Jarkko Kari - Serveur de Pages Professionnelles...
Transcript of Jarkko Kari - Serveur de Pages Professionnelles...
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Jarkko Kari
Department of Mathematics, University of Turku, Finland
TUCS (Turku Centre for Computer Science)
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Cellular Automata (CA): Introduction
Cellular automata are among the oldest models of natural
computing. They are versatile objects of study, investigated
• in physics as discrete models of physical systems,
• in computer science as models of massively parallel
computation under the realistic constraints of locality and
uniformity,
• in mathematics as endomorphisms of the full shift in the
context of symbolic dynamics.
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Cellular automata possess several fundamental properties of
the physical world: they are
• massively parallel,
• homogeneous in time and space,
• all interactions are local,
• time reversibility and conservation laws can be obtained
by choosing the local update rule properly.
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Example: the Game-of-Life by John Conway.
• Infinite checker-board whose squares (=cells) are colored
black (=alive) or white (=dead).
• At each discrete time step each cell counts the number of
living cells surrounding it, and based on this number
determines its new state.
• All cells change their state simultaneously.
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The local update rule asks each cell to check the present states
of the eight surrounding cells.
• If the cell is alive then it stays alive (survives) iff it has
two or three live neighbors. Otherwise it dies of loneliness
or overcrowding.
• If the cell is dead then it becomes alive iff it has exactly
three living neighbors.
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The local update rule asks each cell to check the present states
of the eight surrounding cells.
• If the cell is alive then it stays alive (survives) iff it has
two or three live neighbors. Otherwise it dies of loneliness
or overcrowding.
• If the cell is dead then it becomes alive iff it has exactly
three living neighbors.
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The local update rule asks each cell to check the present states
of the eight surrounding cells.
• If the cell is alive then it stays alive (survives) iff it has
two or three live neighbors. Otherwise it dies of loneliness
or overcrowding.
• If the cell is dead then it becomes alive iff it has exactly
three living neighbors.
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The local update rule asks each cell to check the present states
of the eight surrounding cells.
• If the cell is alive then it stays alive (survives) iff it has
two or three live neighbors. Otherwise it dies of loneliness
or overcrowding.
• If the cell is dead then it becomes alive iff it has exactly
three living neighbors.
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A typical snapshot of a time evolution in Game-of-Life:
Initial uniformly random configuration.
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A typical snapshot of a time evolution in Game-of-Life:
The next generation after all cells applied the update rule.
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A typical snapshot of a time evolution in Game-of-Life:
Generation 10
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A typical snapshot of a time evolution in Game-of-Life:
Generation 100
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GOL is a computationally universal two-dimensional CA:
using gliders as information, one can implement logical gates
AND, OR and NOT.
It is then possible to simulate a computationally universal
counter machine.
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Another famous universal CA: rule 110 by S.Wolfram.
A one-dimensional CA with binary state set {0, 1}, i.e. a
two-way infinite sequence of 0’s and 1’s.
Each cell is updated based on its old state and the states of its
left and right neighbors as follows:
111 −→ 0
110 −→ 1
101 −→ 1
100 −→ 0
011 −→ 1
010 −→ 1
001 −→ 1
000 −→ 0
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Another famous universal CA: rule 110 by S.Wolfram.
A one-dimensional CA with binary state set {0, 1}, i.e. a
two-way infinite sequence of 0’s and 1’s.
Each cell is updated based on its old state and the states of its
left and right neighbors as follows:
111 −→ 0
110 −→ 1
101 −→ 1
100 −→ 0
011 −→ 1
010 −→ 1
001 −→ 1
000 −→ 0
110 is the Wolfram number of this CA rule.
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Space-time diagram is a pictorial representation of a time
evolution in one-dimensional CA, where space and time are
represented by the horizontal and vertical direction:
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General definition of d-dimensional CA
• Finite state set S.
• Cells are indexed by integer coordinates Zd.
• Configurations Zd −→ S assign states to all cells. The
set of all configurations is SZd
.
