Classsification of Cellular Automata

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Dec 1, 2013 (3 years and 9 months ago)

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Classsification of Cellular Automata
Klaus Sutner
Carnegie Mellon University
Pittsburgh,PA 15213
Contents
Glossary 1
1 Definition 2
2 Introduction 3
3 Reversibility and Surjectivity 6
4 Definability and Computability 9
5 Computational Equivalence 15
6 Conclusion 18
References 20
Glossary
Cellular Automaton
For our purposes,a (one-dimensional) cellular automaton (CA) is given by a local map
ρ:Σ
w
→Σ where Σ is the underlying alphabet of the automaton and w is its width.As a
data structure,suitable as input to a decision algorithm,a CA can thus be specified by a
simple lookup table.We abuse notation and write ρ(x) for the result of applying the global
map of the CA to configuration x ∈ Σ
Z
.
Wolfram Classes
Wolfram proposed a heuristic classification of cellular automata based on observations of
typical behaviors.The classification comprises four classes:evolution leads to trivial con-
figurations,to periodic configurations,evolution is chaotic,evolution leads to complicated,
persistent structures.
Undecidability
1
It was recognized by logicians and mathematicians in the first half of the 20th century
that there is an abundance of well-defined problems that cannot be solved by means of an
algorithm,a mechanical procedure that is guaranteed to terminate after finitely many steps
and produce the appropriate answer.The best known example of an undecidable problem
is Turing’s Halting Problem:there is no algorithm to determine whether a given Turing
Machine halts when run on an empty tape.
Semi-Decidability
A problem is said to be semi-decidable or computably enumerable if it admits an al-
gorithm that return “yes” after finitely many steps if this is indeed the correct answer.
Otherwise the algorithm never terminates.The Halting Problem is the standard example
for a semi-decidable problem.A problem is decidable if,and only if,the problem itself and
its negation are semi-decidable.
Universality
A computational device is universal it is capable of simulating any other computational
device.The existence of universal computers was another central insight of the early days
of computability theory and is closely related to undecidability.
Reversibility
A discrete dynamical system is reversible if the evolution of the system incurs no loss of
information:the state at time t can be recovered from the state at time t +1.For CAs this
means that the global map is injective.
Surjectivity
The global map of a CA is surjective if every configuration appears as the image of
another.By contrast,a configuration that fails to have a predecessor is often referred to as
a Garden-of-Eden.
Finite Configurations
One often considers CA with a special quiescent state:the homogeneous configuration
where all cells are in the quiescent state is required to be fixed point under the global map.
Infinite configurations where all but finitely many cells are in the quiescent state are often
called finite configurations.This is somewhat of a misnomer;we prefer to speak about
configurations with finite support.
1 Definition
Cellular automata display a large variety of behaviors.This was recognized clearly when
extensive simulations of cellular automata,and in particular one-dimensional CA,became
computationally feasible around 1980.Surprisingly,even when one considers only elemen-
tary CA,which are constrained to a binary alphabet and local maps involving only nearest
neighbors,complicated behaviors are observed in some cases.In fact,it appears that most
behaviors observed in automata with more states and larger neighborhoods already have
qualitative analogues in the realm of elementary CA.Careful empirical studies lead Wol-
fram to suggest a phenomenological classification of CA based on the long-term evolution of
configurations,see [75,78] and section 2.While Wolfram’s four classes clearly capture some
2
of the behavior of CA it turns out that any attempt at formalizing this taxonomy meets
with considerable difficulties.Even apparently simple questions about the behavior of CA
turn out to be algorithmically undecidable and it is highly challenging to provide a detailed
mathematical analysis of these systems.
2 Introduction
In the early 1980’s Wolfram published a collection of 20 open problems in the the theory
of CA,see [76].The first problem on his list is “What overall classification of cellular au-
tomata behavior can be given?” As Wolfram points out,experimental mathematics provides
a first answer to this problem:one performs a large number of explicit simulations and ob-
serves the patterns associated with the long term evolution of a configuration,see [74,78].
Wolfram proposed a classification that is based on extensive simulations in particular of
one-dimensional cellular automata where the evolution of a configuration can be visualized
naturally as a two-dimensional image.The classification involves four classes that can be
described as follows:
• W1:Evolution leads to homogeneous fixed points.
• W2:Evolution leads to periodic configurations.
• W3:Evolution leads to chaotic,aperiodic patterns.
• W4:Evolution produces persistent,complex patterns of localized structures.
Thus,Wolfram’s first three classes follow closely concepts from continuous dynamics:
fixed point attractors,periodic attractors and strange attractors,respectively.They corre-
spond roughly to systems with zero temporal and spatial entropy,zero temporal entropy but
positive spatial entropy,and positive temporal and spatial entropy,respectively.W4 is more
difficult to associate with a continuous analogue except to say that transients are typically
very long.To understand this class it is preferable to consider CAas models of massively par-
allel computation rather than as particular discrete dynamical systems.It was conjectured
by Wolfram that W4 automata are capable of performing complicated computations and
may often be computationally universal.Four examples of elementary CA that are typical
of the four classes are shown in figure 1.Li and Packard [35,36] proposed a slightly modified
version of this hierarchy by refining the low classes and in particular Wolfram’s W2.Much
like Wolfram’s classification,the Li-Packard classification is concerned with the asymptotic
behavior of the automaton,the structure and behavior of the limiting configurations.Here
is one version of the Li-Packard classification,see [36].
