1 CD5560 FABER Formal Languages, Automata and Models of Computation Lecture 13 Mälardalen University 2010
Feb 10, 2016
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CD5560
FABER
Formal Languages, Automata and Models of Computation
Lecture 13
Mälardalen University
2010
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Content
Alan Turing and Hilbert Program Universal Turing Machine Chomsky Hierarchy DecidabilityReducibilityUncomputable FunctionsRice’s TheoremInteractive Computing, Persistent TM’s (Dina Goldin)
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http://www.turing.org.uk/turing/
Who was Alan Turing?Founder of computer science, mathematician, philosopher, codebreaker, visionary man before his time.
http://www.cs.usfca.edu/www.AlanTuring.net/turing_archive/index.html- Jack Copeland and Diane Proudfoot
http://www.turing.org.uk/turing/ The Alan Turing Home PageAndrew Hodges
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Alan Turing
1912 (23 June): Birth, London
1926-31: Sherborne School
1930: Death of friend Christopher Morcom
1931-34: Undergraduate at King's College, Cambridge University
1932-35: Quantum mechanics, probability, logic
1935: Elected fellow of King's College, Cambridge
1936: The Turing machine, computability, universal machine
1936-38: Princeton University. Ph.D. Logic, algebra, number theory
1938-39: Return to Cambridge. Introduced to German Enigma cipher machine
1939-40: The Bombe, machine for Enigma decryption
1939-42: Breaking of U-boat Enigma, saving battle of the Atlantic
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Alan Turing
1943-45: Chief Anglo-American crypto consultant. Electronic work.
1945: National Physical Laboratory, London
1946: Computer and software design leading the world.
1947-48: Programming, neural nets, and artificial intelligence
1948: Manchester University
1949: First serious mathematical use of a computer
1950: The Turing Test for machine intelligence
1951: Elected FRS. Non-linear theory of biological growth
1952: Arrested as a homosexual, loss of security clearance
1953-54: Unfinished work in biology and physics
1954 (7 June): Death (suicide) by cyanide poisoning, Wilmslow, Cheshire.
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Hilbert’s Program, 1900
Hilbert’s hope was that mathematics would be reducible to finding proofs (manipulating the strings of symbols) from a fixed system of axioms, axioms that everyone could agree were true.
Can all of mathematics be made algorithmic, or will there always be new problems that outstrip any given algorithm, and so require creative acts of mind to solve?
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TURING MACHINES
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Turing’s "Machines". These machines are humans who calculate. (Wittgenstein)
A man provided with paper, pencil, and rubber, and subject to strict discipline, is in effect a universal machine.
(Turing)
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............Tape
Read-Write headControl Unit
Standard Turing Machine
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............
Read-Write head
No boundaries -- infinite length
The head moves Left or Right
The Tape
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............
Read-Write head
1. Reads a symbol2. Writes a symbol3. Moves Left or Right
The head at each time step:
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ExampleTime 0
............ a a cb
Time 1............ a b k c
1. Reads a2. Writes k3. Moves Left
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Head starts at the leftmost positionof the input string
............
Blank symbol
head
a b ca
Input string
The Input String
#####
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1q 2qLba ,
Read Write Move Left
1q 2qRba ,
Move Right
States & Transitions
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............ # a b caTime 1
1q 2qRba ,
............ a b cbTime 2
1q
2q
# # # #
# # # # #
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Determinism
1q
2qRba ,
Allowed Not Allowed
3qLdb ,
1q
2qRba ,
3qLda ,
No lambda transitions allowed in standard TM!
Turing Machines are deterministic
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Formal Definitions for
Turing Machines
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Transition Function
1q 2qRba ,
),,(),( 21 Rbqaq
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Turing Machine
),#,,,,,( 0 FqQM
Transitionfunction
Initialstate
blank
Finalstates
States
Inputalphabet
Tapealphabet
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Universal Turing Machine
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A limitation of Turing Machines:
Better are reprogrammable machines.
