ARTIFICIAL LIFE Gourab Mukhopadhyay What is life There is no generally accepted definition of life. In general, it can be said that the condition that distinguishes living organisms from inorganic objects or dead organisms growth through metabolism, a means of reproduction, and internal regulation in response to the environment. Even though the ability to reproduce is considered essential to life, this might be more true for species than for individual organisms. Some animals are incapable of reproducing, e.g. mules, soldier ants/bees or simply infertile organisms. Does this mean they are not alive? What is Artificial Life? Artificial life researchers have often been divided into two main groups:  The strong alife position states that life is a process which can be abstracted away from any particular medium.  The weak alife position denies the possibility of generating a "living process" outside of a carbon-based chemical solution. Its researchers try instead to mimic life processes to understand the appearance of individual phenomena.  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 Basic of Artificial life  Artificial Life tries to transcend the limitation to Earth bound life, based beyond the carbon-chain, on the assumption that life is a property of the organization of matter, rather than a property of the matter itself.  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.  Synthesis of complex systems from many simple interacting entities.  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 Goal of Artificil life  The goal of Artificial Life is not only to provide biological models but also to investigate general principles of Life.  These principles can be investigated in their own right, without necessarily having to have a direct natural equivalent Linear vs. Non-Linear Models  Linear models are unable to describe many natural phenomena.  In a linear model, the whole is the sum of its parts, and small changes in model parameters have little effect on the behavior of the model.  Many phenomena such as weather, growth of plants, traffic jams, flocking of birds, stock market crashes, development of multi-cellular organisms, pattern formation in nature (for example on sea shells and butterflies), evolution, intelligence, and so forth resisted any linearization; that is, no satisfying linear model was ever found   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. 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 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. Agent-based Modelling  Computational model based in the analysis of specific individuals situated in an environment, for the study of complex systems.  The model was conceptually developed at the end of the ’40s, and had to wait for the arrival of computers to be able to develop totally.  The idea is to build the agents, or computational devices, and simulate them in parallel to be able to model the real phenomena that is being analysed.  The resulting process is the emergency from lower levels of the social system (micro) towards the upper levels (macro).  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. Distributed Intelligence  Complex behaviour patterns of a group, in which there is no central command.  It arises from “emergent behaviour”.  It appears in a group as a whole, but is no explicitly programmed in none of the individual members of the group.  Simple behaviour rules in the individual members of the group can cause a complex behaviour pattern of the group as a whole.  The group is able to solve complex problems a partir only local information.  Examples: Social insects, immunological system, neural net processing 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.  Infrared sensors can be used to detect proximity up to about 5 cm.  Programmed exclusively for avoiding obstacles.  Sensorial stimulation of the left sensor makes the bot turn a bit to the right, and viceversa Social Insects  The main quality for the so-called social insects, ants or bees, is to form part of a self-organised group, whose key aspect is “simplicity”.  These insects solve their complex problems through the sum of simple interactions of every individual insect 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.  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. Ants  Ants are able to find the shortest path between a food source and their anthill without using visual references.  They are also able to find a new path, the shortest one, when a new obstacle appears and the old path cannot be used any more.  Even though an isolated ant moves randomly, it prefers to follow a pheromone-rich path. When they are in a group, then, they are able to make and maintain a path through the pheromones they leave when they walk.  Ants who select the shortest path get to their destination sooner. The shortest path receives then a higher amount of pheromones in a certain time unit. As a consequence, a higher number of ants will follow this shorter path. Self Replication  Self Replication is the process in which something makes copies of itself.  Biological cells, in an adequate environment, do replicate themselves through cellular division.  Biological viruses reproduce themselves by using the reproductive mechanisms of the cells they infect.  Computer virus reproduce themselves by using the hardware and software already present in computers.  Memes do reproduce themselves using human mind as their reproductive machinery. Self Replicant Cellular Automata  In 1948, mathematician von Neumann approached the topic of self-replication from an abstract point of view. He used cellular automata and pointed out for the first time that it was necessary to distinguish between hardware and software.  Unfortunately, Von 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. 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. Biomorphs Evolutive Algorithms  Genetic Algorithms: The most common form of evolutive algorithms. The solution to a problem is search as a text or a bunch of numbers (usually binary), aplying mutation and recombination operators and performing a selection on the possible solutions.  Genetic Programming: Solutions in this case are computer programs, and their fitness is determined by their ability to solve a computational problem Artificial Chemistry  Artificial Chemistry is the computer simulation of chemical processes in a similar way to that found in real world.  It can be the foundation of an artificial life program, and in that case usually some kind of organic chemistry is simulated