# 3515ICT Theory of Computation Turing Machines

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1 Griffith University 3515ICT Theory of Computation Turing Machines (Based loosely on slides by Harald Søndergaard of The University of Melbourne) 9-0

2 Overview Turing machines: a general model of computation (3.1) Variants of Turing machines (3.2) Algorithms and the Church-Turing thesis (3.3) Decidable problems (4.1) The Halting Problem and other undecidable problems (4.2) Undecidable problems from language theory (5.1) The Post Correspondence Problem and its applications (5.2) Rice s Theorem (Problem 5.28) 9-1

3 Alan Turing Born London, Studied quantum mechanics, probablity, logic at Cambridge. Published landmark paper, 1936: Proposed most widely accepted formal model for algorithmic computation. Proved the existence of computationally unsolvable problems. Proved the existence of universal machines. Did PhD in logic, algebra, number theory at Princeton, Worked on UK cryptography program, Contributed to design and construction of first UK digital computers,

4 Alan Turing (cont.) Wrote numerical, learning and AI software for first computers, Proposed Turing Test for artificial intelligence, Studied mathematics of biological growth, elected FRS, Arrested for homosexuality, 1952; died (suicide), See Alan Turing: The Enigma, Andrew Hodge (Vintage, 1992). See 9-3

5 Turing Machines So what was Turing s computational model? A Turing machine (TM) is a finite state machine with an infinite tape from which it reads its input and on which it records its computations. state control tape head b a a infinite tape The machine has both accept and reject states. Comparing a TM and a DFA or DPDA: A TM may both read from and write to its input tape. A TM may move both left and right over its working storage. A TM halts immediately when it reaches an accept or reject state. 9-4

6 Turing Machines Formally A Turing machine is a 7-tuple M = (Q, Σ, Γ, δ, q 0, q a, q r ) where 1. Q is a finite set of states, 2. Γ is a finite tape alphabet, which includes the blank character,, 3. Σ Γ \ {} is the input alphabet, 4. δ : Q Γ Q Γ {L, R} is the transition function, 5. q 0 Q is the initial state, 6. q a Q is the accept state, and 7. q r Q (q r q a ) is the reject state. 9-5

7 The Transition Function A transition δ(q i, x) = (q j, y, d) depends on: 1. the current state q i, and 2. the current symbol x under the tape head. It consists of three actions: 1. change state to q j, 2. over-write tape symbol x by y, and 3. move the tape head in direction d (L or R). If the tape head is on the leftmost symbol, moving left has no effect. We have a graphical notation for TMs similar to that for finite automata and PDAs. On an arrow from q i to q j we write: x d if δ(q i, x) = (q j, x, d), and x y, d if δ(q i, x) = (q j, y, d), y x. 9-6

8 Example 1 b R a R q 0 q 1 b R R R a R q a This machine recognises the regular language (a + b) aa(a + b) = (b + ab) aa(a + b). This machine is not very interesting it always moves right and it never writes to its tape! (Every regular language can be recognised by such a machine. Why?) We can omit q r from TM diagrams with the assumption that transitions that are not specified go to q r. q r 9-7

9 Turing Machine Configurations We write uqv for this configuration: u v blank q On input aba, the example machine goes through these configurations: εq 0 aba (or just q 0 aba) aq 1 ba abq 0 a abaq 1 abaq r We read the relation as yields. 9-8

10 Computations Formally For all q i, q j Q, a, b, c Γ, and u, v Γ, we have uq i bv ucq j v if δ(q i, b) = (q j, c, R) q i bv q j cv if δ(q i, b) = (q j, c, L) uaq i bv uq j acv if δ(q i, b) = (q j, c, L) The start configuration of M on input w is q 0 w. M accepts w iff there is a sequence of configurations C 0, C 1, C 2,...,C k such that 1. C 0 is the start configuration q 0 w, 2. C i C i+1 for all i {0,...,k 1}, and 3. The state of C k is q a. 9-9