Turing Machines are “hardwired”
they executeonly one program
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Solution: Universal Turing Machine
• Reprogrammable machine
• Simulates any other Turing Machine
Characteristics:
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Universal Turing Machine
simulates any other Turing Machine M
Input of Universal Turing Machine
• Description of transitions of M• Initial tape contents of M
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Universal Turing Machine
Description of Three tapes
MTape Contents of
Tape 2
State of M
Tape 3
M
Tape 1
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We describe Turing machine as a string of symbols:
We encode as a string of symbols
M
M
Description of M
Tape 1
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Alphabet Encoding
Symbols: a b c d
Encoding: 1 11 111 1111
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State Encoding
States: 1q 2q 3q 4q
Encoding: 1 11 111 1111
Head Move Encoding
Move:
Encoding:
L R
1 11
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Transition Encoding
Transition: ),,(),( 21 Lbqaq
Encoding: 10110110101
separator
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Machine Encoding
Transitions:
),,(),( 21 Lbqaq
Encoding:
10110110101
),,(),( 32 Rcqbq
110111011110101100
separator
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Tape 1 contents of Universal Turing Machine:
encoding of the simulated machine as a binary string of 0’s and 1’s
M
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A Turing Machine is described with a binary string of 0’s and 1’s.
The set of Turing machines forms a language:
Each string of the language isthe binary encoding of a Turing Machine.
Therefore:
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Language of Turing Machines
L = { 010100101,
00100100101111,
111010011110010101, …… }
(Turing Machine 1)
(Turing Machine 2)
……
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The Chomsky Hierarchy
34Non-recursively enumerable
Recursively-enumerable
Recursive
Context-sensitive
Context-free
Regular
The Chomsky Language Hierarchy
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Unrestricted Grammars
Productionsvu
String of variablesand terminals
String of variablesand terminals
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Example of unrestricted grammar
dAccAaB
aBcS
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A language is recursively enumerableif and only if it is generated by anunrestricted grammar.
L
Theorem
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Context-Sensitive Grammars
and |||| vu
Productionsvu
String of variablesand terminals
String of variablesand terminals
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The language }{ nnn cbais context-sensitive:
aaAaaaBBbbBBbccAcbAAb
aAbcabcS
|
|
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A language is context sensitiveif and only if
it is accepted by a Linear-Bounded automaton.
L
Theorem
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Linear Bounded Automata (LBAs)are the same as Turing Machineswith one difference:
The input string tape spaceis the only tape space allowed to use.
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[ ]a b c d e
Left-endmarker
Input string
Right-endmarker
Working space in tape
All computation is done between end markers.
Linear Bounded Automaton (LBA)
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There is a language which is context-sensitivebut not recursive.
Observation
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Decidability
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Consider problems with answer YES or NO.
Examples
• Does Machine have three states ?M• Is string a binary number? w• Does DFA accept any input? M
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A problem is decidable if some Turing machinesolves (decides) the problem.
Decidable problems:
• Does Machine have three states ?M• Is string a binary number? w• Does DFA accept any input? M
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Turing MachineInputprobleminstance
YES
NO
The Turing machine that solves a problemanswers YES or NO for each instance.
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The machine that decides a problem:
• If the answer is YES then halts in a yes state
• If the answer is NO then halts in a no state
These states may not be the final states.
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YES
NO
Turing Machine that decides a problem
YES and NO states are halting states
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Difference between Recursive Languages (“Acceptera”) and Decidable problems (“Avgöra”)
The YES states may not be final states.For decidable problems:
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Some problems are undecidable:
There is no Turing Machine thatsolves all instances of the problem.
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A famous undecidable problem:
The halting problem
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The Halting Problem
Input: • Turing Machine M• String w
Question: Does halt on ? M w
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Theorem
The halting problem is undecidable.
Proof
Assume to the contrary thatthe halting problem is decidable.
