Natural Computing: A Brief Survey of Ideas and Applications BIC 2005:  International Symposium on Bio-Inspired Computing J...
<ul><li>Imagine a world where computers can create new universes, and within these universes there are natural forms that ...
Outline <ul><li>Part I : Introduction and Motivation </li></ul><ul><ul><li>Some ideas and challenges </li></ul></ul><ul><l...
Part I Introduction and Motivation
Current Computer Technology <ul><li>Turing  Machines (TM) </li></ul><ul><ul><li>Computational device idealized by A. Turin...
Features of Current Computers <ul><li>General-purpose machines </li></ul><ul><li>Manipulate precisely precise information*...
Are You Ready? <ul><li>Develop a computer program to distribute products of a company throughout the country. </li></ul><u...
Why Are These Questions Hard? (1. Products Distribution) <ul><li>How many are the possible routes? </li></ul>
Why Are These Questions Hard? (2. Behavioral Simulation)
Why Are These Questions Hard? (3. New Technologies) <ul><li>Moore’s Law: </li></ul><ul><ul><li>The processing power of sil...
What all these questions have in common? <ul><li>The answer to all of them require a paradigm shift </li></ul>Where can we...
Part II Looking at Nature with Different Eyes ---- Nature’s Solutions: Some Samples
Natural Architects
Natural Deliverers and Cleaners
Natural Behavior Animators
Natural Computer
Part III Natural Computing
From Nature to Computing:  Natural Computing <ul><li>Nature x Computing </li></ul><ul><li>Natural computing is the termino...
The Philosophy of Natural Computing
Part III-A Computing Inspired by Nature
Main Ideas <ul><li>Nature has evolved through ages in order to solve complex real-world problems </li></ul><ul><li>Example...
Main Themes <ul><li>Neurocomputing </li></ul><ul><li>Evolutionary Computing </li></ul><ul><li>Swarm Intelligence </li></ul...
Neurocomputing <ul><li>Inspiration </li></ul>
<ul><li>Design principles: </li></ul><ul><ul><li>Artificial neuron: basic information processing and storage unit </li></u...
Neurocomputing <ul><li>Basic artificial neuron </li></ul><ul><li>Some activation functions </li></ul>
<ul><li>Network architectures </li></ul><ul><ul><li>Single-layer feedforward network </li></ul></ul>Neurocomputing
<ul><li>Network architectures </li></ul><ul><ul><li>Multi-layer feedforward network </li></ul></ul>Neurocomputing
<ul><li>Network architectures </li></ul><ul><ul><li>Recurrent network </li></ul></ul>Neurocomputing
<ul><li>Learning algorithms/rules: </li></ul><ul><ul><li>Hebb learning </li></ul></ul><ul><ul><li>Single-layer perceptron ...
<ul><li>Why neurocomputing? </li></ul><ul><ul><li>Learning capability </li></ul></ul><ul><ul><li>Parallel processing </li>...
<ul><li>Scope: </li></ul><ul><ul><li>Function approximation </li></ul></ul><ul><ul><li>Clustering </li></ul></ul><ul><ul><...
Evolutionary Computing <ul><li>Inspiration </li></ul>+  Reproduction  +  Genetic Variation  +  Selection
<ul><li>The power of (artificial) evolution </li></ul>Evolutionary Computing
Evolutionary Computing <ul><li>The power of evolution </li></ul>
<ul><li>Design principles: </li></ul><ul><ul><li>Population of individuals* </li></ul></ul><ul><ul><li>Reproduction with g...
<ul><li>Standard evolutionary algorithm </li></ul>Evolutionary Computing procedure  [ P ] = standard_EA( pc , pm ) initial...
<ul><li>Main types of evolutionary algorithms: </li></ul><ul><ul><li>Evolutionary programming </li></ul></ul><ul><ul><li>E...
<ul><li>Why evolutionary computing? </li></ul><ul><ul><li>A population may explore and exploit more efficiently than a sin...
<ul><li>Scope: </li></ul><ul><ul><li>Search and optimization </li></ul></ul><ul><ul><li>Planning (e.g. routing, scheduling...
<ul><li>Systems based on the collective behavior of social organisms </li></ul><ul><li>Two main approaches: </li></ul><ul>...
<ul><li>An inspiration </li></ul>Swarm Intelligence
Swarm Intelligence <ul><li>An ant farm </li></ul>
<ul><li>An ant farm </li></ul>Swarm Intelligence
<ul><li>Another inspiration </li></ul>Swarm Intelligence
<ul><li>Robotic autonomous navigation </li></ul>Swarm Intelligence
<ul><li>Why swarm intelligence? </li></ul><ul><ul><li>Again, a multi-agent approach may allow for a better exploration and...
