The Many Facets of Natural Computinglkari/natural_2015.pdf · 2015. 1. 20. · Lila Kari,...
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The Many Facets of Natural Computing
Lila Kari Dept. of Computer Science
University of Western Ontario London, ON, Canada http://www.csd.uwo.ca/~lila/
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Lila Kari, University of Western Ontario
Natural Computing • Investigates models and computational
techniques inspired by nature • Attempts to understand the world around us
in terms of information processing • Interdisciplinary field that connects
computer sciences with natural sciences
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Lila Kari, University of Western Ontario
Natural Computing
• (i) Nature as Inspiration • (ii) Nature as Implementation Substrate • (iii) Nature as Computation
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(i) Nature as Inspiration
• Cellular Automata – self-reproduction • Neural Computation – the brain • Evolutionary Computation – evolution • Swarm Intelligence – group behaviour • Immunocomputing – immune system • Artificial Life – properties of life • Membrane Computing – cells and membranes • Amorphous Computing - morphogenesis
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Lila Kari, University of Western Ontario
1.Cellular Automata
• Cellular automaton = dynamical system consisting of a regular grid of cells
• Space and time and discrete • Each cell can be in a finite number of states • Each cell changes its state according to a list of
transition rules, based on its current state and the states of its neighbours
• The grid updates its configuration synchronously
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Lila Kari, University of Western Ontario
CA Example: Rule 30
111 110 101 100 011 010 001 000 0 0 0 1 1 1 1 0
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CA Classification • Class 1: Initial patterns evolve into a stable state;
Any randomness disappears. • Class 2: Initial patterns evolve into stable or
oscillating states; Some randomness remains. • Class 3: Initial patterns evolve into a pseudo-
random or chaotic manner; Stable structures are destroyed.
• Class 4: Initial patterns evolve into structures that interact in complex ways, with local patterns surviving for a long time; Wolfram conjectured that many Class 4 CA (Rule 110, Game of Life) are capable of universal computation
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Rule 110
Lila Kari, University of Western Ontario
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Conway’s Game of Life • Neighbourhood – 8 neighbours • Any live cell with less than 2 live neighbours dies
(under-population) • Any live cell with 2 or 3 live neighbours lives • Any live cell with more than 3 live neighbours
dies (overcrowding) • Any dead cell with exactly 3 live neighbours
becomes live cell (reproduction) • Patterns: Still lives, Oscillators, Space ships
Lila Kari, University of Western Ontario
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Lila Kari, University of Western Ontario
Conus Textile pattern
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Lila Kari, University of Western Ontario
2.Neural Computation • Artificial Neural Network: a network of
interconnected artificial neurons • Neuron A : * n real- valued inputs x1,…, xn * weights w1,…,wn
* computes fA(w1x1 + w2x2 + …+ wnxn) • Network Function = vectorial function that, for n input values, associates the outputs of the m
pre-selected output neurons
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Applications to Human Cognition [T.Schultz, www.psych.mcgill.ca/labs/lnsc]
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Lila Kari, University of Western Ontario
3.Evolutionary Computation
• Constant or variable-sized population • A fitness criterion according to which
individuals are evaluated • Genetically inspired operators (mutation or
recombination of parents) that produce the next generation from the current one
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Lila Kari, University of Western Ontario
Genetic Algorithms
• Individuals = fixed-length bit strings • Mutation = cut-and-paste of a prefix of a parent
with a suffix of another • Fitness function is problem-dependent • If initial population encodes possible solutions to a
given problem, then the system evolves to produce a near-optimal solution to the problem
• Applications: real-valued parameter optimization
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Cross-over
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Example: Max of f(x) = x^2 x = 0,…, 31
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Cross-over and 1st generation offspring
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Using Genetic Algorithms to Create Evolutionary Art [M.Gold]
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Lila Kari, University of Western Ontario
4.Swarm Intelligence
• Swarm: group of mobile biological organisms (bacteria, ants, bees, fish, birds)
• Each individual communicates with others either directly or indirectly by acting on its environment
• These interactions contribute to collective problem solving = collective intelligence
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Lila Kari, University of Western Ontario