• LP1:Evolution leads to homogeneous fixed points.
• LP2:Evolution leads to non-homogeneous fixed points,perhaps up a to a shift.
3
Figure 1:Typical examples of the behavior described by Wolfram’s classes among elementary
cellular automata.
4
• LP3:Evolution leads to ultimately periodic configurations.Regions with periodic
behavior are separated by domain walls,possibly up to a shift.
• LP4:Configurations produce locally chaotic behavior.Regions with chaotic behavior
are separated by domain walls,possibly up to a shift.
• LP5:Evolution leads to chaotic patterns that are spatially unbounded.
• LP6:Evolution is complex.Transients are long and lead to complicated space-time
patterns which may be non-monotonic in their behavior.
By contrast,a classification closer to traditional dynamical systems theory was introduced
by K˚urka,see [29,30].The classification rests on the notions of equicontinuity,sensitivity to
initial conditions and expansivity.Suppose x is a point in some metric space and f a map
on that space.Then f is equicontinuous at x if
∀ε > 0 ∃δ > 0 ∀y ∈ B
δ
(x),n ∈ N(d(f
n
(x),f
n
(y)) < ε)
where d(.,.) denotes a metric.Thus,all points in a sufficiently small neighborhood of x
remain close to the iterates of x for the whole orbit.Global equicontinuity is a fairly strong
condition,it implies that the limit set of the automaton is reached after finitely many steps.
The map is sensitive (to initial conditions) if
∀x,ε > 0 ∃δ > 0 ∀y ∈ B
δ
(x) ∃n ∈ N(d(f
n
(x),f
n
(y)) ≥ ε)
Lastly,the map is positively expansive if
∃ε > 0 ∀x ￿= y ∃n ∈ N(d(f
n
(x),f
n
(y)) ≥ ε)
K˚urka’s classification then takes the following form.
• K1:All points are equicontinuous under the global map.
• K2:Some but not all points are equicontinuous under the global map.
• K3:The global map is sensitive but not positively expansive.
• K4:The global map is positively expansive.
This type of classification is perfectly suited to the analysis of uncountable spaces such
as the Cantor space {0,1}
N
or the full shift space Σ
Z
which carry a natural metric structure.
For the most part we will not pursue the analysis of CA by topological and measure theoretic
means here and refer to [31] in this volume for a discussion of these methods.See section 4
for the connections between topology and computability.
Given the apparent complexity of observable CA behavior one might suspect that it is
difficult to pinpoint the location of an arbitrary given CA in any particular classification
scheme with any precision.This is in contrast to simple parameterizations of the space of
5
CA rules such as Langton’s λ parameter that are inherently easy to compute.Briefly,the λ
value of a local map is the fraction of local configurations that map to a non-zero value,see
[32,36].Small λ values result in short transients leading to fixed points or simple periodic
configurations.As λ increases the transients grow longer and the orbits become more and
more complex until,at last,the dynamics become chaotic.Informally,sweeping the λ value
from0 to 1 will produce CA in W1,then W2,then W4 and lastly in W3.The last transition
appears to be associated with a threshold phenomenon.It is unclear what the connection
between Langton’s λ-value and computational properties of a CA is,see [51,41].Other
numerical measures that appear to be loosely connected to classifications are the mean field
parameters of Gutowitz [21,22],the Z-parameter by Wuensche [79],see also [49].It seems
doubtful that a structured taxonomy along the lines of Wolframor Li-Packard can be derived
from a simple numerical measure such as the λ value alone,or even from a combination of
several such values.However,they may be useful as empirical evidence for membership in a
particular class.
Classification also becomes significantly easier when one restricts one’s attention to a
limited class of CA such as additive CA,see [70].In this context,additive means that the
local rule of the automaton has the form ρ(￿x) =
￿
i
c
i
x
i
where the coefficients as well as the
states are modular numbers.Anumber of properties starting with injectivity and surjectivity
as well as topological properties such as equicontinuity and sensitivity can be expressed in
terms of simple arithmetic conditions on the rule coefficients.For example,equicontinuity
is equivalent to all prime divisors of the modulus m dividing all coefficients c
i
,i > 1,see
[38] and the references therein.It is also noteworthy that in the linear case methods tend to
carry over to arbitrary dimensions;in general there is a significant step in complexity from
dimension one to dimension two.
No claim is made that the given classifications are complete;in fact,one should think of
them as prototypes rather than definitive taxonomies.For example,one might add the class
of nilpotent CA at the bottom.A CA is nilpotent if all configurations evolve to a particular
fixed point after finitely many steps.Equivalently,by compactness,there is a bound n such
that all configurations evolve to the fixed point in no more than n steps.Likewise,we could
add the class of intrinsically universal CA at the top.A CA is intrinsically universal if
it is capable of simulating all other CA of the same dimension in some reasonable sense.
For a fairly natural notion of simulation see [50].At any rate,considerable effort is made
in the references to elaborate the characteristics of the various classes.For many concrete
CA visual inspection of the orbits of a suitable sample of configurations readily suggests
membership in one of the classes.
