2005: Natural Computing - Concepts and Applications
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2005: Natural Computing - Concepts and Applications

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BIC 2005 (Biologically Inspired Computing Conference), Johor, Malaysia

BIC 2005 (Biologically Inspired Computing Conference), Johor, Malaysia

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    2005: Natural Computing - Concepts and Applications 2005: Natural Computing - Concepts and Applications Presentation Transcript

    • 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
      • 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.
      • Now stop imagining.
      • Welcome to Computing in the New Millennium.
      • Welcome to the Natural Computing age!
      Foreword Adapted from Digital Biology, by P. Bentley.
    • Outline
      • Part I : Introduction and Motivation
        • Some ideas and challenges
      • Part II: Looking at Nature with Different Eyes
        • Nature’s solutions: Some samples
      • Part III : Natural Computing
        • Computing inspired by nature
        • The simulation and emulation of natural phenomena in computers
        • Computing with natural materials
      • Part III: Computing in the New Millennium
    • Part I Introduction and Motivation
    • Current Computer Technology
      • Turing Machines (TM)
        • Computational device idealized by A. Turing in 1936
        • If a problem can be computed, then it can be computed by a Turing Machine
      • J. von Neumman architecture
    • Features of Current Computers
      • General-purpose machines
      • Manipulate precisely precise information*
      • Address-based memory
      • Serial processing*
      • Are not capable of generalizing
      • Are not fault tolerant (robust)
      • Are not adaptable*
    • Are You Ready?
      • Develop a computer program to distribute products of a company throughout the country.
      • Generate a computer model to simulate the evacuation program of a building undergoing fire.
      • What are the new technologies to complement or supplement silicon-based hardware?
    • Why Are These Questions Hard? (1. Products Distribution)
      • How many are the possible routes?
    • Why Are These Questions Hard? (2. Behavioral Simulation)
    • Why Are These Questions Hard? (3. New Technologies)
      • Moore’s Law:
        • The processing power of silicon-based computers doubles approximately every couple of years
        • By the end of the next decade (2020) we may have reached the (miniaturization) limit of current computer technology
      N. of atoms per bit Year 2020: 1 atom per bit
    • What all these questions have in common?
      • The answer to all of them require a paradigm shift
      Where can we find answers to them?
      • Where all these problems and difficulties have been solved and dealt with from ages: In NATURE!!
    • 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
      • Nature x Computing
      • Natural computing is the terminology used to encompass three paradigms:
        • Computing inspired by nature
        • The simulation and emulation of natural phenomena in computers
        • Computing with natural materials
    • The Philosophy of Natural Computing
    • Part III-A Computing Inspired by Nature
    • Main Ideas
      • Nature has evolved through ages in order to solve complex real-world problems
      • Examples abound: nest building, nest cleaning, main senses (hearing, seeing, touching, smelling, tasting), etc.
      • Computer algorithms based or inspired by nature have been developed for some time:
        • Either to model nature,
        • Or to solve complex real-world problems
    • Main Themes
      • Neurocomputing
      • Evolutionary Computing
      • Swarm Intelligence
      • Immunocomputing
      • Artificial Chemistry
      • Growth and Developmental Algorithms
      • etc.
      Older approaches
    • Neurocomputing
      • Inspiration
      • Design principles:
        • Artificial neuron: basic information processing and storage unit
        • Network architecture: how the artificial neurons are interconnected
        • Learning algorithm: guides the dynamics (adaptability) of the system
      Neurocomputing
    • Neurocomputing
      • Basic artificial neuron
      • Some activation functions
      • Network architectures
        • Single-layer feedforward network
      Neurocomputing
      • Network architectures
        • Multi-layer feedforward network
      Neurocomputing
      • Network architectures
        • Recurrent network
      Neurocomputing
      • Learning algorithms/rules:
        • Hebb learning
        • Single-layer perceptron
        • Adaline
        • ART
        • Multi-Layer perceptron
        • Self-organizing networks
        • Hopfield networks
        • Grossberg networks
      Neurocomputing
      • Why neurocomputing?
        • Learning capability
        • Parallel processing
        • Generalization capability
        • Inherently distributed
        • Robust
        • ...