<ul><li>Scope: </li></ul><ul><ul><li>Search and optimization: </li></ul></ul><ul><ul><ul><li>Discrete and continuous optim...
Immunocomputing <ul><li>Inspiration </li></ul>
<ul><li>Design principles: </li></ul><ul><ul><li>Representation </li></ul></ul><ul><ul><li>Architecture </li></ul></ul><ul...
<ul><li>Representation </li></ul><ul><ul><li>Set of coordinates:  m  =   m 1,  m 2, ...,  mL  ,  m     SL       L </l...
Immunocomputing <ul><li>Affinities: related to distance/similarity </li></ul><ul><li>Examples of affinity measures </li></...
Immunocomputing <ul><li>Algorithms and Processes </li></ul><ul><ul><li>Generic algorithms based on specific immune princip...
<ul><li>Exemple of application: </li></ul>Immunocomputing
<ul><li>Another example of application: </li></ul>Immunocomputing
<ul><li>Why immunocomputing? </li></ul><ul><ul><li>Adaptability </li></ul></ul><ul><ul><li>Robustness </li></ul></ul><ul><...
<ul><li>Scope: </li></ul><ul><ul><li>Pattern recognition </li></ul></ul><ul><ul><li>Fault and anomaly detection, and the s...
Part III-B Artificial Life and Fractal Geometry
Main Ideas <ul><li>Biosciences: reductionist approach to understanding life </li></ul><ul><li>Artificial Life & Fractal Ge...
Artificial Life <ul><li>What is life? </li></ul><ul><ul><li>“ The property or quality that distinguishes living organisms ...
<ul><li>Some poetical definitions of life </li></ul><ul><ul><li>“ Life is a long process of getting tired” (Samuel Butler)...
<ul><li>Artificial Life: </li></ul><ul><ul><li>“ Artificial Life is the study of man-made systems that exhibit behaviors c...
<ul><li>“ Artificial Life (AL) is the enterprise of understanding biology by constructing biological phenomena out of arti...
<ul><li>“ Alife is a constructive endeavor: Some researchers aim at evolving patterns in a computer; some seek to elicit s...
Artificial Life <ul><li>Natural Life: An instance </li></ul>
<ul><li>Boids: Simple Behavioral Rules </li></ul><ul><ul><li>Collision avoidance and separation </li></ul></ul><ul><ul><li...
<ul><li>Boids </li></ul>Artificial Life
<ul><li>AIBO ERS 210 </li></ul>Artificial Life
Artificial Life
<ul><li>Wasp Nest Building </li></ul>Artificial Life
<ul><li>Creatures: Adaptive learning through interaction </li></ul>Artificial Life
<ul><li>Artificial fishes: Predator behavior </li></ul>Artificial Life
<ul><li>Traffic simulation: What is needed for a jam? </li></ul>Artificial Life
<ul><li>Life-as-it-is x life-as-it-could-be </li></ul>Artificial Life
<ul><li>Why Artificial Life? </li></ul><ul><ul><li>Increases our understanding of life </li></ul></ul><ul><ul><li>Provides...
Fractal Geometry <ul><li>“ Why is geometry often described as ‘cold’ and ‘dry’? One reason lies in its inability to descri...
Fractal Geometry <ul><li>Some Tools: </li></ul><ul><ul><li>Cellular automata </li></ul></ul><ul><ul><li>Iterated function ...
<ul><li>Cellular automata </li></ul><ul><ul><li>Dynamical system that is discrete in both space and time  </li></ul></ul><...
<ul><li>Cellular automata </li></ul>Fractal Geometry
<ul><li>Lindenmayer Systems </li></ul><ul><ul><li>A formalism to simulate the development of multicellular organisms </li>...
<ul><li>Lindenmayer Systems (without rendering) </li></ul>Fractal Geometry
<ul><li>Lindenmayer Systems (with rendering) </li></ul>Fractal Geometry
Fractal Geometry <ul><li>A Natural Fern </li></ul>
<ul><li>(Random) Iterated Function Systems </li></ul><ul><ul><li>An  iterated function system  (IFS) consists of a complet...
<ul><li>A Fern Generated with a RIFS </li></ul>Fractal Geometry
<ul><li>Brownian Motion </li></ul><ul><ul><li>To model some natural sceneries, it is necessary to have curves that look di...
<ul><li>Fractional Brownian Motion (without rendering) </li></ul>Fractal Geometry
<ul><li>Fractional Brownian Motion (with rendering) </li></ul>Fractal Geometry
<ul><li>Particle Systems </li></ul><ul><ul><li>Modeling physical phenomena like the flowing, dripping and pouring of liqui...