3 Reversibility and Surjectivity
A first tentative step towards the classification of a dynamical systems is to determine its
reversibility or lack thereof.Thus we are trying to determine whether the evolution of the
system is associated with loss of information,or whether it is possible to reconstruct the
state of the system at time t from its state at time t +1.In terms of the global map of the
6
system we have to decide injectivity.Closely related is the question whether the global map
is surjective,i.e.,whether there is no Garden-of-Eden:every configuration has a predecessor
under the global map.As a consequence,the limit set of the automaton is the whole space.It
was shown of Hedlund that for CA the two notions are connected:every reversible CA is also
surjective,see [25,44].As a matter of fact,reversibility of the global map of a CA implies
openness of the global map,and openness implies surjectivity.The converse implications are
both false.By a well-known theorem by Hedlund [25] the global maps of CA are precisely
the continuous maps that commute with the shift.It follows from basic topology that the
inverse global map of a reversible CA is again the global map of a suitable CA.Hence,the
predecessor configuration of a given configuration can be reconstructed by another suitably
chosen CA.For results concerning reversibility on the limit set of the automaton see [67].
From the perspective of complexity the key result concerning reversible systems is the
work by Lecerf [33] and Bennett [7].They show that reversible Turing machines can compute
any partial recursive function,modulo a minor technical problem:In a reversible Turing
machine there is no loss of information;on the other hand even simple computable functions
are clearly irreversible in the sense that,say,the sum of two natural numbers does not
determine these numbers uniquely.To address this issue one has to adjust the notion of
computability slightly in the context of reversible computation:given a partial recursive
function f:N →N the function
￿
f(x) = ￿x,f(x)￿ can be computed by a reversible Turing
machine where ￿.,.￿ is any effective pairing function.If f itself happens to be injective
then there is no need for the coding device and f can be computed by a reversible Turing
machine directly.For example,we can compute the product of two primes reversibly.Morita
demonstrated that the same holds true for one-dimensional cellular automata [68,45,42,
28]:reversibility is no obstruction to computational universality.As a matter of fact,any
irreversible cellular automaton can be simulated by a reversible one,at least on configurations
with finite support.Thus one should expect reversible CA to exhibit fairly complicated
behavior in general.
For infinite,one-dimensional CA it was shown by Amoroso and Patt [2] that reversibility
is decidable.Moreover,it is decidable if the the global map is surjective.An efficient practical
algorithm using concepts of automata theory can be found in [61],see also [14,24,10].The
fast algorithm is based on interpreting a one-dimensional CA as deterministic transducer,
see [6,53] for background.The underlying semi-automaton of the transducer is a de Brujin
automaton B whose states are words in Σ
w−1
where Σ is the alphabet of the CA and w is its
width.The transitions are given by ax
c
−→xb where a,b,c ∈ Σ,x ∈ Σ
w−2
and c = ρ(axb),ρ
being the local map of the CA.Since B is strongly connected,the product automaton of B
will contain a strongly connected component C that contains the diagonal D,an isomorphic
copy of B.The global map of the CA is reversible if,and only if,C = D is the only
non-trivial component.It was shown by Hedlund [25] that surjectivity of the global map
is equivalent with local injectivity:the restriction of the map to configurations with finite
support must be injective.The latter property holds if,and only if,C = D and is thus easily
decidable.Automata theory does not readily generalize to words of dimensions higher than
one.Indeed,reversibility and surjectivity in dimensions higher than one are undecidable,see
7
Figure 2:A reversible automaton obtain by applying Fredkin’s construction to the irre-
versible elementary CA 90.
8
[27] and [28] in this volume for the rather intricate argument needed to establish this fact.
While the structure of reversible one-dimensional CA is well-understood,see [28,16],and
while there is an efficient algorithm to check reversibility,few methods are known that allow
for the construction of interesting reversible CA.There is a noteworthy trick due to Fredkin
that exploits the reversibility of the Fibonacci equation X
n+1
= X
n
+X
n−1
.When addition
is interpreted as exclusive or this can be used to construct a second-order CA from any given
binary CA;the former can then be recoded as a first-order CA over a 4-letter alphabet.For
example,for the open but irreversible elementary CA number 90 we obtain the CA shown
in figure 2.
Another interesting class of reversible one-dimensional CA,the so-called partitioned cel-
lular automata (PCA),is due to Morita and Harao,see [45,42,43].One can think of a PCA
as a cellular automaton whose cells are divided into multiple tracks;specifically Morita uses
an alphabet of the form Σ = Σ
1
× Σ
2
× Σ
3
.The configurations of the automaton can be
written as (X,Y,Z) where X ∈ Σ
1
Z
,Y ∈ Σ
2
Z
and Z ∈ Σ
3
Z
.Now consider the shearing
map σ defined by σ(X,Y,Z) = (RS(X),Y,LS(Z)) where RS and LS denote the right and
left shift,respectively.Given any function f:Σ → Σ we can define a global map f ◦ σ
where f is assumed to be applied point-wise.Since the shearing map is bijective,the CA
will be reversible if,and only if,the map f is bijective.It is relatively easy to construct
bijections f that cause the CA to performparticular computational tasks,even when a direct
construction appears to be entirely intractable.
4 Definability and Computability
4.1 Formalizing Wolfram’s Classes
Wolfram’s classification is an attempt to categorize the complexity of the CA by studying the
patterns observed during the long-term evolution of all configurations.The first two classes
are relatively easy to observe,but it is difficult to distinguish between the last two classes.In
particular W4 is closely related to the kind of behavior that would be expected in connection
with systems that are capable of performing complicated computations,including the ability
to perform universal computation;a property that is notoriously difficult to check,see [58].