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There exists Turing Machinethat solves the halting problem
H
HM
w
YES M halts on w
M doesn’t halt on
wNO
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H
wwM 0qyq
nq
Input:initial tape contents
Encodingof M w
String
YES
NO
Construction of H
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Construct machine H
returns YES then loop forever. HIf
returns NO then halt.HIf
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H
wwM 0qyq
nq NO
aq bq
H
Loop forever
YES
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HConstruct machine
Input:
If M halts on input Mw
Then loop forever
Else halt
Mw (machine )M
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Mw MM wwcopy
Mw H
H
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HRun machine with input itself
Input:
If halts on input
Then loop forever
Else halt
Hw ˆ (machine )H
H Hw ˆ
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on input H Hw ˆ
If halts then loops forever.
If doesn’t halt then it halts.
:
H
H
CONTRADICTION !
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This means that
The halting problem is undecidable.
END OF PROOF
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Another proof of the same theorem
If the halting problem was decidable thenevery recursively enumerable languagewould be recursive.
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Theorem
The halting problem is undecidable.
Proof
Assume to the contrary thatthe halting problem is decidable.
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There exists Turing Machinethat solves the halting problem.
H
HM
w
YES M halts on w
M doesn’t halt on
wNO
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Let be a recursively enumerable language. L
Let be the Turing Machine that accepts .M L
We will prove that is also recursive: L
We will describe a Turing machine thataccepts and halts on any input.L
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M halts on ?wYES
NOM
w
Run with input
Mw
Hreject w
accept w
reject w
Turing Machine that acceptsand halts on any input
L
Halts on final state
Halts on non-final state
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Therefore L is recursive.
But there are recursively enumerablelanguages which are not recursive.
Contradiction!
Since is chosen arbitrarily, we have proven that every recursively enumerablelanguage is also recursive.
L
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Therefore, the halting problem is undecidable.
END OF PROOF
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A simple undecidable problem:
The Membership Problem
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The Membership Problem
Input: • Turing Machine M
• String w
Question: Does accept ? M w
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Theorem
The membership problem is undecidable.
Proof
Assume to the contrary thatthe membership problem is decidable.
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There exists a Turing Machinethat solves the membership problem
H
HM
w
YES M accepts w
NO M rejects w
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Let be a recursively enumerable language. L
Let be the Turing Machine that accepts .M L
We will prove that is also recursive: L
We will describe a Turing machine thataccepts and halts on any input.L
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M accepts ?wNO
YESM
w
Haccept w
Turing Machine that acceptsand halts on any input
L
reject w
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Therefore, L is recursive.
But there are recursively enumerablelanguages which are not recursive.
Contradiction!
Since is chosen arbitrarily, we have proven that every recursively enumerablelanguage is also recursive.
L
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Therefore, the membership problem is undecidable.
END OF PROOF
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Reducibility
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Problem is reduced to problemA B
If we can solve problem thenwe can solve problem .
BA
B
A
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If is decidable then is decidable.B A
If is undecidable then is undecidable.A B
Problem is reduced to problemA B
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Example
the halting problem
reduced to
the state-entry problem.
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The state-entry problem
Inputs:
Question:
M•Turing Machine
•State q
•String w
Does M enter state qon input ?w
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Theorem
The state-entry problem is undecidable.
ProofReduce the halting problem to
the state-entry problem.
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Suppose we have an algorithm (Turing Machine)
that solves the state-entry problem.
We will construct an algorithmthat solves the halting problem.
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Algorithm for state-entry problem
Mw
q
YES
NO
entersM q
doesn’t enter
M q
Assume we have the state-entry algorithm:
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Algorithm for Halting problem
M
w
YES
NO
halts onM w
doesn’t halt on
M w
We want to design the halting algorithm:
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Modify input machine M• Add new state q• From any halting state add transitions to q
M q
halting statesSinglehalt state
M
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M halts
M halts on state q
if and only if
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Algorithm for halting problem
Inputs: machine and stringM w
2. Run algorithm for state-entry problem with inputs: M wq, ,
1. Construct machine with state M q
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GenerateM M
w
M qw
State-entryalgorithm
Halting problem algorithm
YES
NO
YES
NO
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Since the halting problem is undecidable,it must be that the state-entry problemis also undecidable.
END OF PROOF
We reduced the halting problemto the state-entry problem.
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Another example
The halting problem
reduced to
the blank-tape halting problem.