Fractal Geometry <ul><li>Particle Systems </li></ul><ul><li>See http://www.cs.wpi.edu/~matt/courses/cs563/talks/psys.html ...
<ul><li>Why fractal geometry? </li></ul><ul><ul><li>A computationally cheap way of generating computer models of nature </...
Part III-C Computing with New Natural Material
<ul><li>If current computing technology will reach its limit in the near future, what would be the alternative material wi...
Computing with Natural Material <ul><li>DNA Computing </li></ul>
<ul><li>Quantum Computing </li></ul><ul><ul><li>Quantum bit: | x   =  c 1|0   +  c 2|1  </li></ul></ul>Computing with N...
Part IV Computing in the New Millennium
<ul><li>Some ideas that form the basis of natural computing: </li></ul><ul><ul><li>Capacity of dealing with complex proble...
Computing in the New Millennium <ul><li>From singularity to plurality  </li></ul>
<ul><li>The importance of nature has never been so great! </li></ul>Computing in the New Millennium
Main Reference <ul><li>Fundamentals of Natural Computing, Concepts, Algorithms, and Applications; by Leandro de Castro, CR...
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2005: Natural Computing - Concepts and Applications

  1. 1. Natural Computing: A Brief Survey of Ideas and Applications BIC 2005: International Symposium on Bio-Inspired Computing Johor, MY, 9 th September 2005 Dr. Leandro Nunes de Castro [email_address] http://lsin.unisantos.b/lnunes Catholic University of Santos - UniSantos/Brazil
  2. 2. <ul><li>Imagine a world where computers can create new universes, and within these universes there are natural forms that reproduce, grow and adapt. Imagine natural patterns, mountains, ant colonies, immune systems and brains, all learning and evolving, and becoming increasingly more adapted to the environment. Imagine if our computers could contain new forms of life. Think how this would affect our lives. Maybe we could automatically create house and music design, new forms of protecting computers against invaders, new forms of solving complex problems, new organisms and new forms of computing. </li></ul><ul><li>Now stop imagining. </li></ul><ul><li>Welcome to Computing in the New Millennium. </li></ul><ul><li>Welcome to the Natural Computing age! </li></ul>Foreword Adapted from Digital Biology, by P. Bentley.
  3. 3. Outline <ul><li>Part I : Introduction and Motivation </li></ul><ul><ul><li>Some ideas and challenges </li></ul></ul><ul><li>Part II: Looking at Nature with Different Eyes </li></ul><ul><ul><li>Nature’s solutions: Some samples </li></ul></ul><ul><li>Part III : Natural Computing </li></ul><ul><ul><li>Computing inspired by nature </li></ul></ul><ul><ul><li>The simulation and emulation of natural phenomena in computers </li></ul></ul><ul><ul><li>Computing with natural materials </li></ul></ul><ul><li>Part III: Computing in the New Millennium </li></ul>
  4. 4. Part I Introduction and Motivation
  5. 5. Current Computer Technology <ul><li>Turing Machines (TM) </li></ul><ul><ul><li>Computational device idealized by A. Turing in 1936 </li></ul></ul><ul><ul><li>If a problem can be computed, then it can be computed by a Turing Machine </li></ul></ul><ul><li>J. von Neumman architecture </li></ul>
  6. 6. Features of Current Computers <ul><li>General-purpose machines </li></ul><ul><li>Manipulate precisely precise information* </li></ul><ul><li>Address-based memory </li></ul><ul><li>Serial processing* </li></ul><ul><li>Are not capable of generalizing </li></ul><ul><li>Are not fault tolerant (robust) </li></ul><ul><li>Are not adaptable* </li></ul><ul><li>… </li></ul>
  7. 7. Are You Ready? <ul><li>Develop a computer program to distribute products of a company throughout the country. </li></ul><ul><li>Generate a computer model to simulate the evacuation program of a building undergoing fire. </li></ul><ul><li>What are the new technologies to complement or supplement silicon-based hardware? </li></ul>
  8. 8. Why Are These Questions Hard? (1. Products Distribution) <ul><li>How many are the possible routes? </li></ul>
  9. 9. Why Are These Questions Hard? (2. Behavioral Simulation)