The focus on the full configuration space rather than a significant subset thereof corresponds
to the worst-case approach well-known in complexity theory and is somewhat inferior to an
average case analysis.Indeed,Baldwin and Shelah point out that a product construction
can be used to design a CA whose behavior is an amalgamation of the behavior of two given
CA,see [4,3].By combining CA in different classes one obtains striking examples of the
weakness of the worst-case approach.A natural example of this mixed type of behavior
is elementary CA 184 which displays class II or class III behavior,depending on the initial
configuration.Another basic example for this type of behavior is the well-studied elementary
CA 30,see section 6.
Still,for many CA a worst-case classification seems to provide useful information about
the structural properties of the automaton.The first attempt at formalizing Wolfram’s class
9
was made by Culik and Yu who proposed the following hierarchy,given here in cumulative
form,see [11]:
• CY1:All configurations evolve to a fixed point.
• CY2:All configurations evolve to a periodic configuration.
• CY3:The orbits of all configurations are decidable.
• CY4:No constraints.
The Culik-Yu classification employs two rather different methods.The first two classes can
be defined by a simple formula in a suitable logic whereas the third (and the fourth in
the disjoint version of the hierarchy) rely on notions of computability theory.As a general
framework for both approaches we consider discrete dynamical systems,structures of the
form A = ￿C,￿￿ where C ⊆ Σ
Z
is the space of configurations of the system and ￿ is the
“next configuration” relation on C.We will only consider the deterministic case where for
each configuration x there exists precisely one configuration y such that x ￿ y.Hence we
are really dealing with algebras with one unary function,but iteration is slightly easier to
deal with in the relational setting.The structures most important in this context are the
ones arising from a CA.For any local map ρ we consider the structure A
ρ
= ￿C,￿￿ where
the next configuration relation is determined by x ￿ ρ(x).
Using the standard language of first order logic we can readily express properties of the
CA in terms of the system A
ρ
.For example,the system is reversible,respectively surjective,
if the following assertions are valid over A:
∀x,y,z (x ￿ z and y ￿ z implies x = y)
∀x∃y (y ￿ x)
As we have seen,both properties are easily decidable in the one-dimensional case.In fact,
one can express the basic predicate x ￿ y (as well as equality) in terms of finite state ma-
chines on infinite words.These machines are defined like ordinary finite state machines but
the acceptance condition requires that certain states are reached infinitely and co-infinitely
often,see [8,20].The emptiness problem for these automata is easily decidable using graph
theoretic algorithms.Since regular languages on infinite words are closed under union,com-
plementation and projection,much like their finite counterparts,and all the corresponding
operations on automata are effective,it follows that one can decide the validity of first order
sentences over A
ρ
such as the two examples above:the model-checking problem for these
structures and first order logic is decidable,see [37].For example,we can decide whether
there is a configuration that has a certain number of predecessors.Alternatively,one can
translate these sentences into monadic second order logic of one successor,and use well-
known automata-based decision algorithms there directly,see [8].Similar methods can be
used to handle configurations with finite support,corresponding to weak monadic second
order logic.Since the complexity of the decision procedure is non-elementary one should not
expect to be able to handle complicated assertions.On the other hand,at least for weak
10
monadic second order logic practical implementations of the decision method exist,see [18].
There is no hope of generalizing this approach as the undecidability of,say,reversibility in
higher dimensions demonstrates.
Write x
t
→y if x evolves to y in exactly t steps,x
+
→y if x evolves to y in any positive
number of steps and x

→y if x evolves to y in any number of steps.Note that
t
→is definable
for each fixed t,but

→ fails to be so definable in first order logic.This is in analogy to the
undefinability of path existence problems in the first order theory of graphs,see [37].Hence
it is natural to extend our language so we can express iterations of the global map,either
by adding transitive closures or by moving to some limited system of higher order logic over
A
ρ
where

→is definable,see [8].
Arguably the most basic decision problem associated with a system A that requires
iteration of the global map is the Reachability Problem:given two configurations x and y,
does the evolution of x lead to y?A closely related but different question is the Confluence
Problem:will two configurations x and y evolve to the same limit cycle?Confluence is an
equivalence relation and allows for the decomposition of configuration space into limit cycles
together with their basins of attraction.The Reachability and Confluence Problem amount
to determining,given configurations x and y,whether
x

→y,
∃z (x

→z and y

→z),
respectively.As another example,the first two Culik-Yu class can be defined like so:
∀x∃z (x

→z and z ￿ z),
∀x∃z (x

→z and z
+
→z).
It is not difficult to give similar definitions for the lower Li-Packard classes if one extends
the language by a function symbol denoting the shift operator.