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The blank-tape halting problem
Input: MTuring Machine
Question: Does M halt when started witha blank tape?
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ProofReduce the halting problem to the
blank-tape halting problem.
Theorem
The blank-tape halting problem is undecidable.
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Suppose we have an algorithmfor the blank-tape halting problem.
We will construct an algorithmfor the halting problem.
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Algorithm for blank-tape halting problem
M
YES
NO
halts onblank tapeM
doesn’t halton blank tape
M
Assume we have the blank-tape halting algorithm
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Algorithm for halting problem
M
w
YES
NO
halts onM w
doesn’t halt on
M w
We want to design the halting algorithm:
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wMConstruct a new machine
• On blank tape writes w• Then continues execution like M
wM
Mthen write w
step 1 step2if blank tape execute
with input w
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M halts on input string
wM halts when started with blank tape.
if and only if
w
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Algorithm for halting problem
1. Construct wM
2. Run algorithm for blank-tape halting problem with input wM
Inputs: machine and stringM w
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Generate
wMMw
Blank-tape halting algorithm
Halting problem algorithm
YES
NOwM
YES
NO
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Since the halting problem is undecidable,the blank-tape halting problem is also undecidable.
END OF PROOF
We reduced the halting problemto the blank-tape halting problem.
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Summary of Undecidable Problems
Halting Problem
Does machine halt on input ?M w
Membership problemDoes machine accept string ?M w
Is a string member of a
recursively enumerable language ?)Lw(In other words:
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Does machine halt when startingon blank tape?
Blank-tape halting problemM
State-entry Problem:
Does machine enter state on input ?
Mw
q
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Uncomputable Functions
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Uncomputable Functions
A function is uncomputable if it cannotbe computed for all of its domain.
Domain Rangef
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An uncomputable function:
)(nfmaximum number of moves untilany Turing machine with stateshalts when started with the blank tape.
n
Example
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TheoremFunction is uncomputable.)(nf
Then the blank-tape halting problem is decidable.
ProofAssume to the contrary that is computable.)(nf
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Algorithm for blank-tape halting problem
Input: machine M
1. Count states of : M m2. Compute )(mf3. Simulate for steps starting with empty tape
M )(mf
If halts then return YES otherwise return NO
M
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Therefore, the blank-tape haltingproblem must be decidable.
However, we know that the blank-tape halting problem is undecidable.
Contradiction!
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Therefore, function is uncomputable.)(nf
END OF PROOF
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Rice’s Theorem
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Non-trivial properties of recursively enumerable languages:
any property possessed by some (not all)recursively enumerable languages.
Definition
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Some non-trivial properties of recursively enumerable languages:
• is emptyL
L• is finiteL• contains two different strings
of the same length
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Rice’s Theorem
Any non-trivial property of a recursively enumerable languageis undecidable.
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We will prove some non-trivial propertieswithout using Rice’s theorem.
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TheoremFor any recursively enumerable language Lit is undecidable whether it is empty.
Proof
We will reduce the membership problemto the problem of deciding whether is empty.
L
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Membership problem:Does machine accept string ?wM
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Algorithm for empty languageproblem
M
YES
NO
Assume we have the empty language algorithm:
Let be the machine that accepts M L
)(ML
)(ML
empty
not empty
LML )(
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Algorithm for membershipproblem
M
w
YES
NO
acceptsM w
rejectsM w
We will design the membership algorithm:
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First construct machine : wM
When enters a final state, compare original input string with . w
M
Accept if original input is the same as .w
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Lw
)( wML is not empty
if and only if
}{)( wML w
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Algorithm for membership problem
Inputs: machine and string M w
1. Construct wM
2. Determine if is empty )( wML
YES: then )(MLw
NO: then )(MLw
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construct
wM
Check if)( wML
is empty
YES
NO
M
w
NO
YES
Membership algorithm
wM
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Since the membership problem is undecidable,the empty language problem is also undecidable.
END OF PROOF
We reduced the empty language problemto the membership problem.