  10. 10. Why Are These Questions Hard? (3. New Technologies) <ul><li>Moore’s Law: </li></ul><ul><ul><li>The processing power of silicon-based computers doubles approximately every couple of years </li></ul></ul><ul><ul><li>By the end of the next decade (2020) we may have reached the (miniaturization) limit of current computer technology </li></ul></ul>N. of atoms per bit Year 2020: 1 atom per bit
  11. 11. What all these questions have in common? <ul><li>The answer to all of them require a paradigm shift </li></ul>Where can we find answers to them? <ul><li>Where all these problems and difficulties have been solved and dealt with from ages: In NATURE!! </li></ul>
  12. 12. Part II Looking at Nature with Different Eyes ---- Nature’s Solutions: Some Samples
  13. 13. Natural Architects
  14. 14. Natural Deliverers and Cleaners
  15. 15. Natural Behavior Animators
  16. 16. Natural Computer
  17. 17. Part III Natural Computing
  18. 18. From Nature to Computing: Natural Computing <ul><li>Nature x Computing </li></ul><ul><li>Natural computing is the terminology used to encompass three paradigms: </li></ul><ul><ul><li>Computing inspired by nature </li></ul></ul><ul><ul><li>The simulation and emulation of natural phenomena in computers </li></ul></ul><ul><ul><li>Computing with natural materials </li></ul></ul>
  19. 19. The Philosophy of Natural Computing
  20. 20. Part III-A Computing Inspired by Nature
  21. 21. Main Ideas <ul><li>Nature has evolved through ages in order to solve complex real-world problems </li></ul><ul><li>Examples abound: nest building, nest cleaning, main senses (hearing, seeing, touching, smelling, tasting), etc. </li></ul><ul><li>Computer algorithms based or inspired by nature have been developed for some time: </li></ul><ul><ul><li>Either to model nature, </li></ul></ul><ul><ul><li>Or to solve complex real-world problems </li></ul></ul>
  22. 22. Main Themes <ul><li>Neurocomputing </li></ul><ul><li>Evolutionary Computing </li></ul><ul><li>Swarm Intelligence </li></ul><ul><li>Immunocomputing </li></ul><ul><li>Artificial Chemistry </li></ul><ul><li>Growth and Developmental Algorithms </li></ul><ul><li>etc. </li></ul>Older approaches
  23. 23. Neurocomputing <ul><li>Inspiration </li></ul>
  24. 24. <ul><li>Design principles: </li></ul><ul><ul><li>Artificial neuron: basic information processing and storage unit </li></ul></ul><ul><ul><li>Network architecture: how the artificial neurons are interconnected </li></ul></ul><ul><ul><li>Learning algorithm: guides the dynamics (adaptability) of the system </li></ul></ul>Neurocomputing
  25. 25. Neurocomputing <ul><li>Basic artificial neuron </li></ul><ul><li>Some activation functions </li></ul>
  26. 26. <ul><li>Network architectures </li></ul><ul><ul><li>Single-layer feedforward network </li></ul></ul>Neurocomputing
  27. 27. <ul><li>Network architectures </li></ul><ul><ul><li>Multi-layer feedforward network </li></ul></ul>Neurocomputing
  28. 28. <ul><li>Network architectures </li></ul><ul><ul><li>Recurrent network </li></ul></ul>Neurocomputing
  29. 29. <ul><li>Learning algorithms/rules: </li></ul><ul><ul><li>Hebb learning </li></ul></ul><ul><ul><li>Single-layer perceptron </li></ul></ul><ul><ul><li>Adaline </li></ul></ul><ul><ul><li>ART </li></ul></ul><ul><ul><li>Multi-Layer perceptron </li></ul></ul><ul><ul><li>Self-organizing networks </li></ul></ul><ul><ul><li>Hopfield networks </li></ul></ul><ul><ul><li>Grossberg networks </li></ul></ul><ul><ul><li>… </li></ul></ul>Neurocomputing
  30. 30. <ul><li>Why neurocomputing? </li></ul><ul><ul><li>Learning capability </li></ul></ul><ul><ul><li>Parallel processing </li></ul></ul><ul><ul><li>Generalization capability </li></ul></ul><ul><ul><li>Inherently distributed </li></ul></ul><ul><ul><li>Robust </li></ul></ul><ul><ul><li>... </li></ul></ul>Neurocomputing
  31. 31. <ul><li>Scope: </li></ul><ul><ul><li>Function approximation </li></ul></ul><ul><ul><li>Clustering </li></ul></ul><ul><ul><li>Classification </li></ul></ul><ul><ul><li>Pattern recognition </li></ul></ul><ul><ul><li>Control </li></ul></ul><ul><ul><li>… </li></ul></ul><ul><li>Mature field with innumerable academic, industrial, commercial and governmental applications </li></ul>Neurocomputing
  32. 32. Evolutionary Computing <ul><li>Inspiration </li></ul>+ Reproduction + Genetic Variation + Selection