The third Culik-Yu class is somewhat more involved.By definition,a CA lies in the third
class if it admits a global decision algorithm to determine whether a given configuration x
evolves to another given configuration y in a finite number of steps.In other words,we are
looking for automata where the Reachability Problem is algorithmically solvable.While one
can agree that W4 roughly translates into undecidability and is thus properly situated in the
hierarchy,it is unclear howchaotic patterns in W3 relate to decidability.No method is known
to translate the apparent lack of tangible,persistent patterns in rules such as elementary
CA 30 into decision algorithms for Reachability.There is another,somewhat more technical
problem to overcome in formalizing classifications.Recall that the full configuration space
is C = Σ
Z
.Intuitively,given x ∈ C we can effectively determine the next configuration
y = ρ(x).However,classical computability theory does not deal with infinitary objects such
as arbitrary configuration so a bit of care is needed here.The key insight is that we can
determine arbitrary finite segments of ρ(x) using only finite segments of x (and,of course,
the lookup table for the local map).There are several ways to model computability on
11
Σ
Z
based on this idea of finite approximations,we refer to [73] for a particularly appealing
model based on so-called type-2 Turing machines;the reference also contains many pointers
to the literature as well as a comparison between the different approaches.It is easy to see
that for any CA the global map ρ as well as all its iterates ρ
t
are computable,the latter
uniformly in t.However,due to the finitary nature of all computations,equality is not
decidable in type-2 computability:the unequal operator U
0
(x,y) = 0 if x ￿= y,U
0
(x,y)
undefined otherwise,is computable and thus unequality is semi-decidable,but the stronger
U
0
(x,y) = 0 if x ￿= y,U
0
(x,y) = 1,otherwise,is not computable.The last result is
perhaps somewhat counterintuitive,but it is inevitable if we strictly adhere to the finite
approximation principle.
In order to avoid problems of this kind it has become customary to consider certain sub-
spaces of the full configuration space,in particular C
fin
,the collection of configurations with
finite support,C
per
,the collection of spatially periodic configurations and C
ap
,the collection
of almost periodic configurations of the form...uuuwvvv...where u,v and w are all finite
words over the alphabet of the automaton.Thus,an almost periodic configuration differs
from a configuration of the form
ω
uv
ω
in only finitely many places.Configurations with
finite support correspond to the special case where u = v = 0 is a special quiescent symbol
and spatially periodic configurations correspond to u = v,w = ε.The most general type of
configuration that admits a finitary description is the class C
rec
of recursive configurations,
where the assignment of state to a cell is given by a computable function.
It is clear that all these subspaces are closed under the application of a global map.Except
for C
fin
there are also closed under inverse maps in the following sense:given a configuration
y in some subspace that has a predecessor x in C
all
there already exists a predecessor in the
same subspace,see [64,61].This is obvious except in the case of recursive configurations.
The reference also shows that the recursive predecessor cannot be computed effectively from
the target configuration.Thus,for computational purposes the dynamics of the cellular
automaton are best reflected in C
ap
:it includes all configuration with finite support and we
can effectively trace an orbit in both directions.It is not hard to see that C
ap
is the least
such class.Alas,it is standard procedure to avoid minor technical difficulties arising from
the infinitely repeated spatial patterns and establish classifications over the subspace C
fin
.
There is a arguably not much harm in this simplification since C
fin
is a dense subspace of C
all
and compactness can be used to lift properties from C
fin
to the full configuration space.
The Culik-Yu hierarchy is correspondingly defined over C
fin
,the class of all configurations
of finite support.In this setting,the first three classes of this hierarchy are undecidable and
the fourth is undecidable in the disjunctive version:there is no algorithm to test whether a
CA admits undecidable orbits.As it turns out,the CA classes are complete in their natural
complexity classes within the arithmetical hierarchy [56,58].Checking membership in the
first two classes comes down to performing an infinite number of potentially unbounded
searches and can be described logically by a Π
2
expression,a formula of type ∀x∃y R(x,y)
where R is a decidable predicate.Indeed,CY1 and CY2 are both Π
2
-complete.Thus,
deciding whether all configurations on a CA evolve to a fixed point is equivalent to the
classical problem of determining whether a semi-decidable set is infinite.The third class is
12
even less amenable to algorithmic attack;one can show that CY3 is Σ
3
-complete,see [59].
Thus,deciding whether all orbits are decidable is as difficult as determining whether any
given semi-decidable set is decidable.It is not difficult to adjust these undecidability results
to similar classes such as the lower levels of the Li-Packard hierarchy that takes into account
spatial displacements of patterns.
4.2 Effective Dynamical Systems and Universality
The key property of CA that is responsible for all these undecidability results is the fact
that CA are capable of performing arbitrary computations.This is unsurprising when one
defines computability in terms of Turing machines,the devices introduced by Turing in the
1930’s,see [69,52].Unlike the G¨odel-Herbrand approach using general recursive functions
or Church’s λ-calculus,Turing’s devices are naturally closely related to discrete dynamical
systems.For example,we can express an instantaneous description of a Turing machine as
a finite sequence
a
−l
a
−l+1
...a
−1
p a
1
a
2
...a
r
where the a
i
are tape symbols and p is a state of the machine,with the understanding that
the head is positioned at a
1
and that all unspecified tape cells contain the blank symbol.
Needless to say,these Turing machine configurations can also be construed as finite sup-
port configurations of a one-dimensional CA.It follows that a one-dimensional CA can be
used to simulate an arbitrary Turing machine,hence CA are computational universal:any
computable function whatsoever can already be computed by a CA.
Note,though,that the simulation is not entirely trivial.First,we have to rely on in-
put/output conventions.For example,we may insist that objects in the input domain,
typically tuples of natural numbers,are translated into a configuration of the CA by a prim-
itive recursive coding function.Second,we need to adopt some convention that determines
when the desired output has occurred:we follow the evolution of the input configuration
until some “halting” condition applies.Again,this condition must be primitive recursively
decidable though there is considerable leeway as to how the end of a computation should
be signaled by the CA.For example,we could insist that a particular cell reaches a special
state,that an arbitrary cell reaches a special state,that the configuration be a fixed point
and so forth.Lastly,if and when a halting configuration is reached,we a apply a primitive
recursive decoding function to obtain the desired output.
