The **Turing machine** is an abstract model of computer execution and storage introduced in 1936 by Alan Turing to give a mathematically precise definition of algorithm or 'mechanical procedure'.
As such it is still widely used in theoretical computer science, especially in complexity theory and the theory of computation.
The thesis that states that Turing machines indeed capture the informal notion of effective or mechanical method in logic and mathematics is known as the Church-Turing thesis.

Turing machines shouldn't be confused with the Turing test, Turing's attempt to capture the notion of artificial intelligence.

A Turing machine that is able to simulate any other Turing machine is called a **universal Turing machine**.

Table of contents |

2 Example 3 Universal Turing machines 4 A physical Turing machine 5 References and external links 6 See also |

## Definition

Briefly, a Turing machine is a pushdown automaton made more powerful by relaxing the last-in-first-out requirement of its stack. (Interestingly, a seemingly minor relaxation enables the Turing machine to perform such a wide variety of computations that it can serve as a model for the computational capabilities of all modern computer software.)

More precisely, a Turing machine consists of:

- A
*tape*which is divided into cells, one next to the other. Each cell contains a symbol from some finite alphabet. The alphabet contains a special*blank*symbol (here written as '0') and one or more other symbols. The tape is assumed to be arbitrarily extendible to the left and to the right, i.e., the Turing machine is always supplied with as much tape as it needs for its computation. Cells that have not been written to before are assumed to be filled with the blank symbol. - A
*head*that can read and write symbols on the tape and move left and right. - A
*state register*that stores the state of the Turing machine. The number of different states is always finite and there is one special*start state*with which the state register is initialized. - An
*action table*that tells the machine what symbol to write, how to move the head ('L' for one step left, and 'R' for one step right) and what its new state will be, given the symbol it has just read on the tape and the state it is currently in. If there is no entry in the table for the current combination of symbol and state then the machine will halt.

## Example

The following Turing machine has an alphabet {'0', '1'}, with 0 being the blank symbol. It expects a series of 1's on the tape, with the head initially on the leftmost 1, and doubles the 1's with a 0 in between, i.e., "111" becomes "1110111". The set of states is {s1, s2, s3, s4, s5} and the start state is s1. The action table is as follows.

Old Read Wr. New Old Read Wr. New St. Sym. Sym. Mv. St. St. Sym. Sym. Mv. St. - - - - - - - - - - - - - - - - - - - - - - - - s1 1 -> 0 R s2 s4 1 -> 1 L s4 s2 1 -> 1 R s2 s4 0 -> 0 L s5 s2 0 -> 0 R s3 s5 1 -> 1 L s5 s3 0 -> 1 L s4 s5 0 -> 1 R s1 s3 1 -> 1 R s3A computation of this Turing machine might for example be: (the position of the head is indicated by displaying the cell in bold face)

Step State Tape Step State Tape - - - - - - - - - - - - - - - - - 1 s1The behavior of this machine can be described as a loop: it starts out in s1, replaces the first 1 with a 0, then uses s2 to move to the right, skipping over 1's and the first 0 encountered. S3 then skips over the next sequence of 1's (initially there are none) and replaces the first 0 it finds with a 1. S4 moves back to the left, skipping over 1's until it finds a 0 and switches to s5. s5 then moves to the left, skipping over 1's until it finds the 0 that was originally written by s1. It replaces that 0 with a 1, moves one position to the right and enters s1 again for another round of the loop. This continues until s1 finds a 0 (this is the 0 right in the middle between the two strings of 1's) at which time the machine halts.11 9 s2 1001 2 s2 0110 s3 10013 s2 01011 s3 100104 s3 010012 s4 10011 5 s4 0101 13 s4 10011 6 s5 0101 14 s5 10011 7 s50101 15 s1 11011 8 s1 1101 -- halt --

## Universal Turing machines

Every Turing machine computes a certain fixed partial function from the input strings over its alphabet.
In that sense it behaves like a computer with a fixed program.
However, as Alan Turing already described, we can encode the action table of any Turing machine in a string. Thus we might try to construct a Turing machine that expects on its tape a string describing an action table followed by a string describing the input tape, and then computes the tape that the encoded Turing machine would have computed.
As Turing showed, such a Turing machine is indeed possible and since it is able to simulate any other Turing machine it is called a *universal Turing machine*.

With this encoding of action tables as strings, it becomes possible in principle for Turing machines to answer questions about the behavior of other Turing machines. Most of these questions, however, are undecidable. For instance, the problem of determining whether a particular Turing machine will halt on a particular input, or on all inputs, known as the Halting problem, was already shown to be undecidable in Turing's original paper. Rice's theorem shows that any nontrivial question about the behavior or output of a Turing machine is undecidable.

If we broaden the definition to include any Turing machine that simulates some Turing-complete computational model, not just Turing machines that directly simulate other Turing machines, a universal Turing machine can be fairly simple, using just a few states and a few symbols. For example, only 2 states are needed, since a 2×18 (meaning 2 states, 18 symbols) universal Turing machine is known. A complete list of the smallest known universal Turing machines is: 2×18, 3×10, 4×6, 5×5, 7×4, 10×3, 22×2. These simulate a computational model called tag systems.

A universal Turing machine is Turing-complete.
It can calculate any recursive function, decide any recursive language, and accept any recursively enumerable language.
According to the Church-Turing thesis, the problems solvable by a universal Turing machine are exactly those problems solvable by an *algorithm* or an *effective method of computation*, for any reasonable definition of those terms.

## A physical Turing machine

It is also possible to build a Turing Machine on a purely mechanical basis. The mathematician Karl Scherer has indeed built such a machine in 1986 using metal and plastic construction sets, and some wood. The 1.5 meter high machine uses the pulling of strings to read, move and write the data (which is represented using ball bearingss).

The machine is now exhibited in the entrance of the Department of Computer Science of the University of Heidelberg, Germany.

## References and external links

- Visual Turing, a Turing machine interactive simulator/IDE (free software for Windows). http://www.cheransoft.com/vturing/
- Suzanne Brittons Turing Machine Simulator (java applet). http://www.igs.net/~tril/tm/
- Turing, A.,
*On Computable Numbers, With an Application to the Entscheidungsproblem*, Proceedings of the London Mathematical Society, Series 2, Volume 42, 1936; reprinted in M. David (ed.),*The Undecidable*, Hewlett, NY: Raven Press, 1965; online: http://www.abelard.org/turpap2/tp2-ie.asp - Boolos, G. and Jeffrey, R.,
*Computability and Logic*, 2nd ed., Cambridge: Cambridge University Press, 1980. - Rogozhin, Yurii, "A Universal Turing Machine with 22 States and 2 Symbols",
*Romanian Journal Of Information Science and Technology*, 1(3), 259-265, 1998. http://www.imt.ro/Romjist/Volum1/Vol1_3/turing.htm (surveys known results about small universal Turing machines) - C++ Simulator of a Nondeterministic and Deterministic Turing Machine (free software). http://sourceforge.net/projects/turing-machine/

## See also

- Langton's ant, a simple two-dimensional Turing machine
- Church's Conjecture, effective Turing machine can perform any computation in any language