• A neighborhood is a finite set N ⊆ Zd that provides the
relative offsets to neighbors.
• The neighbors of a cell at location ~x ∈ Zd are the cells at
locations
~x+ ~n, for ~n ∈ N .
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Typical two-dimensional neighborhoods:
c c
Von Neumann Moore
neighborhood neighborhood
{(0, 0), (±1, 0), (0,±1)} {−1, 0, 1} × {−1, 0, 1}
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The local rule is a function
f : Sn −→ S
where n is the size of the neighborhood.
State f(a1, a2, . . . , an) is the new state of a cell whose n
neighbors were at states a1, a2, . . . , an one time step before.
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Global dynamics of the CA: Configuration c becomes in one
time step the configuration e obtained by applying the local
rule at every cell.
The transformation
G : SZd
−→ SZd
that maps c 7→ e is the global transition function of the CA.
Function G is our main object of study and we simply call it a
CA function. In algorithmic questions we use its finite
presentation (the local rule).
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Lattice gas example
CA have traditionally been used in simulations of physical
systems. Good examples are lattice gases.
These are CA simulations of fluid or gas dynamics based on
storing individual molecules in the cells and implementing
particle interactions by the CA local rule.
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Simplest lattice gas model: HPP (due to Hardy, Pomeau and
de Pazzis).
A two-dimensional CA where each cell can store up to four
moving particles. Each particle has a direction of movement
which can be up, down, left or right:
There can be at most one particle of each direction in any
individual cell. So there are 24 = 16 possible states:
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At each time step
• each particle moves to the neighboring cell as indicated by
the direction of the particle
• If a cell receives exactly two particles moving in opposite
directions then the particles turn 90◦.
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At each time step
• each particle moves to the neighboring cell as indicated by
the direction of the particle
• If a cell receives exactly two particles moving in opposite
directions then the particles turn 90◦.
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At each time step
• each particle moves to the neighboring cell as indicated by
the direction of the particle
• If a cell receives exactly two particles moving in opposite
directions then the particles turn 90◦.
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At each time step
• each particle moves to the neighboring cell as indicated by
the direction of the particle
• If a cell receives exactly two particles moving in opposite
directions then the particles turn 90◦.
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At each time step
• each particle moves to the neighboring cell as indicated by
the direction of the particle
• If a cell receives exactly two particles moving in opposite
directions then the particles turn 90◦.
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At each time step
• each particle moves to the neighboring cell as indicated by
the direction of the particle
• If a cell receives exactly two particles moving in opposite
directions then the particles turn 90◦.
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The local rule of HPP preserves the total number of particles
and their total momentum. These are conservation laws
that hold in the HPP automaton.
(Remark: HPP is not a realistic lattice gas model as it has
several incorrect conservation laws.)
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Even more interestingly, the HPP local rule fully preserves
information. There is another CA that traces back the
configurations in the reverse direction. This inverse CA
simply moves the particles to the opposite direction, and
applies the same collision rule as HPP.
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Reversible CA
A CA is called
• injective if G is one-to-one,
• surjective if G is onto,
• bijective if G is both one-to-one and onto.
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Reversible CA
A CA is called
• injective if G is one-to-one,
• surjective if G is onto,
• bijective if G is both one-to-one and onto.
A CA G is a reversible (RCA) if there is another CA
function F that is its inverse, i.e.
G ◦ F = F ◦G = identity function.
RCA G and F are called the inverse automata of each other.
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Game-of-Life and Rule 110 are not reversible:
Configurations may have several pre-images.
HPP lattice gas is reversible.
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Theorem (Hedlund et.al 1969) Every bijective cellular
automaton is reversible.
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Theorem (Hedlund et.al 1969) Every bijective cellular
automaton is reversible.
The point of the Theorem is that if G is bijective then each
cell can determine its previous state by looking at the current
states in some bounded neighborhood around it.
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Theorem (Hedlund et.al 1969) Every bijective cellular
automaton is reversible.
The point of the Theorem is that if G is bijective then each
cell can determine its previous state by looking at the current
states in some bounded neighborhood around it.