Restricting the space to configurations that have finite support,that are spatially peri-
odic,and so forth,produces an effective dynamical system:the configurations can be coded
as integers in some natural way,and the next configuration relation is primitive recursive
in the sense that the corresponding relation on code numbers is so primitive recursive.A
classical example for an effective dynamical system is given by selecting the instantaneous
descriptions of a Turing machine M as configurations,and one-step relation of the Turing
machine as the operation of C.Thus we obtain a system A
M
whose orbits represent the
computations of the Turing machine.Likewise,given the local map ρ of a CA we obtain a
system A
ρ
whose operation is the induced global map.While the full configuration space C
all
13
violates the effectiveness condition,any of the spaces C
per
,C
fin
,C
ap
and C
rec
will give rise to an
effective dynamical system.Closure properties as well as recent work on the universality of
elementary CA 110,see section 6,suggests that the class of almost periodic configurations,
also known as backgrounds or wallpapers,see [64,9],is perhaps the most natural setting.
Both C
fin
and C
ap
provide a suitable setting for a CA that simulates a Turing machine:we
can interpret A
M
as a subspace of A
ρ
for some suitably constructed one-dimensional CA ρ;
the orbits of the subspace encode computations of the Turing machine.It follows from the
undecidability of the Halting Problem for Turing machines that the Reachability Problem
for these particular CA is undecidable.
Note,though,that orbits in A
M
may well be finite,so some care must be taken in setting
up the simulation.For example,one can translate halting configurations into fixed points.
Another problem is caused by the worst-case nature of our classification schemes:in Turing
machines and their associated systems A
M
it is only behavior on specially prepared initial
configurations that matters,whereas the behavior of a CAdepends on all configurations.The
behavior of a Turing machine on all instantaneous descriptions,rather than just the ones that
can occur during a legitimate computation on some actual input,was first studied by Davis,
see [12,13],and also Hooper [26].Call a Turing machine stable if it halts on any instantaneous
description whatsoever.With some extra care one can then construct a CA that lies in the
first Culik-Yu class,yet has the same computational power as the Turing machine.Davis
showed that every total recursive function can already be computed by a stable Turing
machine,so membership in CY1 is not an impediment to considerable computational power.
The argument rests on a particular decomposition of recursive functions.Alternatively,one
directly manipulate Turing machines to obtain a similar result,see [55,59].On the other
hand,unstable Turing machines yield a natural and coding-free definition of universality:a
Turing machine is Davis-universal if the set of all instantaneous description on which the
machine halts is Σ
1
-complete.
The mathematical theory of infinite CAis arguably more elegant than the actually observ-
able finite case.As a consequence,classifications are typically concerned with CA operating
on infinite grids,so that even a configuration with finite support can carry arbitrarily much
information.If we restrict our attention to the space of configurations on a finite grid a more
fine-grained analysis is required.For a finite grid of size n the configuration space has the
form C
n
= [n] →Σ and is itself finite,hence any orbit is ultimately periodic and the Reach-
ability Problem is trivially decidable.However,in practice there is little difference between
the finite and infinite case.First,computational complexity issues make it practically impos-
sible to analyze even systems of modest size.The Reachability Problem for finite CA,while
decidable,is PSPACE-complete even in the one-dimensional case.Computational hardness
appears in many other places.For example,if we try to determine whether a given configu-
ration on a finite grid is a Garden-of-Eden the problem turns out to be NLOG-complete in
dimension one and NP-complete in all higher dimensions,see [62].
Second,it stands to reason that the more interesting classification problem in the finite
case takes the following parameterized form:given a local map together with boundary con-
ditions,determine the behavior of ρ on all finite grids.Under periodic boundary conditions
14
this comes down to the study of C
per
and it seems that there is little difference between this
and the fixed boundary case.Since all orbits on a finite grid are ultimately periodic one
needs to apply a more fine-grained classification that takes into account transient lengths.
It is undecidable whether all configurations on all finite grids evolve to a fixed point under
a given local map,see [60].Thus,there is no algorithm to determine whether
￿C
n
,￿￿ |= ∀x∃z (x

→z and z ￿ z)
for all grid sizes n.The transient lengths are trivially bounded by k
n
where k is the size
of the alphabet of the automaton.It is undecidable whether the transient lengths grow
according to some polynomial bound,even when the polynomial in question is constant.
