튜링 기계

수학노트
둘러보기로 가기 검색하러 가기

example

  • http://www.soc.napier.ac.uk/~andrew/tm/ryo.htm
  • Unary increment - this function takes a string of 1's and adds another: 111→1111
  • Unary decrement - this function takes a string of 1's and removes one: 111→11
  • Binary double - this function doubles a binary string: 111→1110
  • Binary increment - add one to a binary number: 101→110
  • Unary double - double the length of a unary string 111→111111
  • Palindrome cheker
  • Binary addition


unary double

  • x,1,a,R,y
  • x,b,1,R,x
  • x, , ,R,HALT
  • y,1,1,R,y
  • y,b,b,R,y
  • y, ,b,L,z
  • z,1,1,L,z
  • z,b,b,L,z
  • z,a,1,R,x

computational resource


노트

  • A probabilistic Turing machine (PTM) is a Turing machine (TM) modified for executing a randomized computation.[1]
  • Essentially, a Turing machine consists of a tape with instructions written on it and the device that can read up and down the tape.[2]
  • Neural Turing Machines have taken all of the functions of the basic Turing machine and found smooth analogues.[2]
  • All a Turing machine does is read and write from a piece of tape.[3]
  • A much better way to write a Turing machine is with a diagram like above.[3]
  • We define the symbols a Turing machine works with.[3]
  • In other words, any algorithm ever can be built on a Turing machine.[3]
  • A Turing machine is a hypothetical machine thought up by Alan Turing.[4]
  • Conceptually, the Turing machine consists of an infinite tape and a tape head.[4]
  • We can simulate Turing machines (with finite tape) on our computers, but they are notoriously cumbersome to program.[4]
  • The machines discussed in this article are not the same as the hypothetical Turing machine as Alan Turing defined it.[4]
  • Section 2 provides some basic notations about Turing machine, Rubel’s EAC model, and the uEAC.[5]
  • A Turing machine can be seen as a state machine; at each moment the machine is in one of a finite number of states.[5]
  • Consider an example of single tape Turing machine with three states and is the initial state.[5]
  • In Figure 1, states of the Turing machine are represented by circles, with the concentric circle being the initial state .[5]
  • We will begin by constructing a Turing machine for the language L = {anbncn}.[6]
  • We will be adding a lot of states to create a Turing machine for L = {anbncn}.[6]
  • The value in the first box represents the current value under the head of the Turing machine.[6]
  • Add the transitions in your screen below to your Turing machine.[6]
  • It was late one night when I was starting my problem set on writing a turing machine to compute some operation.[7]
  • *position* position)) ;; The following are the procedures that implement the Turing machine.[7]
  • A turing machine consists of a tape of infinite length on which read and writes operation can be performed.[8]
  • Below, you can see the initial configuration of a Turing machine on the input 101001: .[9]
  • In 1936, Turing defined Turing machines as a universal model of computation on natural numbers.[9]
  • This means that all computable functions you can imagine can be computed by a Turing machine.[9]
  • He considered Turing machines with 2 symbols, and defined the functions S(n,2) and Sigma(n,2).[9]
  • A Turing machine consists of a line of cells known as the "tape", together with a single active cell, known as the "head".[10]
  • Any particular Turing machine is defined by a rule which specifies what the head should do at each step.[10]
  • Not every Turing machine has this property; many can only behave in very simple ways.[10]
  • A universal Turing machine has the property that it can emulate any other Turing machine---or indeed any computer or software system.[10]
  • Then run the instructions two more times and see if you can figure out what this Turing machine does.[11]
  • So this Turing machine is designed to flip bits.[11]
  • The instructions for this Turing machine only had one state, but more complex Turing machines can be built using multiple states.[11]
  • In this step, we’re going to have a look at Turing machines, which were hypothetical computers invented by Alan Turing in 1936.[11]
  • next → ← prev Turing Machine Turing machine was invented in 1936 by Alan Turing.[12]
  • There are various features of the Turing machine: It has an external memory which remembers arbitrary long sequence of input.[12]
  • Thus a common set of alphabets can be used for the Turing machine.[12]
  • The main advantage of the Turing machine is we have a tape head which can be moved forward or backward, and the input tape can be scanned.[12]
  • In its simplest form, a Turing machine is composed of a "tape", a ribbon of paper of indefinite length.[13]
  • The Turing machine is said to be in a certain "state".[13]
  • We then say a Turing machine is emulating another one (the one on the tape).[13]
  • the same computational capabilities than a Turing machine is to see if it can emulate a Turing machine.[13]
  • A Turing machine is an abstract computational device that can be in one of a finite set of possible states.[14]
  • The computational complexity of an algorithm is measured by the number of steps required by a Turing machine to run through the algorithm.[14]
  • A Turing machine as defined above is a deterministic machine.[14]
  • For each sequence of choices, the sequence of transitions corresponds to a sequence of steps executed by a deterministic Turing machine.[14]
  • To show there were algorithms that Turing machines would run indefinitely and inconclusively was a way of showing Hilbert was mistaken.[15]
  • , Turing noted that people are really Turing machines.[15]
  • A Turing machine is a very simple machine, but, logically speaking, has all the power of any digital computer.[16]
  • A Turing machine processes an infinite tape.[16]
  • At any time, the Turing machine has a read/write head positioned at some square on the tape.[16]
  • The very simplicity of a Turing machine makes it a challenge to program one to perform a specific computation.[16]
  • As shown in the animation above, a Turing machine consists of a tape that is initialized with a string of symbols.[17]
  • The table below describes a simple Turing machine that takes a string of 1 1 1’s as input and doubles it.[17]
  • A state register stores the state of the Turing machine.[18]
  • The heart of the turing machine is the read-write head.[19]
  • As Turing claimed, any process that can be naturally called an effective procedure is realized by a Turing machine.[20]
  • If the Turing machine halts for all inputs, then the function computed is defined for all arguments and we call it total computable.[20]
  • It is possible to give an effective (computable) one-to-one pairing between natural numbers and Turing machines.[20]
  • The last reference contains an excellent discussion of Turing machines, their computations, and related machines.[20]
  • Turing completeness is the ability for a system of instructions to simulate a Turing machine.[21]
  • The Turing machine mathematically models a machine that mechanically operates on a tape.[21]
  • A state register that stores the state of the Turing machine, one of finitely many.[21]
  • that stores the state of the Turing machine, one of finitely many.[21]
  • There are just six types of fundamental operation that a Turing machine performs in the course of a computation.[22]
  • It is a remarkable fact that none of these computers can outdo a Turing machine.[22]
  • Despite the Turing machine's austere simplicity, it is capable of computing anything that any computer on the market can compute.[22]
  • Indeed, since it is an abstract or notional machine, a Turing machine can compute more than any physical computer.[22]
  • They were first named ‘Turing machines’ by Alonzo Church in a review of Turing’s paper (Church 1937).[23]
  • Turing introduced Turing machines in the context of research into the foundations of mathematics.[23]
  • Another typical format to represent Turing machines and which was also used by Turing is the transition table.[23]
  • Thus, Post introduced a modified version of the Turing machine.[23]
  • A template for specifying a 3-state, 2-color Turing machine is shown above using a form of notation due to Wolfram (2002).[24]
  • An example 3-state, 2-color Turing machine is illustrated above (Wolfram 2002, p. 78).[24]
  • Determining whether a Turing machine will ever halt for a given input and set of rules is called the halting problem.[24]
  • For an -state binary Turing machine, the number of 1s written for a busy beaver is denoted .[24]
  • In section two, let's learn about LEDs, GPIO pins, resistors, and python, before embarking on building our Turing machine![25]

