Miriam Ruiz Artificial Life
Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
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?
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".
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.
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 .
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.
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.
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 .
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.
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.
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.
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 .
Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
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 .
Lindenmeyer Systems EMERGENT PATTERNS > L-Systems
Lindenmeyer Systems EMERGENT PATTERNS > L-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).
Lindenmeyer Systems EMERGENT PATTERNS > L-Systems
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.
Diffusion Limited Aggregation (DLA) EMERGENT PATTERNS > DLA
Diffusion Limited Aggregation (DLA) EMERGENT PATTERNS > DLA
Diffusion Limited Aggregation (DLA) EMERGENT PATTERNS > DLA
Diffusion Limited Aggregation (DLA) EMERGENT PATTERNS > DLA
Contents • Introduction • Emergent Patterns • Cellular Automata • Agent-based modelling • Distributed Intelligence • Artificial Evolution • Artificial Chemistry • Examples • Bibliography
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.
Wolfram’s Cellular Automata CELLULAR AUTOMATA > Wolfram CAs • 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”.
Wolfram’s Cellular Automata CELLULAR AUTOMATA > Wolfram CAs
Wolfram’s Cellular Automata CELLULAR AUTOMATA > Wolfram CAs
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