May 20, 2022

# Computability Part 3: Tag Systems

In the previous post we talked about universal Turing machines and looked at some very small machines that are still capable of computing anything that can be computed (the Turing-completeness property). In this post, we'll look at another model for computation: tag systems.

A tag system operates on a string of symbols by reading the symbol from the head of the string, deleting a constant number of symbols from the head of the string, and appending one or more symbols to the tail of the string based on the symbol read from the head.

Formally:

A tag system is a triplet $$\langle m, A, P \rangle$$.

• $$m$$ is a positive integer, called the deletion number, which specifies how many symbols are deleted from the head during each iteration.
• $$A$$ is a finite alphabet of symbols, including a special halting symbol.
• $$P$$ is a set of production rules which map each symbol in $$A$$ to a string of symbols or words from $$A$$ (to be appended to the end of the string).

Tag systems were specified by Emil Leon Post in 1943, 7 years after Turing Machines. We usually refer to tag systems as m-tag systems where $$m$$ is the deletion number from the definition above.

At each step, $$x$$ is read from the head of the string, $$m$$ symbols are deleted, and $$P(x)$$ is appended to the end of the string. The tag system halts when $$x$$ is the halting symbol.

An alternative definition that doesn't require a halting symbol considers as halting all words that are smaller than $$m$$. In this case, the tag system halts when the string shrinks sufficiently. Yet another alternative considers as halting the empty string. In this case, the tag system halts when the string becomes empty.

Let's look at a Python implementation for a tag system:

def tag_system(m, productions, string):
# Repeat until the string is empty or we see the halting symbol
while string and string in productions:
string = string[m:] + productions[string]

yield string


As an example, let's take the tag system with $$m = 2, A = \langle a, b, H \rangle$$, and the production rules

Symbol Word
a aab
b H

Starting with the string aa, the steps are:

aa              // Erase 2 symbols from head, a -> aab
aab           // Erase 2 symbols from head, a -> aab
baab        // Erase 2 symbols from head, b -> H
abH       // Erase 2 symbols from head, a -> aab
Haab    // Halt


Using our tag_system() function implemented above:

productions = {
'a': 'aab',
'b': 'H',
}

string = 'aa'

print(string)
for string in tag_system(2, productions, string):
print(string)


Tag systems are simple, even simpler than Turing machines. Remember we defined a Turing machine as a 7-tuple while tag systems are represented by triplets. Turing machines have states, and depending on the state, a machine takes different actions. Tag systems technically have a single state: when a symbol is read from the head of the string, the same thing will always happen: $$m$$ symbols are deleted from the head and the corresponding production rule determines what word to append to the tail of the string. Even so, tag systems are Turing-complete.

## Turing completeness

For $$m \gt 1$$, m-tag systems are Turing complete. For any Turing machine, there is an m-tag system that can emulate that Turing machine. John Cocke and Marvin Minsky showed in 1964 how a 2-tag system can emulate a universal Turing machine1. That means that such a super simple system is also capable of universal computation!

But it gets even simpler.

## Cyclic tag systems

A cyclic tag system is a modification of tag systems where:

• $$m = 1$$: only one symbol is deleted from the head of the string.
• The alphabet consists of only 0 and 1.
• Instead of production rules, we use a finite list of words (on the alphabet consisting of only 0 and 1) called productions.

Instead of production rules, we cycle through the list of productions. We start from the head of the list of productions. At each step, if the symbol at the head of the string is 1, we append the production to the end of the string. If the symbol at the head of the string is 0, we don't append anything. We then move to the next production in the list for the next step. Once we exhaust the list of productions, we loop around to the head (this inspired the cyclic name).

Here is a Python implementation for a cyclic tag system:

def cyclic_tag_system(productions, string):
# Keeps track of current production
i = 0

# Repeat until the string is empty
while string:
string = string[1:] + (productions[i] if string == '1' else '')

# Update current production
i = i + 1
if i == len(productions):
i = 0

yield string


For example, we will use the production rules 11, 01, 00. With an initial string 1, the steps are:

1               // Append production 11
11             // Append production 01
101           // Append production 00
0100         // Current production 11 (won't append since head is 0)
100         // Append production 01
0001       // Current production 00 (won't append since head is 0)
001       // Current production 11 (won't append since head is 0)
01       // Current production 01 (won't append since head is 0)
1       // Append production 00
00     // Current production 11 (won't append since head is 0)
0     // Current production 01 (won't append since head is 0)
// Halts


Using our Python implementation:

productions = ['11', '01', '00']

string = '1'

print(string)
for string in cyclic_tag_system(productions, string):
print(string)


Cyclic tag systems are simpler than tag systems since $$m$$ is fixed to 1, the alphabet is fixed to 0 and 1, and productions are a represented as a cyclic list rather than a map of symbols to words. Even so, a cyclic tag system can emulate any m-tag system.