  33. 33. <ul><li>The power of (artificial) evolution </li></ul>Evolutionary Computing
  34. 34. Evolutionary Computing <ul><li>The power of evolution </li></ul>
  35. 35. <ul><li>Design principles: </li></ul><ul><ul><li>Population of individuals* </li></ul></ul><ul><ul><li>Reproduction with genetic inheritance </li></ul></ul><ul><ul><li>Genetic variation </li></ul></ul><ul><ul><li>Selection </li></ul></ul>Evolutionary Computing
  36. 36. <ul><li>Standard evolutionary algorithm </li></ul>Evolutionary Computing procedure [ P ] = standard_EA( pc , pm ) initialize P f  eval( P ) P  select( P , f ) t  1 while not_stopping_criterion do , P  reproduce( P , f ,pc ) P  variate( P ,pm ) f  eval( P ) P  select( P , f ) t  t + 1 end while end procedure
  37. 37. <ul><li>Main types of evolutionary algorithms: </li></ul><ul><ul><li>Evolutionary programming </li></ul></ul><ul><ul><li>Evolution strategies </li></ul></ul><ul><ul><li>Genetic algorithms </li></ul></ul><ul><ul><li>Genetic programming* </li></ul></ul><ul><ul><li>Classifier systems* </li></ul></ul>Evolutionary Computing
  38. 38. <ul><li>Why evolutionary computing? </li></ul><ul><ul><li>A population may explore and exploit more efficiently than a single individual </li></ul></ul><ul><ul><li>Importance of information (experience) exchange </li></ul></ul><ul><ul><li>Maintenance of good quality solutions </li></ul></ul><ul><ul><li>Diversity and creativity </li></ul></ul>Evolutionary Computing
  39. 39. <ul><li>Scope: </li></ul><ul><ul><li>Search and optimization </li></ul></ul><ul><ul><li>Planning (e.g. routing, scheduling and packing ) </li></ul></ul><ul><ul><li>Design (e.g. signal processing) </li></ul></ul><ul><ul><li>Simulation, identification, control (e.g. general plant control) </li></ul></ul><ul><ul><li>Classification (e.g. machine learning, pattern recognition and classification) </li></ul></ul>Evolutionary Computing
  40. 40. <ul><li>Systems based on the collective behavior of social organisms </li></ul><ul><li>Two main approaches: </li></ul><ul><ul><li>Systems based on the collective behavior of social insects </li></ul></ul><ul><ul><ul><li>Ant Colony Optimization (ACO) </li></ul></ul></ul><ul><ul><ul><li>Ant Clustering Algorithm (ACA) </li></ul></ul></ul><ul><ul><li>Systems based on sociocognition </li></ul></ul><ul><ul><ul><li>Particle Swarm Optimization (PSO) </li></ul></ul></ul>Swarm Intelligence
  41. 41. <ul><li>An inspiration </li></ul>Swarm Intelligence
  42. 42. Swarm Intelligence <ul><li>An ant farm </li></ul>
  43. 43. <ul><li>An ant farm </li></ul>Swarm Intelligence
  44. 44. <ul><li>Another inspiration </li></ul>Swarm Intelligence
  45. 45. <ul><li>Robotic autonomous navigation </li></ul>Swarm Intelligence
  46. 46. <ul><li>Why swarm intelligence? </li></ul><ul><ul><li>Again, a multi-agent approach may allow for a better exploration and exploitation of the space </li></ul></ul><ul><ul><li>Simple agents together can perform complicated tasks </li></ul></ul><ul><ul><li>It may be easier and cheaper to have many simple agents than a single complex one </li></ul></ul>Swarm Intelligence
  47. 47. <ul><li>Scope: </li></ul><ul><ul><li>Search and optimization: </li></ul></ul><ul><ul><ul><li>Discrete and continuous optimization </li></ul></ul></ul><ul><ul><li>Data analysis (clustering) </li></ul></ul><ul><ul><li>Robotics (autonomous navigation) </li></ul></ul>Swarm Intelligence
  48. 48. Immunocomputing <ul><li>Inspiration </li></ul>
  49. 49. <ul><li>Design principles: </li></ul><ul><ul><li>Representation </li></ul></ul><ul><ul><li>Architecture </li></ul></ul><ul><ul><li>Affinity/Fitness functions </li></ul></ul><ul><ul><li>Dynamics/Metadynamics </li></ul></ul>Immunocomputing
  50. 50. <ul><li>Representation </li></ul><ul><ul><li>Set of coordinates: m  =   m 1,  m 2, ...,  mL  , m    SL       L </li></ul></ul><ul><ul><li>Ab  =   Ab 1,  Ab 2, ...,  AbL  , Ag  =   Ag 1,  Ag 2, ...,  AgL  </li></ul></ul><ul><li>Some Types of Shape Space </li></ul><ul><ul><li>Hamming </li></ul></ul><ul><ul><li>Euclidean </li></ul></ul><ul><ul><li>Manhattan </li></ul></ul><ul><ul><li>Symbolic </li></ul></ul>Immunocomputing