Particle Swarm Optimization • Inspired by flocking behaviour of birds • Start with a swarm of particles (each
representing a potential solution) • Particles move through a multidimensional
space and positions are updated based on * previous own velocity * tendency towards personal best * tendency toward neighbourhood best
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Lila Kari, University of Western Ontario
Ant Algorithms
• Model the foraging behaviour of ants • In finding the best path between nest and a
source of food, ants rely on indirect communication by laying a pheromone trail on the way back (if food is found) and by following concentration of pheromones (if food is sought)
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Lila Kari, University of Western Ontario
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Lila Kari, University of Western Ontario
5.Immunocomputing
• Immune system’s function = protect our bodies against external pathogens
• Role of immune system: recognize cells and categorize them as self or non-self
• Innate (non-specific) immune system • Adaptive (acquired) immune system
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Lila Kari, University of Western Ontario
Artificial Immune Systems
• Computational aspects of the immune system: distinguishing self from non-self, feature extraction, learning, immunological memory, self-regulation, fault-tolerance
• Applications: computer virus detection, anomaly detection in a time-series of data, fault diagnosis, pattern recognition
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Lila Kari, University of Western Ontario
6.Artificial Life
• ALife attempts to understand the very essence of what it means to be alive
• Builds ab initio, within in silico computers, artificial systems that exhibit properties normally associated only with living organisms
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Lila Kari, University of Western Ontario
Lindenmayer Systems
• Parallel rewriting systems • Start with an initial word • Apply the rewriting rules in parallel to all
letters of the word • Used, e.g., for modelling of plant growth
and morphogenesis
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L systems
• G = (V, a, P) • V = the alphabet (set of symbols) • a = axiom (string of symbols from V) • P = set of production rules
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Example: Growth of Algae
• Variables : A, B • Axiom: A • Rules: A à AB, B à A • Length of each string: Fibonacci sequence
Lila Kari, University of Western Ontario
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Example: Pythagoras Tree
• Variables: 0, 1 • Constants: [, ] • Axiom: 0 • Rules: 1à 11, 0 à 1[0]0 • 2nd recursion 11[1[0]0]1[0]0 • 3rd recursion 1111[11[1[0]0]1[0]0]11[1[0]0]1[0]0 Lila Kari, University of Western Ontario
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Turtle Graphics
• 0 – draw a line segment (ending in a leaf) • 1 – draw a line segment • [ - push position and angle, turn left 45
degrees • ]- pop position and angle, turn right 45
degrees
Lila Kari, University of Western Ontario
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Pythagoras Tree 7th Recursion
Lila Kari, University of Western Ontario
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Fractal Weeds (3D)
Lila Kari, University of Western Ontario
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L-system Trees
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Lila Kari, University of Western Ontario
L-Systems Applications • Plant growth [Fuhrer, Wann Jensen, Prusinkiewicz 2004-05] • Architecture and design [J.Bailey, Archimorph]
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Lila Kari, University of Western Ontario
Mechanical Artificial Life
• Evolving populations of artificial creatures in simulated environments
• Combining the computational and experimental approaches and using rapid manufacturing technology to fabricate physical evolved robots that were selected for certain abilities (to walk or get a cube)
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Lila Kari, University of Western Ontario
• How to insert pdf file
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Lila Kari, University of Western Ontario
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Lila Kari, University of Western Ontario
7.Membrane Computing
• Inspired by the compartmentalized internal structure of cells
• Membrane System = a nested hierarchical structure of regions delimited by “membranes”
• Each region contains objects and transformation rules + transfer rules
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9-region “membrane computer”
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P-system which outputs square numbers
Lila Kari, University of Western Ontario
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8.Amorphous Computing • Inspired by developmental biology • Consist of a multitude of irregularly placed,
asynchronous, locally interacting computing elements
• The identically programmed “computational particles” communicate only with others situated within a small radius
• Goal: engineer specified coherent computational behaviour from the interaction of large quantities of such unreliable computational particles.
