PPT - University of Virginia

```Lecture 13:
Turing Machines
cs302: Theory of Computation
University of Virginia
Computer Science
David Evans
http://www.cs.virginia.edu/evans
The Story So Far
0n1n
0n
Described by DFA, NFA,
RegExp, RegGram
w
Regular Languages
Lecture 13: Turing Machines
2
The Story So Far (Simplified)
0n1n
0n
Described by DFA, NFA,
RegExp, RegGram
w
Regular Languages
Lecture 13: Turing Machines
3
Computability Story: This Week
Languages recognizable by
any mechanical computing
machine
Lecture 13: Turing Machines
4
Computability Story: Next Week
Undecidable Problems
Decidable Problems
Recognizable Languages
Lecture 13: Turing Machines
5
Computability  Complexity (April)
Decidable Problems
Decidable Problems
P
NP
Note: not known if P  NP or P = NP
Problems that can be solved
by a computer (eventually).
Lecture 13: Turing Machines
Problems that can be solved by a
computer in a reasonable time.
6
Exam 1
Lecture 13: Turing Machines
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Exam 1
• Problem 4c: Prove that the language {0n1n2} is
not context-free.
Lengths of strings in L:
n = 0 0 + 02 = 0
n = 1 1 + 12 = 2
n = 2 2 + 22 = 6
n = 3 3 + 32 = 12
...
n = k k + k2
Lecture 13: Turing Machines
Pumping lemma for CFLs says
there must be some way of
picking s = uvxyz such that
m = |v| + |y| > 0 and uvixyiz in L for
all i.
So, increasing i by 1 adds m
symbols to the string, which must
produce a string of a length that is
not the length of a string in L.
8
Recognizing
2
n
n
{0 1 }
DPDA with two stacks?
DPDA with three stacks?
...
?
Lecture 13: Turing Machines
9
3-Stack DPDA Recognizing
2
n
n
{0 1 }
0, ε/ε/ε \$/\$/\$
Start
Count
0s
0, ε/ε/ε +/ε/+
1, +/ε/ε ε/+/ε
1s
b->r
1, \$/+/+ \$/ε/ε
1, +/ε/ε ε/+/ε
Done
Lecture 13: Turing Machines
1, ε/\$/\$ ε/ε/ε
10
1s
r->b
Can it be done with 2 Stacks?
Lecture 13: Turing Machines
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Simulating 3-DPDA with 2-DPDA
#
#
A
Lecture 13: Turing Machines
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B
Simulating 3-DPDA with 2-DPDA+
#
#
pop green
\$
3-DPDA
Lecture 13: Turing Machines
A
13
B
pushB(\$)
pushB(popA())
...
pushB(popA(#))
pushB(popA())
...
pushB(popA(#))
res = popA()
pushA(popB(#))
pushA(popB())
...
popB(\$)
2-DPDA + Forced ε-Transitions
Need to do lots of stack
manipulation to simulate
3-DPDA on one transition:
need transitions with no
input symbol (but not
nondeterminism!)
#
#
\$
A
Lecture 13: Turing Machines
B
14
Impact of Forced ε-Transitions
What is the impact of adding
non-input consuming transitions?
#
DPDA in length n input:
runs for  n steps
DPDA+ε in length n input:
can run forever!
#
\$
A
Lecture 13: Turing Machines
B
15
Is there any computing
machine we can’t simulate
with a 2-DPDA+?
Lecture 13: Turing Machines
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Use one stack to simulate the
NDPDA’s stack.
Use the other stack to keep track
of nondeterminism points: copy
of stack and decisions left to
make.
A
Lecture 13: Turing Machines
B
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Turing Machine?
A
Lecture 13: Turing Machines
B
18
Turing Machine
...
FSM
Infinite tape: Γ*
write into current square,
move one square left or right
FSM:
like PDA, except:
transitions also include direction (left/right)
final accepting and rejecting states
Lecture 13: Turing Machines
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Turing Machine Formal Description
...
FSM
7-tuple: (Q, , Γ, δ, q0, qaccept, qreject)
Q: finite set of states
: input alphabet (cannot include blank symbol, _)
Γ: tape alphabet, includes  and _
δ: transition function: Q  Γ  Q  Γ  {L, R}
q0: start state, q0  Q
qaccept: accepting state, qaccept  Q
qreject: rejecting state, qreject  Q
(Sipser’s notation)
Lecture 13: Turing Machines
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Turing Machine Computing Model
Initial configuration:
x
x
x
FSM
q0
x
x
x
x
_
_
_
...
blanks
input
TM Configuration: Γ*  Q  Γ*
tape contents
Lecture 13: Turing Machines
_
x
current
FSM state
21
tape contents
TM Computing Model
δ*: Γ*  Q  Γ*  Γ*  Q  Γ*
The qaccept and qreject states are final:
δ*(L, qaccept, R)  (L, qaccept, R)
δ*(L, qreject, R)  (L, qreject, R)
Lecture 13: Turing Machines
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TM Computing Model
δ*: Γ*  Q  Γ*  Γ*  Q  Γ*
u, v  Γ*, a, b  Γ
a
u
...
b
FSM
v
q
δ*(ua, q, bv) = (uac, qr, v) if δ(q, b) = (qr, c, R)
δ*(ua, q, bv) = (u, qr, acv) if δ(q, b) = (qr, c, L)
Also: need a rule to cover what happens at left edge of tape
Lecture 13: Turing Machines
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Thursday’s Class
• Robustness
of TM model
• ChurchTuring
Thesis
Lecture 13: Turing Machines