  51. 51. Immunocomputing <ul><li>Affinities: related to distance/similarity </li></ul><ul><li>Examples of affinity measures </li></ul><ul><ul><li>Euclidean </li></ul></ul><ul><ul><li>Manhattan </li></ul></ul><ul><ul><li>Hamming </li></ul></ul>
  52. 52. Immunocomputing <ul><li>Algorithms and Processes </li></ul><ul><ul><li>Generic algorithms based on specific immune principles, processes or theoretical models </li></ul></ul><ul><li>Main Types </li></ul><ul><ul><li>Bone marrow algorithms </li></ul></ul><ul><ul><li>Thymus algorithms </li></ul></ul><ul><ul><li>Clonal selection algorithms </li></ul></ul><ul><ul><li>Immune network models </li></ul></ul>
  53. 53. <ul><li>Exemple of application: </li></ul>Immunocomputing
  54. 54. <ul><li>Another example of application: </li></ul>Immunocomputing
  55. 55. <ul><li>Why immunocomputing? </li></ul><ul><ul><li>Adaptability </li></ul></ul><ul><ul><li>Robustness </li></ul></ul><ul><ul><li>Distributivity </li></ul></ul><ul><ul><li>Decentralization </li></ul></ul><ul><ul><li>Fault detection and tolerance </li></ul></ul><ul><ul><li>Self/Nonself discrimination* </li></ul></ul><ul><ul><li>... </li></ul></ul>Immunocomputing
  56. 56. <ul><li>Scope: </li></ul><ul><ul><li>Pattern recognition </li></ul></ul><ul><ul><li>Fault and anomaly detection, and the security of information systems </li></ul></ul><ul><ul><li>Data analysis (knowledge discovery in databases, clustering, etc.) </li></ul></ul><ul><ul><li>Agent-based systems </li></ul></ul><ul><ul><li>Scheduling </li></ul></ul><ul><ul><li>Machine-learning </li></ul></ul><ul><ul><li>Autonomous navigation and control </li></ul></ul><ul><ul><li>Search and optimization problems </li></ul></ul><ul><ul><li>Artificial life </li></ul></ul>Immunocomputing
  57. 57. Part III-B Artificial Life and Fractal Geometry
  58. 58. Main Ideas <ul><li>Biosciences: reductionist approach to understanding life </li></ul><ul><li>Artificial Life & Fractal Geometry: bottom-up approach to synthesize life patterns and behaviors </li></ul><ul><li>Focus on the computational synthesis of natural patterns and behaviors, not problem solving </li></ul><ul><li>Widely used in computer graphics and movie making </li></ul>
  59. 59. Artificial Life <ul><li>What is life? </li></ul><ul><ul><li>“ The property or quality that distinguishes living organisms from dead organisms and inanimate matter, manifested in functions such as metabolism, growth, reproduction, and response to stimuli or adaptation to the environment originating from within the organism. ” (Dictionary.com) </li></ul></ul><ul><ul><li>Are mules alive? </li></ul></ul>
  60. 60. <ul><li>Some poetical definitions of life </li></ul><ul><ul><li>“ Life is a long process of getting tired” (Samuel Butler) </li></ul></ul><ul><ul><li>“ Life is a tale told by an idiot - full of sound and fury, signifying nothing ” (Shakespeare) </li></ul></ul>Artificial Life
  61. 61. <ul><li>Artificial Life: </li></ul><ul><ul><li>“ Artificial Life is the study of man-made systems that exhibit behaviors characteristic of natural living systems. It complements the traditional biological sciences concerned with the analysis of living organisms by attempting to synthesize life-like behaviors within computers and other artificial media. By extending the empirical foundation upon which biology is based beyond the carbon-chain life that has evolved on Earth, Artificial Life can contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be . ” (Chris Langton) </li></ul></ul>Artificial Life
  62. 62. <ul><li>“ Artificial Life (AL) is the enterprise of understanding biology by constructing biological phenomena out of artificial components, rather than breaking natural life forms down into their component parts. It is the synthetic rather than the reductionist approach.” (Ray, 1994) </li></ul>Artificial Life
  63. 63. <ul><li>“ Alife is a constructive endeavor: Some researchers aim at evolving patterns in a computer; some seek to elicit social behaviors in real-world robots; others wish to study life-related phenomena in a more controllable setting, while still others are interested in the synthesis of novel lifelike systems in chemical, electronic, mechanical, and other artificial media. Alife is an experimental discipline, fundamentally consisting of the observation of run-time behaviors, those complex interactions generated when populations of man-made, artificial creatures are immersed in real or simulated environments.” (Ronald et al., 1999) </li></ul>Artificial Life