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Amorphous Computing [Generating patterns: Abelson, Sussman, Knight, Ragpal]
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(ii) Nature as Implementation Substrate
• Molecular Computing (DNA Computing) Uses biomolecules, e.g., DNA, RNA • Quantum Computing Uses, e.g., ion traps, superconductors, nuclear magnetic resonance
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Lila Kari, University of Western Ontario
(ii-1) Molecular Computing
• Data can be encoded as biomolecules (DNA, RNA)
• Arithmetic/logic operations are performed by molecular biology tools
• The proof-of-principle experiment was Adleman’s bio-algorithm solving a Hamiltonian Path Problem (1994)
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Lila Kari, University of Western Ontario
Molecular (DNA) Computing • Single-stranded DNA is a string over the
four-letter alphabet, {A, C, G, T}
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Power of DNA Computing
Data: DNA single and double strands • Watson–Crick Complementarity: W(C) = G, W(A) = T • Bio-operations: cut-and-paste by enzymes,
extraction by pattern, copy, read-out • R.Freund, L.Kari, G.Paun. DNA computing based on
splicing: the existence of universal computers. Theory of Computing Systems, 32 (1999).
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Lila Kari, University of Western Ontario
DNA-Encoded Information
• DNA strands interact with each other in programmed but also undesirable ways
• The information has no fixed location • The results of a biocomputation are not
deterministic, as they depend e.g. on concentration of populations of DNA strands, diffusion reactions, statistical laws
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Lila Kari, University of Western Ontario
DNA-Motivated Concepts
• θ-periodicity w = u1u2…un where ui is in {u, θ(u)} and θ is an antimorphic involution • Generalize Lyndon-Schutzenberger u^n v^m = w^m • θ-prefix, θ-infix, θ-compliant codes
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Lila Kari, University of Western Ontario
Our DNA Information Research • L. Kari, S. Seki, On pseudoknot-bordered words and their
properties, Journal of Computer and System Sciences, (2008)
• L.Kari, K.Mahalingam, Watson-Crick Conjugate and Commutative Words, Proc. DNA Computing 13, LNCS 4848 (2008)
• L. Kari, K. Mahalingam, S. Seki, Twin-roots of words and their properties, Theoretical Computer Science (2008)
• E.Czeizler, L.Kari, S.Seki. On a Special Class of Primitive Words. MFCS (2008)
• M. Ito, L. Kari, Z. Kincaid, S. Seki, Duplication in DNA sequences. Proc. of Developments in Language Theory (2008)
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Lila Kari, University of Western Ontario
Computing by Self-Assembly
• Self-Assembly = The process by which objects autonomously come together to form complex structures
• Examples § Atoms bind by chemical bonds to form molecules § Molecules may form crystals or
macromolecules § Cells interact to form organisms
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Lila Kari, University of Western Ontario
Motivation for Self-Assembly
Nanotechnology: miniaturization in medicine, electronics, engineering, material science, manufacturing
• Top-Down techniques: lithography (inefficient in creating structures with size of molecules or atoms)
• Bottom-Up techniques: self-assembly
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Lila Kari, University of Western Ontario
Computing by Self-Assembly of Tiles
• Tile = square with the edges labelled from a finite alphabet of glues
• Tiles cannot be rotated • Two adjacent tiles on the plane stick if they
have the same glue at the touching edges
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Lila Kari, University of Western Ontario
Computation by DNA Self-Assembly [Mao, LaBean, Reif, , Seeman, Nature, 2000]
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Lila Kari, University of Western Ontario
Our Self-Assembly Research • L.Adleman, J.Kari, L.Kari, D.Reishus, P.Sosik. The Undecidability of the Infinite Ribbon Problem:
Implications for Computing by Self-Assembly (SIAM Journal of Computing, 2009) • This solves an open problem formerly known as the
“unlimited infinite snake problem” • Undecidability of existence of arbitrarily large
supertiles that can self-assemble from a given tile set (starting from an arbitrary “seed”)