Restrictions of the configuration space are one way to obtain an effective dynamical
system.Another is to interpret the approximation-based notion of computability on the
full space in terms of topology.It is well-known that computable maps C
all
→ C
all
are
continuous in the standard product topology.The clopen sets in this topology are the finite
unions of cylinder sets where a cylinder set is determined by the values of a configuration
in finitely many places.By a celebrated result of Hedlund the global maps of a CA on
the full space are characterized by being continuous and shift-invariant.Perhaps somewhat
counter-intuitively,the decidable subsets of C
all
are quite weak,they consist precisely of the
clopen sets.Now consider a partition of C
all
into finitely many clopen sets C
0
,C
2
,...,
C
n−1
.Thus,it is decidable which block of the partition a given point in the space belongs
to.Moreover,Boolean operations on clopen sets as well as application of the global map
and the inverse global map are all computable.The partition affords a natural projection
π:C
all
→ Σ
n
where Σ
n
= {0,1,...,n −1} and π(x) = i iff x ∈ C
i
.Hence the projection
translates orbits in the full space C
all
into a class W of ω-words over Σ
n
,the symbolic orbits
of the system.The Cantor space Σ
Z
n
together with the shift describes all logically possible
orbits with respect to the given partition and W describes the symbolic orbits that actually
occur in the given CA.The shift operator corresponds to an application of the global map
of the CA.The finite factors of W provide information about possible finite traces of an
orbit when filtered through the given partition.Whole orbits,again filtered through the
partition,can be described by ω-words.To tackle the classification of the CA in terms of
W it was suggested by Delvenne et al.,see [15],to refer to the CA as decidable if there
it is decidable whether W has non-empty intersection with a ω-regular language.Alas,
decidability in this sense is very difficult,its complexity being Σ
1
1
-complete and thus outside
of the arithmetical hierarchy.Likewise it is suggested to call a CA universal if the problem
of deciding whether the cover of W,the collection of all finite factors,is Σ
1
-complete,in
analogy to Davis-universality.
5 Computational Equivalence
In recent work,Wolfram suggests a so-called Principle of Computational Equivalence,or
PCE for short,see [78,p.717].PCE states that most computational processes come in only
two flavors:they are either of a very simple kind and avoid undecidability,or they represent a
15
universal computation and are therefore no less complicated than the Halting Problem.Thus,
Wolfram proposes a zero-one law:almost all computational systems,and thus in particular
all CA,are either as complicated as a universal Turing machine or are computationally
simple.As evidence for PCE Wolframadduces a very large collection of simulations of various
effective dynamical systems such as Turing machines,register machines,tag systems,rewrite
systems,combinators,and cellular automata.It is pointed out in chapter 3 of [78],that in
all these classes of systems there are surprisingly small examples that exhibit exceedingly
complicated behavior–and presumably are capable of universal computation.Thus it is
conceivable that universality is a rather common property,a property that is indeed shared
by all systems that are not obviously simple.Of course,it is often very difficult to give a
complete proof of the computational universality of a natural system,as opposed to carefully
constructed one,so it is not entirely clear how many of Wolfram’s examples are in fact
universal.As a case in point consider the universality proof of Conway’s Game of Life,or
the argument for elementary CA 110.If Wolfram’s PCE can be formally established in some
formit stands to reason that it will apply to all effective dynamical systems and in particular
to CA.Hence,classifications of CA would be rather straightforward:at the top there would
be the class of universal CA,directly preceded by a class similar to the third Culik-Yu class,
plus a variety of subclasses along the lines of the lower Li-Packard classes.
The corresponding problem in classical computability theory was first considered in the
1930’s by Post and is now known as Post’s Problem:is there a semi-decidable set that fails
to be decidable,yet is not as complicated as the Halting Set?In terms of Turing degrees the
problem thus is to construct a semi-decidable set A such that ∅ <
T
A <
T

￿
,or to rule out
the existence of any such set,see [52,58,34] for background on Turing degrees in general
and semi-decidable degrees in particular.Post’s Problem resisted all attempts at resolution
until Friedberg and Muchnik independently and almost simultaneously discovered a way to
construct a set of intermediate complexity,see [19,46].The construction is based on the idea
of a so-called priority argument and is significantly more complicated than any construction
of semi-decidable sets previously known [58].Indeed,priority arguments have since become
the hallmark of computability theory and have even engendered some criticism as being so
very technical that,occasionally,the proofs seemto attract more attention than the theorems
being established,see [72].Be that as it may,it is striking how much more artificial and ad
hoc intermediate sets are,as compared to natural examples such as the theory of the reals
(decidable) or of Diophantine equations (equivalent to the Halting Problem).No natural
examples of intermediate semi-decidable sets are known to date.
Nonetheless,given an intermediate set A one can construct a one-dimensional CA whose
Reachability Problem has the same degree as A.This suggests a degree-based classification:
given any computably enumerable degree d,define the class C
d
to consist of all CA whose
Reachability Problem has degree exactly d,see [63,65].The degree classification is non-
trivial in the sense that every class is non-empty.Note that the first three Culik-Yu classes are
all contained in C
0
whereas C
0
￿
comprises all computationally universal CA.Unsurprisingly,
it is again undecidable whether a CA belongs to any particular class.At the bottom end
of the hierarchy it is Σ
3
-complete to determine membership in C
0
;at the top end it is
16
Σ
4
-complete to determine membership in C
0
￿
.Thus,it is easier to determine decidability
than universality.In general,deciding membership in C
d
is Σ
d
3
-complete for any semi-
decidable degree d.Similar results hold for the analogous cumulative classes C
≤d
=
￿
e≤d
C
e
.