11 Turing Machines and Languages The set of strings accepted by a Turing machine M is the language recognised by M, L(M). A language A is Turing-recognisable or computably enumerable (c.e.) or recursively enumerable (r.e.) (or semi-decidable) iff A = L(M) for some Turing machine M. Three behaviours are possible for M on input w: M may accept w, reject w, or fail to halt. If language A is recognised by a Turing machine M that halts on all inputs, we say that M decides A. A language A is Turing-decidable, or just decidable, or computable, or (even) recursive iff some Turing machine decides A. (The difference between Turing-recognisable and (Turing-)decidable is very important.) 9-10

12 Example 2 (Sipser, Example 3.7) This machine decides the language { 0 2n n 0 }. (Note that this language is not context-free.) We can describe how this machine operates as follows (an implementation level description): M 2 = On input string w: 1. Read tape from left to right crossing off every second If the tape contained a single 0, accept. 3. Otherwise, if the tape contained an odd number of 0 s, reject. 4. Return to the left of the tape. 5. Go to step

13 Example 2 (cont.) This machine decides the language { 0 2n n 0 }. A formal description of the machine follows. It has input alphabet {0}, tape alphabet {, 0, x, #} and start state q 1. x R x R q 0 #,R 1 q 0 x,r 2 q 3 R # R q a L 0 R 0 x,r x R Running the machine on input 000: q #q 2 00 #xq 3 0 #x0q 4 #x0q r Running the machine on input 0000: q #q #xq 3 00 #x0q 4 0 #x0xq 3 #x0q 5 x #xq 5 0x #q 5 x0x q 5 #x0x #q 2 x0x #xq 2 0x #xxq 3 x #xxxq 3 q 5 0 L x L q

14 Example 3 (Sipser, Example 3.9) This machine decides the language { w#w w {0, 1} }. (Again, this language is not context-free.) M 3 = On input string w: 1. Zig-zag over the tape to corresponding positions on either side of the # symbol checking that these positions contain the same symbol. If they do not, or if no # is found, reject. 2. When all symbols to the left of # have been marked, check for any remaining symbols to the right of #. If there are none, accept; otherwise, reject. See pp and Fig. 3.10, p.145, for details. 9-13

15 Example 4 (Sipser, Example 3.11) This machine decides the language { a i b j c k i j = k, i, j, k > 0 }. (This language is also not context-free.) An implementation-level description of the machine follows. M 3 = On input string w: 1. Cross off the first a, and scan to the right for the first b. Alternately cross off the first b and the first c until all bs are gone. 2. Move left to the last crossed-off a, restoring the crossed-off bs. If there is another a, return to step 1. Otherwise, move right past all crossed-off bs and cs. If no c s remain, accept, else reject. 9-14

16 Example 4 (cont.) This machine decides the language { a i b j c k i j = k, i, j, k > 0 }. A formal description of the machine follows. The tape alphabet is {, a, b, c, A, B, C}. a R a A,R b B,R a L b R A R a L b L B R A R b L B b,l c C,L B b,l q a C R L C R b R C R C L 9-15

17 Example 5 (Sipser, Example 3.12) The element distinctness problem: E = { #x 1 #x 2 #...#x l each x i {0, 1} and x i x j for each i j } A machine to recognise (the non-cfl) E: M = On input w: 1. Mark the leftmost symbol. If it was blank, accept. If it was not #, reject. 2. Find the next # and mark it. If no # is found, accept. 3. By zig-zagging, compare the strings to the right of the two marked # s; if they are equal, reject. 4. Move the rightmost of the two marks to the next #, if any. Otherwise, move the leftmost mark to the next #, if any. Otherwise, accept. 5. Go to step

18 Variants of Turing Machines Other textbooks have definitions of Turing machines that differ slightly from Sipser s. E.g., we may allow a stay put move in addition to the left and right moves, i.e., we may define the transition function as follows: δ : Q Γ Q Γ {L, R, S}. This clearly adds no expressive power, because any such TM can be transformed to a standard TM by replacing each stay put transition by two transitions, one that moves right to a temporary state and a second that immediately moves back to the left. I.e., we can simulate stay put TMs by standard TMs. But the robustness of Turing machines goes far beyond that