• E.Czeizler, L.Kari, Geometrical tile design for complex neighbourhoods (2008)
• L.Kari, B.Masson, Simulating arbitrary neighbourhoods by polyominoes (2008)
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Lila Kari, University of Western Ontario
DNA Clonable Octahedron [Shih, Joyce, Nature, 2004]
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Lila Kari, University of Western Ontario
Nanoscale DNA Tetrahedra [Goodman, Turberfield, Science, 2005]
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Lila Kari, University of Western Ontario
DNA Origami [Rothemund, Nature, 2006]
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Lila Kari, University of Western Ontario
(ii-2) Quantum Computing
• A qubit can hold a “0”, a “1” or a quantum superposition of these
• Quantum mechanical phenomena such as superposition and entanglement are used to perform operations on qubits
• Shor’s quantum algorithm for factoring integers (1994)
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Lila Kari, University of Western Ontario
Quantum Crytography • “Unbreakable encryption unveiled” (BBC News,
Oct 2008) • “Perfect secrecy has come a step closer with the
launch of the world's first computer network protected by unbreakable quantum encryption.”
• The network connects six locations across Vienna and in the nearby town of St Poelten, using 200 km of standard commercial fibre optic cables.
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Lila Kari, University of Western Ontario
(iii) Nature as Computation
Understand nature by viewing natural processes as information processing • Systems Biology • Synthetic Biology • Cellular Computing
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Lila Kari, University of Western Ontario
(iii-1) Systems Biology
• Attempt to understand complex interactions in biological systems by taking a systemic approach and focusing on the interaction networks themselves and on the properties that arise because of these interactions
* gene regulatory networks * protein-protein interaction networks * transport networks
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Lila Kari, University of Western Ontario
The Genomic Computer [Istrail, De Leon, Davidson, 2007]
• Molecular transport replaces wires • Causal coordination replaces imposed temporal
synchrony • Changeable architecture replaces rigid structure • Communication channels are formed on an as-needed basis • Very large scale • Robustness is achieved by rigorous selection
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Lila Kari, University of Western Ontario
(iii-2) Synthetic Biology
• TIMES best inventions 2008 : #21 The Synthetic Organism [C.Venter et al.]
• Generate a synthetic genome (5,386bp) of a virus by self-assembly of chemically synthesized short DNA strands
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Lila Kari, University of Western Ontario
(iii-3) Cellular Computing
Computation in living cells: ciliated protozoa
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Lila Kari, University of Western Ontario
Ciliates: Gene Rearrangement
Photo courtesy of L.F. Landweber
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Lila Kari, University of Western Ontario
Our Cellular Computing Research
§ L.Landweber, L.Kari. The evolution of cellular computing: nature's solution to a computational problem. Biosystems 52(1999)
§ L.Kari, L.F.Landweber. Computational power of gene rearrangement. Proc. DNA Computing 5, DIMACS Series, 54(2000)
§ L.Kari, J.Kari, L.Landweber. Reversible molecular computation in ciliates. In Jewels are Forever, Springer-Verlag (1999)
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Lila Kari, University of Western Ontario
Natural Computing
• Nature as inspiration: cellular automata, neural networks, evolutionary computation, swarm intelligence, immunocomputing, ALife, membrane computing, amorphous computing
• Nature as implementation substrate: molecular (DNA) computing*, quantum computing
• Nature as computation: systems biology, synthetic biology, cellular computing*
* Research interests of the UWO Biocomputing Lab
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Lila Kari, University of Western Ontario
Natural Sciences, Ours to Discover
• “Biology and computer science – life and computation – are related. I am confident that at their interface great
discoveries await those who seek them” [Leonard Adleman, Scientific American, August 1998]