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Decidability…continued…
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Theorem
For a recursively enumerable language Lit is undecidable to determine whether is finite. L
Proof
We will reduce the halting problemto the finite language problem.
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Assume we have the finite language algorithm:
Algorithm for finite languageproblem
M
YES
NO
)(ML
)(ML
finite
not finite
Let be the machine that accepts M LLML )(
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We will design the halting problem algorithm:
Algorithm for Halting problem
M
w
YES
NO
halts onM w
doesn’t halt on
M w
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First construct machine .wM
When enters a halt state, accept any input (infinite language).
M
Initially, simulates on input . M w
Otherwise accept nothing (finite language).
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M halts on
)( wML is not finite.
if and only if
w
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Algorithm for halting problem:Inputs: machine and string M w
1. Construct wM2. Determine if is finite )( wML
YES: then doesn’t halt on M wNO: then halts on M w
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construct
wM
Check if)( wML
is finite
YES
NO
M
w
NO
YES
Machine for halting problem
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Since the halting problem is undecidable,the finite language problem is also undecidable.
END OF PROOF
We reduced the finite language problemto the halting problem.
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TheoremFor a recursively enumerable language Lit is undecidable whether contains two different strings of same length.
L
ProofWe will reduce the halting problemto the two strings of equal length- problem.
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Assume we have the two-strings algorithm:
Let be the machine that accepts M LLML )(
Algorithm for two-stringsproblem
M
YES
NO
)(ML
)(ML
contains
doesn’t contain
two equal length strings
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We will design the halting problem algorithm:
Algorithm for Halting problem
M
w
YES
NO
halts onM w
doesn’thalt on
M w
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First construct machine . wM
When enters a halt state, accept symbols or .
M
Initially, simulates on input . M w
a b(two equal length strings)
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M halts on
wM
if and only if
w
accepts and a b
(two equal length strings)
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Algorithm for halting problem
Inputs: machine and string M w
1. Construct wM2. Determine if accepts two strings of equal length
wM
YES: then halts on M wNO: then doesn’t halt on M w
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construct
wM
Check if)( wML
has twoequal lengthstrings
YES
NO
M
w
YES
NO
Machine for halting problem
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Since the halting problem is undecidable,the two strings of equal length problem is also undecidable.
END OF PROOF
We reduced the two strings of equal length - problem to the halting problem.
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Rices sats
Om är en mängd av Turing-accepterbara språk som innehåller något men inte alla sådana språk, så kan ingen TM avgöra för ett godtyckligt Turing-accepterbart språk L om L tillhör eller ej.
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Exempel
Givet en Turingmaskin M, kan man avgöra om alla strängar som accepteras av M börjar och slutar på samma tecken?
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Oavgörbart
Problemet handlar om en icke-trivial språkegenskap. Det finns TM:er vars accepterade strängar har egenskapen i fråga, och det finns TM:er vars accepterade strängar inte har egenskapen.
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Formellt:
= { L | TM accepterbara språkvars strängar börjar och slutar på samma tecken. }
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Interaction: Conjectures, Results, and Myths
Dina GoldinUniv. of Connecticut, Brown University
http://www.cse.uconn.edu/~dqg
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Fundamental Questions Underlying Theory of
Computation
What is computation?
How do we model it?
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Shared Wisdom(from our undergraduate Theory of Computation courses)
computation: finite transformation of input to outputinput: finite size (e.g. string or number)closed system: all input available at start, all output generated at end
behavior: functions, transformation of input data to output dataChurch-Turing thesis: Turing Machines capture this (algorithmic) notion of computation
Mathematical worldview: All computable problems
are function-based.
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“The theory of computability and non-computability [is] usually referred to as the theory of recursive functions... the notion of TM has been made central in the development."
Martin Davis, Computability & Unsolvability, 1958
“Of all undergraduate CS subjects, theoretical computer science has changed the least over the decades.”
SIGACT News, March 2004
“A TM can do anything that a computer can do.”Michael Sipser, Introduction to the Theory of Computation, 1997
The Mathematical Worldview
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The Operating System Conundrum
Real programs, such as operating systems and word processors, often receive an unbounded amount of input over time, and never "finish" their task. Turing machines do not model such ongoing computation well…
[TM entry, Wikipedia]
If a computation does not terminate,
it’s “useless” – but aren’t OS’s
useful??