Theorem (Kari 1989) It is undecidable if a given
two-dimensional cellular automaton is reversible.
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Theorem (Hedlund et.al 1969) Every bijective cellular
automaton is reversible.
The point of the Theorem is that if G is bijective then each
cell can determine its previous state by looking at the current
states in some bounded neighborhood around it.
Theorem (Kari 1989) It is undecidable if a given
two-dimensional cellular automaton is reversible.
This implies that there is no computable upper bound on
the extend of the neighborhood needed in the inverse time.
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Theorem (Toffoli 1977) Any d-dimensional cellular
automaton can be simulated by a (d+ 1)-dimensional
reversible cellular automaton.
Corollary: Computationally universal two-dimensional
reversible cellular automata exist.
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Theorem (Toffoli 1977) Any d-dimensional cellular
automaton can be simulated by a (d+ 1)-dimensional
reversible cellular automaton.
Corollary: Computationally universal two-dimensional
reversible cellular automata exist.
Even better:
Theorem (Harao,Morita 1989) Computationally universal
reversible one-dimensional cellular automata exist.
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Garden-Of-Eden and orphans
Configurations that do not have a pre-image are called
Garden-Of-Eden -configurations. Only non-surjective CA
have GOE configurations.
A finite pattern consists of a finite domain D ⊆ Zd and an
assignment
p : D −→ S
of states.
Finite pattern is called an orphan for CA G if the pattern
does not have a pre-image.
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Every configuration containing an orphan is a GOE.
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Every configuration containing an orphan is a GOE.
An easy compactness reasoning gives the converse:
Proposition: Every GOE configuration contains an orphan
pattern.
Non-surjectivity is hence equivalent to the existence of
orphans.
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Balance in surjective CA
All surjective CA have balanced local rules: for every a ∈ S
∣
∣f−1(a)∣
∣ = |S|n−1.
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Balance in surjective CA
All surjective CA have balanced local rules: for every a ∈ S
∣
∣f−1(a)∣
∣ = |S|n−1.
Indeed, consider a non-balanced local rule such as rule 110
where five contexts give new state 1 while only three contexts
give state 0:
111 −→ 0
110 −→ 1
101 −→ 1
100 −→ 0
011 −→ 1
010 −→ 1
001 −→ 1
000 −→ 0
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Consider finite patterns where state 0 appears in every third
position. There are 22(k−1) = 4k−1 such patterns where k is
the number of 0’s.
0 0 0 0
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Consider finite patterns where state 0 appears in every third
position. There are 22(k−1) = 4k−1 such patterns where k is
the number of 0’s.
0 0 0 0
A pre-image of such a pattern must consist of k segments of
length three, each of which is mapped to 0 by the local rule.
There are 3k choices.
As for large values of k we have 3k < 4k−1, there are fewer
choices for the red cells than for the blue ones. Hence some
pattern has no pre-image and it must be an orphan.
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One can also verify directly that pattern
01010
is an orphan of rule 110. It is the shortest orphan.
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Balance of the local rule is not sufficient for surjectivity. For
example, the majority CA (Wolfram number 232) is a
counter example. The local rule
f(a, b, c) = 1 if and only if a+ b+ c ≥ 2
is clearly balanced, but 01001 is an orphan.
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The balance property of surjective CA generalizes to finite
patterns of arbitrary shape:
Theorem: Let G be surjective. Let M,D ⊆ Zd be finite
domains such that D contains the neighborhood of M . Then
every finite pattern with domain M has the same number
n|D|−|M |
of pre-images in domain D, where n is the number of states.
D M
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The balance property means that the uniform probability
measure is invariant for surjective CA. (Uniform randomness
is preserved by surjective CA.)
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Garden-Of-Eden -theorem
Let us call configurations c1 and c2 asymptotic if the set
diff (c1, c2) = {~n ∈ Zd | c1(~n) 6= c2(~n) }
of positions where c1 and c2 differ is finite.
A CA is called pre-injective if any asymptotic c1 6= c2 satisfy
G(c1) 6= G(c2).