      Neurocomputing
      • Scope:
        • Function approximation
        • Clustering
        • Classification
        • Pattern recognition
        • Control
      • Mature field with innumerable academic, industrial, commercial and governmental applications
      Neurocomputing
    • Evolutionary Computing
      • Inspiration
      + Reproduction + Genetic Variation + Selection
      • The power of (artificial) evolution
      Evolutionary Computing
    • Evolutionary Computing
      • The power of evolution
      • Design principles:
        • Population of individuals*
        • Reproduction with genetic inheritance
        • Genetic variation
        • Selection
      Evolutionary Computing
      • Standard evolutionary algorithm
      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
      • Main types of evolutionary algorithms:
        • Evolutionary programming
        • Evolution strategies
        • Genetic algorithms
        • Genetic programming*
        • Classifier systems*
      Evolutionary Computing
      • Why evolutionary computing?
        • A population may explore and exploit more efficiently than a single individual
        • Importance of information (experience) exchange
        • Maintenance of good quality solutions
        • Diversity and creativity
      Evolutionary Computing
      • Scope:
        • Search and optimization
        • Planning (e.g. routing, scheduling and packing )
        • Design (e.g. signal processing)
        • Simulation, identification, control (e.g. general plant control)
        • Classification (e.g. machine learning, pattern recognition and classification)
      Evolutionary Computing
      • Systems based on the collective behavior of social organisms
      • Two main approaches:
        • Systems based on the collective behavior of social insects
          • Ant Colony Optimization (ACO)
          • Ant Clustering Algorithm (ACA)
        • Systems based on sociocognition
          • Particle Swarm Optimization (PSO)
      Swarm Intelligence
      • An inspiration
      Swarm Intelligence
    • Swarm Intelligence
      • An ant farm
      • An ant farm
      Swarm Intelligence
      • Another inspiration
      Swarm Intelligence
      • Robotic autonomous navigation
      Swarm Intelligence
      • Why swarm intelligence?
        • Again, a multi-agent approach may allow for a better exploration and exploitation of the space
        • Simple agents together can perform complicated tasks
        • It may be easier and cheaper to have many simple agents than a single complex one
      Swarm Intelligence
      • Scope:
        • Search and optimization:
          • Discrete and continuous optimization
        • Data analysis (clustering)
        • Robotics (autonomous navigation)
      Swarm Intelligence
    • Immunocomputing
      • Inspiration
      • Design principles:
        • Representation
        • Architecture
        • Affinity/Fitness functions
        • Dynamics/Metadynamics
      Immunocomputing
      • Representation
        • Set of coordinates: m  =   m 1,  m 2, ...,  mL  , m    SL       L
        • Ab  =   Ab 1,  Ab 2, ...,  AbL  , Ag  =   Ag 1,  Ag 2, ...,  AgL 
      • Some Types of Shape Space
        • Hamming
        • Euclidean
        • Manhattan
        • Symbolic
      Immunocomputing
    • Immunocomputing
      • Affinities: related to distance/similarity
      • Examples of affinity measures
        • Euclidean
        • Manhattan
        • Hamming
    • Immunocomputing
      • Algorithms and Processes
        • Generic algorithms based on specific immune principles, processes or theoretical models
      • Main Types
        • Bone marrow algorithms
        • Thymus algorithms
        • Clonal selection algorithms
        • Immune network models
      • Exemple of application:
      Immunocomputing
      • Another example of application:
      Immunocomputing
      • Why immunocomputing?
        • Adaptability
        • Robustness
        • Distributivity
        • Decentralization
        • Fault detection and tolerance
        • Self/Nonself discrimination*
        • ...
      Immunocomputing
      • Scope:
        • Pattern recognition
        • Fault and anomaly detection, and the security of information systems
        • Data analysis (knowledge discovery in databases, clustering, etc.)
        • Agent-based systems
        • Scheduling
        • Machine-learning
        • Autonomous navigation and control
        • Search and optimization problems
        • Artificial life
      Immunocomputing
    • Part III-B Artificial Life and Fractal Geometry
    • Main Ideas
      • Biosciences: reductionist approach to understanding life
      • Artificial Life & Fractal Geometry: bottom-up approach to synthesize life patterns and behaviors
      • Focus on the computational synthesis of natural patterns and behaviors, not problem solving
      • Widely used in computer graphics and movie making
    • Artificial Life
      • What is life?
        • “ 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)
        • Are mules alive?