소스

  1. ↑ Probabilistic Turing machine
  2. ↑ 2.0 2.1 Neural Turing Machines: Perils and Promise
  3. ↑ 3.0 3.1 3.2 3.3 What’s a Turing Machine? (And Why Does It Matter?)
  4. ↑ 4.0 4.1 4.2 4.3 We are all Turing machines.
  5. ↑ 5.0 5.1 5.2 5.3 Simulation of Turing Machine with uEAC-Computable Functions
  6. ↑ 6.0 6.1 6.2 6.3 Turing Machine
  7. ↑ 7.0 7.1 Universal Turing Machine
  8. ↑ Turing Machine in TOC
  9. ↑ 9.0 9.1 9.2 9.3 Turing machines
  10. ↑ 10.0 10.1 10.2 10.3 Wolfram 2,3 Turing Machine Research Prize : What is a Turing Machine?
  11. ↑ 11.0 11.1 11.2 11.3 Turing machines
  12. ↑ 12.0 12.1 12.2 12.3 Automata Turing Machine
  13. ↑ 13.0 13.1 13.2 13.3 Turing machine
  14. ↑ 14.0 14.1 14.2 14.3 Turing Machines - an overview
  15. ↑ 15.0 15.1 How Alan Turing found machine thinking in the human mind
  16. ↑ 16.0 16.1 16.2 16.3 AMS :: Feature Column from the AMS
  17. ↑ 17.0 17.1 Brilliant Math & Science Wiki
  18. ↑ Turing Machine Introduction
  19. ↑ A Turing Machine Overview
  20. ↑ 20.0 20.1 20.2 20.3 Turing machine
  21. ↑ 21.0 21.1 21.2 21.3 Turing machine
  22. ↑ 22.0 22.1 22.2 22.3 AlanTuring.net What is a Turing machine?
  23. ↑ 23.0 23.1 23.2 23.3 Turing Machines (Stanford Encyclopedia of Philosophy)
  24. ↑ 24.0 24.1 24.2 24.3 Turing Machine -- from Wolfram MathWorld
  25. ↑ Department of Computer Science and Technology – Raspberry Pi: Introduction: What is a Turing machine?

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Spacy 패턴 목록

  • [{'LOWER': 'turing'}, {'LEMMA': 'machine'}]
  • [{'LOWER': 'deterministic'}, {'LOWER': 'turing'}, {'LEMMA': 'machine'}]
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