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The Garden-Of-Eden -theorem by Moore (1962) and
Myhill (1963) connects surjectivity with pre-injectivity.
Theorem: CA G is surjective if and only if it is pre-injective.
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The Garden-Of-Eden -theorem by Moore (1962) and
Myhill (1963) connects surjectivity with pre-injectivity.
Theorem: CA G is surjective if and only if it is pre-injective.
The proof idea can be easily explained using rule 110 as a
running example.
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1) G not surjective =⇒ G not pre-injective:
Since rule 110 is not surjective it has an orphan 01010 of
length five. Consider a segment of length 5k − 2, for some k,
and configurations c that are in state 0 outside this segment.
There are 25k−2 = 32k/4 such configurations.
0 0 0 00 0
5k-2
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1) G not surjective =⇒ G not pre-injective:
The non-0 part of G(c) is within a segment of length 5k.
Partition this segment into k parts of length 5. Pattern 01010
cannot appear in any part, so only 25 − 1 = 31 different
patterns show up in the subsegments. There are at most 31k
possible configurations G(c).
0 0 0 00 0
00 0 0
5k-2
5k
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1) G not surjective =⇒ G not pre-injective:
The non-0 part of G(c) is within a segment of length 5k.
Partition this segment into k parts of length 5. Pattern 01010
cannot appear in any part, so only 25 − 1 = 31 different
patterns show up in the subsegments. There are at most 31k
possible configurations G(c).
0 0 0 00 0
00 0 0
5k-2
5k
As 32k/4 > 31k for large k, there are more choices for red than
blue segments. So there must exist two different red
configurations with the same image.
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2) G not pre-injective =⇒ G not surjective: Can be explained
as easily!
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Garden-Of-Eden -theorem: CA G is surjective if and only
if it is pre-injective.
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Garden-Of-Eden -theorem: CA G is surjective if and only
if it is pre-injective.
Corollary: Every injective CA is also surjective. Injectivity,
bijectivity and reversibility are equivalent concepts.
Proof: If G is injective then it is pre-injective. The claim
follows from the Garden-Of-Eden -theorem.
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Examples:
The majority rule is not surjective: finite configurations
. . . 0000000 . . . and . . . 0001000 . . .
have the same image, so G is not pre-injective. Pattern
01001
is an orphan.
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Examples:
In Game-Of-Life a lonely living cell dies immediately, so G is
not pre-injective. GOL is hence not surjective.
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Interestingly, no small orphans are known for Game-Of-Life.
Currently, the smallest known orphan consists of 92 cells (56
life, 36 dead):
M. Heule, C. Hartman, K. Kwekkeboom, A. Noels (2011)
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Examples:
The Traffic CA is the elementary CA number 226.
111 −→ 1110 −→ 1101 −→ 1100 −→ 0011 −→ 0010 −→ 0001 −→ 1000 −→ 0
The local rule replaces pattern 01 by pattern 10.
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111 −→ 1
110 −→ 1
101 −→ 1
100 −→ 0
011 −→ 0
010 −→ 0
001 −→ 1
000 −→ 0
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111 −→ 1
110 −→ 1
101 −→ 1
100 −→ 0
011 −→ 0
010 −→ 0
001 −→ 1
000 −→ 0
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111 −→ 1
110 −→ 1
101 −→ 1
100 −→ 0
011 −→ 0
010 −→ 0
001 −→ 1
000 −→ 0
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111 −→ 1
110 −→ 1
101 −→ 1
100 −→ 0
011 −→ 0
010 −→ 0
001 −→ 1
000 −→ 0
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The local rule is balanced. However, there are two finite
configurations with the same successor:
and hence traffic CA is not surjective.
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There is an orphan of size four:
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G injective G bijective G reversible
G surjective G pre-injective
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G injective G bijective G reversible
G surjective G pre-injective
XOR
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The xor-CA is the binary state CA with neighborhood (0, 1)
and local rule
f(a, b) = a+ b (mod 2).