      • Some poetical definitions of life
        • “ Life is a long process of getting tired” (Samuel Butler)
        • “ Life is a tale told by an idiot - full of sound and fury, signifying nothing ” (Shakespeare)
      Artificial Life
      • Artificial Life:
        • “ 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)
      Artificial Life
      • “ 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)
      Artificial Life
      • “ 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)
      Artificial Life
    • Artificial Life
      • Natural Life: An instance
      • Boids: Simple Behavioral Rules
        • Collision avoidance and separation
        • Velocity match and alignment
        • Flock centering or cohesion
      Artificial Life
      • Boids
      Artificial Life
      • AIBO ERS 210
      Artificial Life
    • Artificial Life
      • Wasp Nest Building
      Artificial Life
      • Creatures: Adaptive learning through interaction
      Artificial Life
      • Artificial fishes: Predator behavior
      Artificial Life
      • Traffic simulation: What is needed for a jam?
      Artificial Life
      • Life-as-it-is x life-as-it-could-be
      Artificial Life
      • Why Artificial Life?
        • Increases our understanding of life
        • Provides new perspectives about ‘life’ and its many models
        • Development of new technologies: softwares, robotics, interactive games, computer graphics, educational systems, behavior animation tools
        • ...
      Artificial Life
    • Fractal Geometry
      • “ 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)
      • 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
      • Fractal geometry is the geometry of nature with all its irregular, fragmented and complex structures
    • Fractal Geometry
      • Some Tools:
        • Cellular automata
        • Iterated function systems
        • Lindenmayer systems
        • Brownian motion
        • Particle systems
        • Evolutionary design
        • etc.
      • Cellular automata
        • Dynamical system that is discrete in both space and time
        • Prototypical models for complex systems and processes consisting of a large number of identical, simple, locally interacting components
        • Formal description: C  = ( S , s 0, G , d , f ),
          • S is a finite set of states,
          • s 0     S are the initial states of the CA,
          • G is the cellular neighborhood,
          • d      Z + is the dimension of C , and
          • f is the local cellular interaction rule, also referred to as the transition function or transition rule .
      Fractal Geometry
      • Cellular automata
      Fractal Geometry
      • Lindenmayer Systems
        • A formalism to simulate the development of multicellular organisms
        • 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
        • The geometric interpretation of the words generated by an L-system can be used to generate schematic images of diverse natural patterns
      Fractal Geometry
      • Lindenmayer Systems (without rendering)
      Fractal Geometry
      • Lindenmayer Systems (with rendering)
      Fractal Geometry
    • Fractal Geometry
      • A Natural Fern
      • (Random) Iterated Function Systems
        • 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 .
        • 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
        • 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
      Fractal Geometry
      • A Fern Generated with a RIFS
      Fractal Geometry
      • Brownian Motion
        • To model some natural sceneries, it is necessary to have curves that look different when magnified but still possess the same characteristic impression
        • 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
      Fractal Geometry
      • Fractional Brownian Motion (without rendering)
      Fractal Geometry
      • Fractional Brownian Motion (with rendering)
      Fractal Geometry
      • Particle Systems
        • Modeling physical phenomena like the flowing, dripping and pouring of liquids, the liquid mixing with other substances, gases in motion, explosions, clouds, fireworks, etc.
        • A particle system consists of a collection of particles (objects) with various properties and some behavioral rules they must obey
        • The precise definition of these properties and laws depends on what is intended to be modeled
      Fractal Geometry
    • Fractal Geometry
      • Particle Systems
      • See http://www.cs.wpi.edu/~matt/courses/cs563/talks/psys.html
      • Why fractal geometry?
        • A computationally cheap way of generating computer models of nature
        • 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.
      Fractal Geometry
    • Part III-C Computing with New Natural Material
      • If current computing technology will reach its limit in the near future, what would be the alternative material with which to compute?
      • New computing methods based on other natural material than silicon:
        • Molecules
        • Membranes
        • Quantum elements
      Computing with Natural Material
    • Computing with Natural Material
      • DNA Computing
      • Quantum Computing
        • Quantum bit: | x   =  c 1|0   +  c 2|1 
      Computing with Natural Material
    • Part IV Computing in the New Millennium
      • Some ideas that form the basis of natural computing:
        • Capacity of dealing with complex problems
        • The use of sets of candidate solutions
        • Capacity of dealing imprecisely with imprecise information
        • Robustness
        • Distributivity
        • Self-repair
        • etc.
      Computing in the New Millennium
    • Computing in the New Millennium
      • From singularity to plurality
      • The importance of nature has never been so great!
      Computing in the New Millennium
    • Main Reference
      • Fundamentals of Natural Computing, Concepts, Algorithms, and Applications; by Leandro de Castro, CRC Press, 2006
    • How far can we go? Questions, comments?