Unlike the Culik-Yu classification,the structure of the degree classification between C
0
and C
0
￿
is exceedingly complicated.For example,the proof of the Friedberg-Muchnik theo-
rem shows that there are incomparable semi-decidable degrees d
1
and d
2
.Hence there is are
CA whose orbits are undecidable but not as complicated as the Halting Problem.Indeed,
complete knowledge of the orbits of one of the two CA will not help in deciding membership
in the orbits of the other.Another surprising result in the theory of computably enumer-
able degrees is Sack’s Density Theorem,see [58]:between any two computably enumerable
degrees d
1
< d
2
there lies a third:d
1
< d < d
2
.Thus,between any two CA of strictly
increasing complexity there is an infinite and dense hierarchy of other CA.The computably
enumerable degrees form a semi-lattice,so it is natural to try to understand the complexity
of the structure by analyzing its first order theory.It is well-known that the Σ
1
-theory of
this semi-lattice is decidable.However,the reason for this decidability result lies in the fact
that any countable partial order can be embedded into the semi-lattice so that the relative
computational strength of cellular automata is indeed arbitrarily complicated.On the other
hand,the full theory of the semi-lattice of semi-decidable degrees is known to be highly un-
decidable,see [23];its degree is ∅
(ω)
.One might hope that restriction to reversible CA would
simplify the situation somewhat.Somewhat surprisingly it turns out that each class C
d
al-
ready contains an irreversible CA,see [66],so the same difficulties arise in the classification
of reversible CA as in the classification of ordinary CA.
While reachability is arguably the most basic relation between configurations,similar
difficulties also arise with confluence.As a matter of fact,one can construct a CA whose
Reachability Problemhas complexity some arbitrarily chosen computably enumerable degree
d
1
while the Confluence Problem for the same CA has degree d
2
,another arbitrarily chosen
computably enumerable degree.Thus,a classification according to reachability is entirely
independent of a confluence-based classification.
How do these results relate to PCE?Wolfram would not accept any of the intermediate
classes of CAas a counterexample to PCE.The argument is that though intermediate degrees
exist,their construction is critically linked to universal computation.While the universal
computation is invisible when only the output of the system is observed,the associated
computational process includes the whole computation and is thus universal.As a case
in point,consider the standard Friedberg-Muchnik construction for an intermediate semi-
decidable set A.The construction actually builds two semi-decidable sets A and B that are
mutually incomparable with respect to Turing reducibility.Only A is output and B remains
hidden.However,even ignoring all the intricate technical details of the whole construction,if
we consider both A and B as output then the computation is indeed universal:the disjoint
union A ⊕ B is Σ
1
-complete,see [57].It remains to be seen if similar arguments can be
put forth in connection with priority-free constructions of intermediate degrees or if natural
examples of intermediate sets can be found.At any rate,by considering only the reachability
relation instead of a whole segment of the orbit we also achieve information-hiding,much as
17
Figure 3:A pseudo-random pattern generated by elementary CA 30.
in the classical Friedberg-Muchnik construction.
6 Conclusion
Classification schemes of cellular automata based on the long-term evolution of pattern are
typically undecidable,even if the property in question can be expressed in a fairly week
system.While it is easy to construct examples of CA in particular classes it is usually very
difficult to establish the position of a given CA in a particular classification.An excellent
example for the difficulty of analyzing a given CA is Cook’s proof of the universality of
elementary CA number 110 whose local rule is given by ρ(x,y,z) = (
x∧y ∧z) ⊕y ⊕z where
⊕ denotes exclusive or,see [9,17].The argument shows that cyclic tag systems,which
are known to be complete,can be simulated by elementary CA 110 provided one allows an
almost periodic background.Recent work by Neary and Woods has shown that the whole
simulation can effected with only a polynomial slow-down,see [47,48].This result suggests
that the appropriate setting for classifications is the space of almost periodic configurations
rather than finite ones.
In light of the successful analysis of elementary CA 110 it is tempting to ask about the
18
Figure 4:Interacting signals in Mazoyer’s optimal solution to the firing squad problem.
classification of elementary CA 30.Figure 3 shows a segment of the orbit of a one-point seed
configuration under rule 30.It is striking how chaotic and apparently random the image is.
As a matter of fact,rule 30 has been used for many years as the default random number
generator in the commercial computer algebra systemMathematica,see [77].The underlying
local map is simply ρ(x,y,z) = x ⊕(y ∨ z).Alas,there appear to be no structures in the
evolution of configurations under rule 30 such as “moving particles” that might be exploited
in a universality argument along the lines of rule 110.On the other hand,it is unclear
how a decision procedure for reachability could be developed.This makes it tempting to
conjecture that rule 30 in C
ap
might be a member of one of the intermediate classes C
d
,
though at present there seems to be no way to either establish or refute this conjecture.
While undecidability results rule out the possibility of automatic classification mecha-
nisms there is still ample room for the development of sufficient criteria for membership in
certain classes,see [79,1,71].For example,a proof of computational universality in a CA
that has not been artificially constructed to simulate some other device often rests on the
presence of “particles” or “gliders” that can be used to send “signals” between spatially
separated locations.Moreover,one has to be able to process these signals much in the way
of Boolean logic gates,to store state and so forth.A good example for complicate inter-
actions between signals are the various solutions to the firing squad problem,albeit not in
the context of simulating arbitrary computations;see figure 4,[39].A more recent example
is Cook’s ingenious method of using natural gliders in elementary CA 110 to implement a
cyclic tag system in C
ap
,thereby establishing computational universality of rule 110,see [9].
Notable here is the fact that the automaton was fixed from the start and the the appropriate
19
coding mechanisms had to be developed in a very constrained environment.This is in stark
contrast to other hardness arguments where the CA is carefully constructed to display the
desired behavior.Careful visual inspection of rule 110 orbits was a crucial component in
Cook’s proof,it is difficult to imagine that the result could have been established in a purely
combinatorial or algebraic fashion.One can envision an interactive software system that
helps to tackle some algorithmically unsolvable classification problems in special cases,much
as Baumslag’s Magnus project in group theory,see [5].
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24