  64. 64. Artificial Life <ul><li>Natural Life: An instance </li></ul>
  65. 65. <ul><li>Boids: Simple Behavioral Rules </li></ul><ul><ul><li>Collision avoidance and separation </li></ul></ul><ul><ul><li>Velocity match and alignment </li></ul></ul><ul><ul><li>Flock centering or cohesion </li></ul></ul>Artificial Life
  66. 66. <ul><li>Boids </li></ul>Artificial Life
  67. 67. <ul><li>AIBO ERS 210 </li></ul>Artificial Life
  68. 68. Artificial Life
  69. 69. <ul><li>Wasp Nest Building </li></ul>Artificial Life
  70. 70. <ul><li>Creatures: Adaptive learning through interaction </li></ul>Artificial Life
  71. 71. <ul><li>Artificial fishes: Predator behavior </li></ul>Artificial Life
  72. 72. <ul><li>Traffic simulation: What is needed for a jam? </li></ul>Artificial Life
  73. 73. <ul><li>Life-as-it-is x life-as-it-could-be </li></ul>Artificial Life
  74. 74. <ul><li>Why Artificial Life? </li></ul><ul><ul><li>Increases our understanding of life </li></ul></ul><ul><ul><li>Provides new perspectives about ‘life’ and its many models </li></ul></ul><ul><ul><li>Development of new technologies: softwares, robotics, interactive games, computer graphics, educational systems, behavior animation tools </li></ul></ul><ul><ul><li>... </li></ul></ul>Artificial Life
  75. 75. Fractal Geometry <ul><li>“ Why is geometry often described as ‘cold’ and ‘dry’? One reason lies in its inability to describe the shape of a cloud, a mountain, a coastline, or a tree. Clouds are not spheres, mountains are not cones, coastlines are not circles, and bark is not smooth, nor does lightning travel in a straight line. … The existence of these patterns challenges us to study those forms that Euclid leaves aside as being ‘formless’, to investigate the morphology of the ‘amorphous’.” (Mandelbrot, 1983; p. 1) </li></ul><ul><li>A major breakthrough in the process of modeling and synthesizing natural patterns and structures was the recognition that nature is fractal and the development of fractal geometry </li></ul><ul><li>Fractal geometry is the geometry of nature with all its irregular, fragmented and complex structures </li></ul>
  76. 76. Fractal Geometry <ul><li>Some Tools: </li></ul><ul><ul><li>Cellular automata </li></ul></ul><ul><ul><li>Iterated function systems </li></ul></ul><ul><ul><li>Lindenmayer systems </li></ul></ul><ul><ul><li>Brownian motion </li></ul></ul><ul><ul><li>Particle systems </li></ul></ul><ul><ul><li>Evolutionary design </li></ul></ul><ul><ul><li>etc. </li></ul></ul>
  77. 77. <ul><li>Cellular automata </li></ul><ul><ul><li>Dynamical system that is discrete in both space and time </li></ul></ul><ul><ul><li>Prototypical models for complex systems and processes consisting of a large number of identical, simple, locally interacting components </li></ul></ul><ul><ul><li>Formal description: C  = ( S , s 0, G , d , f ), </li></ul></ul><ul><ul><ul><li>S is a finite set of states, </li></ul></ul></ul><ul><ul><ul><li>s 0     S are the initial states of the CA, </li></ul></ul></ul><ul><ul><ul><li>G is the cellular neighborhood, </li></ul></ul></ul><ul><ul><ul><li>d      Z + is the dimension of C , and </li></ul></ul></ul><ul><ul><ul><li>f is the local cellular interaction rule, also referred to as the transition function or transition rule . </li></ul></ul></ul>Fractal Geometry
  78. 78. <ul><li>Cellular automata </li></ul>Fractal Geometry
  79. 79. <ul><li>Lindenmayer Systems </li></ul><ul><ul><li>A formalism to simulate the development of multicellular organisms </li></ul></ul><ul><ul><li>A string or word OL-system is defined as the ordered triplet G  =   V ,  , P  , where V is the alphabet of the system,       V + is a nonempty word called the axiom , and P      V      V* is a finite set of productions </li></ul></ul><ul><ul><li>The geometric interpretation of the words generated by an L-system can be used to generate schematic images of diverse natural patterns </li></ul></ul>Fractal Geometry
  80. 80. <ul><li>Lindenmayer Systems (without rendering) </li></ul>Fractal Geometry
  81. 81. <ul><li>Lindenmayer Systems (with rendering) </li></ul>Fractal Geometry