19 Variants of Turing Machines (cont.) More significant generalisations of Turing machines have also been proposed: Let the TM have a doubly-infinite tape (i.e., infinite to left and right), with the tape head initially on the leftmost symbol of the (finite) input, and blanks to left and right of the input. (This is actually the most common definition!) Let the TM have several tapes, each with its own independent tape head, which may move left or right or stay put. Let the TM be nondeterministic. It turns out that none of these variants increase a Turing machine s capabilities as a recogniser! 9-18

20 Multitape Machines A multitape Turing machine has k tapes. Input is on tape 1, the other tapes are initially blank. The transition function now has the form δ : Q Γ k Q Γ k {L, R} k It specifies how the k tape heads behave when the machine is in state q i, reading a 1,...,a k : δ(q i, a 1,...,a k ) = (q j, (b 1,...,b k ), (d 1,...,d k )) state control b a a x x b b b x y a y x 9-19 x

21 Multitape Machines (cont.) Theorem. A language is Turing-recognisable iff some multitape Turing machine recognises it. Proof outline. We show how to simulate a multitape Turing machine M by a standard Turing machine S. S has tape alphabet {#} Γ Γ where # is a separator, not in Γ Γ, and there is a one-to-one correspondence between symbols in Γ and marked symbols in Γ. S initially reorganises its input x 1 x 2 x n into #x 1x 2 x n # # # # } {{ } k 1 times Symbols of Γ represent marked symbols from Γ, and denote the positions of the tape heads on the k tapes of M. 9-20

22 Multitape Machines (cont.) For example, a possible correspondence is: M b a a x x b b b x y a y x x S b # a a x x # b b b # x y a y x 9-21

23 Multitape Machines (cont.) To simulate a move of M, S scans its tape to determine the marked symbols. S then scans the tape again, updating it according to M s transition function. (Note that this requires adding a large but finite number of states and transitions to S.) If a virtual head of M moves to a # (i.e., passes the input on that tape), S shifts that symbol, and every symbol after it, one cell to the right. In the vacant cell it writes. It then continues to apply M s transition function. If M accepts or rejects, then S accepts or rejects, respectively. I.e., adding tapes to Turing machines do not enable them to recognise any more languages. 9-22

24 Nondeterministic Turing Machines A nondeterministic Turing machine (NTM) has a transition function of the form δ : Q Γ P(Q Γ {L, R}) I.e., from each configuration, there may be several possible transitions, generating a tree of computations. If some computation branch leads to accept, then the machine accepts its input. This is exactly the same form of nondeterminism as in NFAs and PDAs (except that some branches of an NTM may have infinite length). 9-23

25 Nondet. Turing Machines (cont.) First, here is a deterministic machine, NEXT, to generate {1,...,b}, in increasing length, in lexicographic order within each length: i L 1 R 1,L R 1 2,L 2 3,L. (b 1) b,l R L b 1,L i R Try running this for b = 3, starting with a blank tape. 9-24

26 Simulating Nondeterminism Theorem. A language is Turing-recognisable iff some nondeterministic Turing machine recognises it. Proof outline. We need to show that every nondeterministic Turing machine N can be simulated by a (deterministic) Turing machine D. We show how N can be simulated by a 3-tape Turing machine. Let b be the largest number of choices, according to N s transition function, for any state/symbol combination. Tape 1 contains the input. Tape 2 is used to simulate N s behaviour for each sequence of choices given by tape 3. Tape 3 holds successive sequences from {1,...,b}, in increasing length. 9-25

27 Simulating Nondeterminism (cont.) x 1 0 x D Initially tape 1 contains input w and tapes 2 and 3 are empty. 2. Copy tape 1 to tape Use tape 2 to simulate N on one branch of the nondeterministic computation. Tape 3 determines how N makes each successive choice. If the end of tape 3 is reached, or if N rejects, go to step 4. If N accepts, accept. 4. Replace the string on tape 3 by the next string generated by the machine NEXT. If N rejects on all branches to this depth, reject. Otherwise, go to step

28 Nondet. Turing Machines (cont.) I.e., adding nondeterminism to Turing machines does not allow them to recognise any more languages. A nondeterministic Turing machine that halts on every branch of its computation on all inputs is called a decider. Corollary. A language is decidable iff some nondeterministic Turing machine decides it. 9-27