In the xor-CA every configuration has exactly two pre-images,
so G is surjective but not injective:
0 0000 0 0 0 01 1 1 1 1 1 1
One can freely choose one value in the pre-image, after which
all remaining states are uniquely determined by the
left-permutativity and the right-permutativity of xor.
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The xor-CA is the binary state CA with neighborhood (0, 1)
and local rule
f(a, b) = a+ b (mod 2).
In the xor-CA every configuration has exactly two pre-images,
so G is surjective but not injective:
0 0000 0 0 0 01 1 1 1 1 1 1
0
One can freely choose one value in the pre-image, after which
all remaining states are uniquely determined by the
left-permutativity and the right-permutativity of xor.
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The xor-CA is the binary state CA with neighborhood (0, 1)
and local rule
f(a, b) = a+ b (mod 2).
In the xor-CA every configuration has exactly two pre-images,
so G is surjective but not injective:
0 0000 0 0 0 01 1 1 1 1 1 1
0 01
One can freely choose one value in the pre-image, after which
all remaining states are uniquely determined by the
left-permutativity and the right-permutativity of xor.
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The xor-CA is the binary state CA with neighborhood (0, 1)
and local rule
f(a, b) = a+ b (mod 2).
In the xor-CA every configuration has exactly two pre-images,
so G is surjective but not injective:
0 0000 0 0 0 01 1 1 1 1 1 1
0 01 00 0 0 0 1
One can freely choose one value in the pre-image, after which
all remaining states are uniquely determined by the
left-permutativity and the right-permutativity of xor.
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The xor-CA is the binary state CA with neighborhood (0, 1)
and local rule
f(a, b) = a+ b (mod 2).
In the xor-CA every configuration has exactly two pre-images,
so G is surjective but not injective:
0 0000 0 0 0 01 1 1 1 1 1 1
0 01 00 0 0 0 10
One can freely choose one value in the pre-image, after which
all remaining states are uniquely determined by the
left-permutativity and the right-permutativity of xor.
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The xor-CA is the binary state CA with neighborhood (0, 1)
and local rule
f(a, b) = a+ b (mod 2).
In the xor-CA every configuration has exactly two pre-images,
so G is surjective but not injective:
0 0000 0 0 0 01 1 1 1 1 1 1
0 01 00 0 0 0 10111001
One can freely choose one value in the pre-image, after which
all remaining states are uniquely determined by the
left-permutativity and the right-permutativity of xor.
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Theorem (Kari 1989) It is undecidable if a given
two-dimensional CA is surjective.
Corollary: There is no computable upper bound on the
extend of the smallest orphan of non-surjective CA.
(Both reversibility and surjectivity can be tested in polynomial
time in dimension 1.)
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Conclusion
Cellular Automata have a rich theory developed over half a
century. Yet many questions remain open. For example:
• Does there exist a one-dimensional CA with states {0, 1}
such that every periodic configuration eventually leads to
the blinking orbit . . . 00000 . . .←→ . . . 11111 . . . ?
Thank You
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Conclusion
Cellular Automata have a rich theory developed over half a
century. Yet many questions remain open. For example:
• Does there exist a one-dimensional CA with states {0, 1}
such that every periodic configuration eventually leads to
the blinking orbit . . . 00000 . . .←→ . . . 11111 . . . ?
Let G be a two-dimensional surjective cellular automaton.
• Does every periodic configuration have a periodic
pre-image?
• Does every finite pattern occur in a configuration that is
temporally repeating (that is Gn(c) = c for some n) ?
Thank You
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Conclusion
Cellular Automata have a rich theory developed over half a
century. Yet many questions remain open. For example:
• Does there exist a one-dimensional CA with states {0, 1}
such that every periodic configuration eventually leads to
the blinking orbit . . . 00000 . . .←→ . . . 11111 . . . ?
Let G be a two-dimensional surjective cellular automaton.
• Does every periodic configuration have a periodic
pre-image?
• Does every finite pattern occur in a configuration that is
temporally repeating (that is Gn(c) = c for some n) ?
Thank You