Transcript Natural Computing - Computer Science

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/
[email protected]
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
Lila Kari, University of Western Ontario
Natural Computing
• (i) Nature as Inspiration
• (ii) Nature as Implementation Substrate
• (iii) Nature as Computation
Lila Kari, University of Western Ontario
(i) Nature as Inspiration
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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
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
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
Lila Kari, University of Western Ontario
Conus Textile pattern
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
Lila Kari, University of Western Ontario
Applications to Human Cognition
[T.Schultz, www.psych.mcgill.ca/labs/lnsc]
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
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
Lila Kari, University of Western Ontario
Using Genetic Algorithms to
Create Evolutionary Art [M.Gold]
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
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
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)
Lila Kari, University of Western Ontario
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
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
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
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
Lila Kari, University of Western Ontario
L-Systems Applications
• Plant growth [Fuhrer, Wann Jensen, Prusinkiewicz 2004-05]
• Architecture and design [J.Bailey, Archimorph]
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)
Lila Kari, University of Western Ontario
• How to insert pdf file
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
Lila Kari, University of Western Ontario
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.
Lila Kari, University of Western Ontario
Amorphous Computing
[Generating patterns: Abelson, Sussman, Knight, Ragpal]
Lila Kari, University of Western Ontario
(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
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)
Lila Kari, University of Western Ontario
Molecular (DNA) Computing
• Single-stranded DNA is a string over the
four-letter alphabet, {A, C, G, T}
Lila Kari, University of Western Ontario
Power of DNA Computing
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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).
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
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
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)
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
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
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
Lila Kari, University of Western Ontario
Computation by DNA Self-Assembly
[Mao, LaBean, Reif, , Seeman, Nature, 2000]
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, to appear, 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, submitted)
• L.Kari, B.Masson, Simulating arbitrary
neighbourhoods by polyominoes (2008, in preparation)
Lila Kari, University of Western Ontario
DNA Clonable Octahedron
[Shih, Joyce, Nature, 2004]
Lila Kari, University of Western Ontario
Nanoscale DNA Tetrahedra
[Goodman, Turberfield, Science, 2005]
Lila Kari, University of Western Ontario
DNA Origami
[Rothemund, Nature, 2006]
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)
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.
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
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
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
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
Lila Kari, University of Western Ontario
(iii-3) Cellular Computing
Computation in living cells: ciliated protozoa
Lila Kari, University of Western Ontario
Ciliates: Gene Rearrangement
Photo courtesy of L.F. Landweber
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)
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
Lila Kari, University of Western Ontario
Biocomputing at Western
* UWO Biocomputing Lab
http://www.csd.uwo.ca/~lila/biocomplab.html
• DNA COMPUTING, CS 9562B/4462B
http://www.csd.uwo.ca/~lila/cs662.html
• UWO Biocomputing Student Award
http://www.csd.uwo.ca/~lila/award.html
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]
Lila Kari, University of Western Ontario