  82. 82. Fractal Geometry <ul><li>A Natural Fern </li></ul>
  83. 83. <ul><li>(Random) Iterated Function Systems </li></ul><ul><ul><li>An iterated function system (IFS) consists of a complete metric space ( X , d ) together with a finite set of contraction mappings w n  :  X      X , with respective contractivity factors s n , n  = 1,2,… N . </li></ul></ul><ul><ul><li>Let { X ; w 1 , w 2 ,…, w N } be an IFS, where a probability p i  > 0 has been assigned to each w i , i  = 1,…, N ,  i   p i  = 1 </li></ul></ul><ul><ul><li>Choose a point x      X and then choose recursively and independently a new point x obtained by applying only one of the transformations, chosen according to a given probability, to the current point x </li></ul></ul>Fractal Geometry
  84. 84. <ul><li>A Fern Generated with a RIFS </li></ul>Fractal Geometry
  85. 85. <ul><li>Brownian Motion </li></ul><ul><ul><li>To model some natural sceneries, it is necessary to have curves that look different when magnified but still possess the same characteristic impression </li></ul></ul><ul><ul><li>The term fractional Brownian motion (fBm) was introduced to refer to a family of Gaussian random functions capable of providing useful models of various natural time series </li></ul></ul>Fractal Geometry
  86. 86. <ul><li>Fractional Brownian Motion (without rendering) </li></ul>Fractal Geometry
  87. 87. <ul><li>Fractional Brownian Motion (with rendering) </li></ul>Fractal Geometry
  88. 88. <ul><li>Particle Systems </li></ul><ul><ul><li>Modeling physical phenomena like the flowing, dripping and pouring of liquids, the liquid mixing with other substances, gases in motion, explosions, clouds, fireworks, etc. </li></ul></ul><ul><ul><li>A particle system consists of a collection of particles (objects) with various properties and some behavioral rules they must obey </li></ul></ul><ul><ul><li>The precise definition of these properties and laws depends on what is intended to be modeled </li></ul></ul>Fractal Geometry
  89. 89. Fractal Geometry <ul><li>Particle Systems </li></ul><ul><li>See http://www.cs.wpi.edu/~matt/courses/cs563/talks/psys.html </li></ul>
  90. 90. <ul><li>Why fractal geometry? </li></ul><ul><ul><li>A computationally cheap way of generating computer models of nature </li></ul></ul><ul><ul><li>Study natural patterns: extinct vegetation, design new variety of plants, study growth and developmental processes, aid farmers and decorators, crop prediction, computer graphics and movie making, etc. </li></ul></ul>Fractal Geometry
  91. 91. Part III-C Computing with New Natural Material
  92. 92. <ul><li>If current computing technology will reach its limit in the near future, what would be the alternative material with which to compute? </li></ul><ul><li>New computing methods based on other natural material than silicon: </li></ul><ul><ul><li>Molecules </li></ul></ul><ul><ul><li>Membranes </li></ul></ul><ul><ul><li>Quantum elements </li></ul></ul>Computing with Natural Material
  93. 93. Computing with Natural Material <ul><li>DNA Computing </li></ul>
  94. 94. <ul><li>Quantum Computing </li></ul><ul><ul><li>Quantum bit: | x   =  c 1|0   +  c 2|1  </li></ul></ul>Computing with Natural Material
  95. 95. Part IV Computing in the New Millennium
  96. 96. <ul><li>Some ideas that form the basis of natural computing: </li></ul><ul><ul><li>Capacity of dealing with complex problems </li></ul></ul><ul><ul><li>The use of sets of candidate solutions </li></ul></ul><ul><ul><li>Capacity of dealing imprecisely with imprecise information </li></ul></ul><ul><ul><li>Robustness </li></ul></ul><ul><ul><li>Distributivity </li></ul></ul><ul><ul><li>Self-repair </li></ul></ul><ul><ul><li>etc. </li></ul></ul>Computing in the New Millennium
  97. 97. Computing in the New Millennium <ul><li>From singularity to plurality </li></ul>
  98. 98. <ul><li>The importance of nature has never been so great! </li></ul>Computing in the New Millennium
  99. 99. Main Reference <ul><li>Fundamentals of Natural Computing, Concepts, Algorithms, and Applications; by Leandro de Castro, CRC Press, 2006 </li></ul>
  100. 100. How far can we go? Questions, comments?

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