karl Sims’ Virtual Creatures • 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.
  65. 65. EVOLUTION > Karl Sims’ Virtual Creatures Karl Sims' Virtual Creatures
  66. 66. EVOLUTION > Karl Sims’ Virtual Creatures Karl Sims' Virtual Creatures
  67. 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 EVOLUTION > Evolutive Algorithms 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.
  68. 68. EVOLUTION > Genetic Algorithms Genetic Algorithms
  69. 69. EVOLUTION > Genetic Programming Genetic Programming
  70. 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). EVOLUTION > Tierra • 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.
  71. 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. EVOLUTION > Tierra • Soon mutations only 79 instructions long proliferated - after a while even shorter organisms. Evolution had begun optimising the code.
  72. 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. EVOLUTION > Tierra • 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.
  73. 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 EVOLUTION > Tierra both. • The system continued with its evolution of competing and cooperating self-replicating organisms
  74. 74. EVOLUTION > Tierra Tierra • Many hosts (red) • Some parasites appear (yellow)
  75. 75. EVOLUTION > Tierra Tierra • Parasites have increased a lot. • Hosts are lowering. • The first immune creatures (blue) appear
  76. 76. EVOLUTION > Tierra Tierra • Parasites are spacially displaced. • Non-immunte hosts lower even more. • Immune creatures keep increasing and diplace the parasites.
  77. 77. EVOLUTION > Tierra Tierra • Parasites are even more scarce. • Non-immune hosts keep lowering. • Immune creatures are the domintant life form.
  78. 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. EVOLUTION > Avida • It’s similar to Tierra, but: – Has a virtual CPU for each program. – Creatures can evolve for more than just reproduction. Configurable fitness function.
  79. 79. EVOLUTION > Avida AVida
  80. 80. 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
  81. 81. Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
  82. 82. Artificial Chemistry • Artificial Chemistry is the computer simulation of chemical processes in a ARTIFICIAL CHEMISTRY > Introduction 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.
  83. 83. Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
  84. 84. EXAMPLES > Games > SimLife SimLife
  85. 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 EXAMPLES > Games > SimLife 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.
  86. 86. EXAMPLES > Games > Creatures Creatures
  87. 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 EXAMPLES > Games > Creatures 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.
  88. 88. EXAMPLES > Games > The Sims The Sims
  89. 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 EXAMPLES > Games > The Sims 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 object. That makes adding new objects to the game easier.
  90. 90. EXAMPLES > Galapagos Galapagos
  91. 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 EXAMPLES > Galapagos 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.
  92. 92. EXAMPLES > FramSticks FramSticks
  93. 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 EXAMPLES > FramSticks 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.
  94. 94. Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
  95. 95. Bibliography • Tierra: • Avida: • Physis: • Galapagos: • Wikipedia: • Course on Artificial Life by University of Zurich: • Course on Artificial Life: • Vida artificial, Un enfoque desde la Informática Teórica: • Digitales Leben: _hysis.ppt • GNU/Linux AI & Alife HOWTO: linux/howto/html/ai.html • Matrem:
  96. 96. Bibliography • Diffusion-Limited Aggregation: • DLA - Diffusion Limited Aggregation: • John Conway's solitaire game "life“: http://ddi.cs.uni- entificAmerican.htm • Boids, background and update, by Craig Reynolds: • Flocks, Herds, and Schools: A Distributed Behavioral Model: • Creatures: Artificial Life Autonomous Software Agents for Home Entertainment: • Evolving Virtual Creatures: • Core War, artículos escaneados de A.K. Dewdney: • FramSticks: • StarLogo: