CELLULAR AUTOMATA Derek Karssenberg, Utrecht University, the Netherlands LIFE (Conway)
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Transcript of CELLULAR AUTOMATA Derek Karssenberg, Utrecht University, the Netherlands LIFE (Conway)
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CELLULAR AUTOMATADerek Karssenberg, Utrecht University, the Netherlands
LIFE (Conway)
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introductionPrinciple of cellular automata
Local (spatial) interactions create global pattern of changeExample: vegetation patterns
local global
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introductionPrinciple of cellular automataLocal (spatial) interactions create global pattern of changeExample: forest fire local
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introductionPrinciple of cellular automataLocal (spatial) interactions create global pattern of changeExample: forest fire global
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introductionSpatial discretization: regular lattice of cells
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introductionFormulating cellular automata
General case for dynamic spatial models:
with:zk 1..k state variables
ij 1..j inputs
fk 1..k functionals
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introductionFormulating cellular automata
General case for dynamic spatial models:
which can be rewritten as:
z vector of state variables (was zk),
the ‘state of the cell’i vector of inputs (was ij)
f number of functionals (was fk)
( ) ttnjtimktztz jkkk each for ;..1),( ;..1),(f)1( ===+
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introductionFormulating cellular automata
Cellular automata:
• no inputs (although in practice inputs may be used)
• all variables in z are classified (although scalars are sometimes used)
• f is a function (‘transition rule’) of a direct neighbourhood and the cell itself only
• the change from t to t + 1 is not a function of time
What is f?
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introductionNotation rules: referring to neighbouring cells
the state of the cell on row i, column j
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transition rulesTransition rule: von Neumann Neighborhood
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transition rulesTransition rule: Moore neighborhood
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transition rulesTransition rule: extended von Neumann neighborhood
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introductionLife, also known as Game of Life
Developed by John Conway, 1970
One boolean variable: TRUE (= live cell) or FALSE (= dead cell)
Uses the Moore neighborhood
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introductionLife, also known as Game of Life
Transition rules (i.e. f)
• a dead cell with exactly three alive neighbors becomes a live cell (birth)
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introductionLife, also known as Game of Life
Transition rules (i.e. f)
• a dead cell with exactly three alive neighbors becomes a live cell (birth)• a live cell with two or three neighbors stays alive (survival)
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introductionLife, also known as Game of Life
Transition rules (i.e. f)
• a dead cell with exactly three alive neighbors becomes a live cell (birth)• a live cell with two or three neighbors stays alive (survival)• in all other cases, a cell dies or remains dead
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introductionLIFE in PCRaster
initial Alive=uniform(1) gt 0.5;
dynamic
NumberOfAliveNeighbors=windowtotal(scalar(Alive),3)-
scalar(Alive); Alive=(NumberOfAliveNeighbors eq 3) or
((NumberOfAliveNeighbors eq 2) and Alive);
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stochastic variables in PCRasterDemo
game of life
edit tot.moddisplay ini.mapaguila –2 alive000.001+1000 test0000.001+1000
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stochastic variables in PCRasterSelf-organization
Internal organization of a system increases in complexity without being guided by an input variable
Emergent properties
Complex pattern formation from more basic constituent parts or behaviors
Example: game of life
Many examples in ecology, sedimentology