## Emulating tag systems with cyclic tag systems

An m-tag system with $$n$$ symbols $$\lbrace a_1, a_2, ... a_n \rbrace$$ and their corresponding production rules $$\lbrace P_1, P_2, ... P_n \rbrace$$ can be translated to a cyclic tag system with $$m * n$$ productions where the first $$n$$ productions $$\lbrace P'_1, P'_2, ... P'_n \rbrace$$ are encodings of their respective $$P$$-productions in the m-tag system and the rest are empty strings.

Productions in the m-tag system are words over the alphabet $$A$$. We encode each symobl in $$A$$ as a binary string of length $$n$$, with a 1 in the $$k$$-th position for $$a_k$$. For example, for $$n = 3$$ and the alphabet $$A = \lbrace a_1, a_2, a_3 \rbrace$$, we encode $$a_1$$ as 100, $$a_2$$ as 010, $$a_3$$ as 001. Since a production $$P_k$$ is a sequence of symbols, we can similarly translate it into an encoded representation $$P'_k$$ using symbols 0 and 1.

Our first example was the 2-tag system with the alphabet $$A = \langle a, b, H \rangle$$, and the production rules

Symbol Word
a aab
b H
H H

Here we added the production rule H -> H for completeness, so we have exactly $$n$$ production rules.

Translating this into a cyclic tag system, $$a, b, H$$ become 100, 010, and 001 respectively. The production rules translate as:

a -> aab becomes 100100010

b -> H becomes 001

H -> H becomes 001

The full list of production for the cyclic tag system is 100100010, 001, 001, -, -, - where - is the empty string.

The initial string aa becomes 100100, so our emulation is:

100100                      // * Emulated production rule a -> aab
00100100100010             // P = 001 (but head is 0)
0100100100010             // P = 001 (but head is 0)
100100100010             // P = empty string
00100100010             // P = empty string, head is 0
0100100010             // P = empty string, head is 0
100100010             // * Emulated production rule a -> aab
00100010100100010    // P = 001 (but head is 0)
0100010100100010    // P = 001 (but head is 0)
100010100100010    // P = empty string
00010100100010    // P = empty string, head is 0
0010100100010    // P = empty string, head is 0
010100100010    // P = 100100010 (but is 0)
10100100010    // * Emulated production rule b -> H
0100100010001 // P = 001 (but head is 0)
100100010001 // P = empty string
...


Using our Python implementation:

productions = ['100100010', '001', '001', '', '', '']

string = '100100'

print(string)
for string in cyclic_tag_system(productions, string):
print(string)


Note in this case the cyclic tag system won't halt when the emulated m-tag system halts, since that would be an emulated halt. But we can stop it by checking whether the first 3 symbols represent the encoding of H. We do this every sixth step, since we have a 2-tag system with 3 symbols, which means we emulate 1 step of the tag system with 6 steps of the cyclic tag system.

productions = ['100100010', '001', '001', '', '', '']

i, string = 0, '100100'

print(string)
for string in cyclic_tag_system(productions, string):
print(string)

i = (i + 1) % 6

# Break if halting symbol is at the head of the string
if i == 0 and string[:3] == '001':
break


Or, an updated example that prints every sixth step and translates from the cyclic tag system encoding to the original symbols:

productions = ['100100010', '001', '001', '', '', '']

symbols = {
'100': 'a',
'010': 'b',
'001': 'H',
}

def translate(s):
return ''.join([symbols[s[i:i + 3]] for i in range(0, len(s), 3)])

i, string = 0, '100100'

print(f'{string} ({translate(string)})')
for string in cyclic_tag_system(productions, string):
i = (i + 1) % 6
if i == 0:
print(f'{string} ({translate(string)})')
if string[:3] == '001':
break


Running this code should be the emulated equivalent of our first example in this post.

Since m-tag systems (with $$m \gt 1$$) are Turing-complete and cyclic tag systems can emulate any m-tag system, it follows that cyclic tag systems are also Turing complete. We can compute anything that is computable with the alphabet 0, 1, and a list of words over this alphabet!

In the next post, we will continue our exploration of simple systems capable of universal computation with cellular automata.