29 Enumerators The Turing machine NEXT we built to generate all strings in {1,...,b} is an example of an enumerator. We could imagine it being attached to a printer, and it would print all the strings in {1,...,b}, one after the other, never terminating. Or, we could imagine a 2-tape Turing machine that wrote and never erased the sequence of strings on its second tape. For an enumerator to generate a language L, it must eventually generate (or print, or write) each string w L. It is allowed to generate any string more than once, and it is allowed to generate the strings in any order. The reason why we also call Turing-recognisable languages computably enumerable (or recursively enumerable) is the following theorem. 9-28

30 Enumerators (cont.) Theorem. A language L is Turing-recognisable iff some enumerator generates L. Proof. Let E enumerate L. Then we can build a Turing machine recognising L as follows: 1. Let w be the input. 2. Simulate E. For each string s output by E, if s = w, accept. Conversely, let M recognise L. Then we can build an enumerator E by elaborating the enumerator from a few slides back: We can enumerate Σ, producing s 1, s 2,... Here is what E does: 1. Let i = Simulate M for i steps on each of s 1,...s i. 3. For each accepting computation, print that s. 4. Increment i and go to step

31 Enumerators (cont.) Corollary. A language L is Turing-decidable iff some enumerator generates each string of L exactly once, in order of nondecreasing length. Proof. Exercise for the reader. (Actually, a simplification of the previous proof.) 9-30

32 Restricted TMs Adding capabilities to TMs does not give more power. What happens if we remove capabilities? Theorem. Every TM can be simulated by a TM with a binary tape alphabet Γ = {1, B}. Theorem. Not every TM can be simulated by a TM whose head always remains on the cells containing the initial input. 9-31

33 Equivalence with other models (cont.) (Deterministic) Turing machines have been proved to have the same expressive power as each of the following computational models: Doubly-infinite Turing machines Multitape Turing machines Nondeterministic Turing machines Binary-tape-alphabet Turing machines Two-stack pushdown automata Queue machines Lambda-calculus expressions Recursively defined (numeric) functions Lisp-definable (symbolic) functions Register machines (effectively, stored program computers, with infinite storage) Many other models! 9-32

34 The Church-Turing Thesis Church and Turing (in effect) proposed the following thesis: A problem (or language) is (algorithmically) decidable if and only if some Turing machine can decide it. Equivalently, as Turing machines can also compute functions, if we consider the final tape state as the function value: A function is (algorithmically) computable if and only if some Turing machine can compute it. We cannot prove this thesis because it concerns the informal concepts algorithmically decidable and algorithmically computable. But the evidence for this thesis is overwhelming and the thesis is universally accepted. 9-33

35 An example Hilbert s tenth problem (1900). Find an algorithm that determines the integral roots of an arbitrary polynomial (e.g., 5x 3 y 2 z 4x 2 y 3 + 3y 2 z 5 60 = 0). As it turns (Matijasevi c 1970), no such algorithm exists! I.e., Matijasevi c proved that the problem Does polynomial p have integral roots? is not algorithmically decidable. This fact, however, could only be proved after mathematicians had agreed on what an algorithm is. 9-34

36 An example (cont.) Note that the corresponding language P = { p p is a polynomial with integral roots } is Turing-recognisable. To see this, here is how we can build a Turing machine M to recognise P. Suppose the variables in p are x, y and z. M can enumerate all integer triples (i, j, k). So M can evaluate p on each triple (i, j, k) in turn. If any of these evaluations returns 0, M accepts. (As there are infinitely many integer triples, M might never halt.) 9-35

37 Descriptions of TMs Turing machines may be described at different levels: 1. Formal description (states, tape and transition function) 2. Implementation description (data representation on tape, head movement, procedural) 3. Abstract (high-level) description (English prose, ignoring data representation, control details) 9-36

38 Uses of Turing Machines Problems from many domains may be encoded as strings for Turing machines: 1. Arithmetic 2. Polynomials 3. Context-free grammars 4. Graphs, e.g, see Example 3.23, pp We can also regard a TM as defining a function, with input as the argument, and final tape state as the value. These two observations mean that TMs can be used as general computing machines, not just as language recognisers. 9-37

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