153
Rethinking Shared Wisdom:(what do computers do?)
computation: finite transformation of input to outputinput: finite-size (string or number)
closed system: all input available at start, all output generated at end
behavior: functions, algorithmic transformation of input data to output dataChurch-Turing thesis: Turing Machines capture this (algorithmic) notion of computation
computation: ongoing process which performs a task or delivers a service
dynamically generated stream of input tokens (requests, percepts, messages)
open system: later inputs depend on earlier outputs and vice versa (I/O entanglement, history dependence)
behavior: processes, components, control devices, reactive systems, intelligent agents
Wegner’s conjecture: Interaction is more powerful than algorithms
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Example: Driving home from work
Algorithmic input: a description of the world (a static “map”)
Output: a sequence of pairs of #s (time-series data)- for turning the wheel- for pressing gas/break
Similar to classic AI search/planning problems.
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But… in a real-world environment, the output depends on every grain of sand in the road (chaotic behavior).
Can we possibly have a map that’s detailed enough?
Worse yet… the domain is dynamic. The output depends on weather conditions, and on other drivers and pedestrians.
We can’t possibly be expected to predict that in advance!
Nevertheless the problem is solvable!
Google “autonomous vehicle research”
Driving home from work (cont.)
?
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Driving home from work (cont.)
The problem is solvable interactively.
Interactive input: stream of video camera images, gathered as we are driving
Output: the desired time-series data, generated as we are driving
similar to control systems, or online computation
A paradigm shift in the conceptualization of computational problem solving.
157
• Rethinking the mathematical worldview • Persistent Turing Machines (PTMs)• PTM expressiveness• Sequential Interaction
– Sequential Interaction Thesis• The Myth of the Church-Turing Thesis
– the origins of the myth• Conclusions and future work
Outline
158
Sequential Interaction
• Sequential interactive computation:
system continuously interacts with its environment by alternately accepting an input string
and computing a corresponding output string.
• Examples:- method invocations of an object instance
in an OO language- a C function with static variables- queries/updates to single-user databases- recurrent neural networks
- control systems- online computation- transducers- dynamic algorithms- embedded systems
159
Sequential Interaction Thesis
• Universal PTM: simulates any other PTM– Need additional input describing the PTM (only once)
• Example: simulating Answering Machine(simulate AM, will-do), (record hello, ok), (erase, done), (record John, ok),(record Hopkins, ok), (playback, John Hopkins), …
Simulation of other sequential interactive systems is analogous.
Whenever there is an effective method for performing sequential interactive computation, this computation
can be performed by a Persistent Turing Machine
160
Church-Turing Thesis Revisited• Church-Turing Thesis:
Whenever there is an effective method for obtaining the values of a mathematical function, the function can be computed by a Turing Machine
• Common Reinterpretation (Strong Church-Turing Thesis) A TM can do (compute) anything that a computer can do
• The equivalence of the two is a myth – the function-based behavior of algorithms does not capture all
forms of computation– this myth has been dogmatically accepted by the CS community
• Turing himself would have denied it– in the same paper where he introduced what we now call Turing
Machines, he also introduced choice machines, as a distinct model of computation
– choice machines extend Turing Machines to interaction by allowing a human operator to make choices during the computation.
161
Origins of the Church-Turing Thesis Myth
A TM can do anything that a computer can do.
Based on several claims:1. A problem is solvable if there exists a Turing Machine
for computing it.2. A problem is solvable if it can be specified by an algorithm.3. Algorithms are what computers do.
Each claim is correct in isolationprovided we understand the underlying assumptions
Together, they induce an incorrect conclusionTMs = solvable problems = algorithms = computation
162
Deconstructing the Turing Thesis Myth (1)
TMs = solvable problems
• Assumes:All computable problems are function-based.
• Reasons: – Theory of Computation started as a field of mathematics; mathematical
principles were adopted for the fundamental notions of computation, identifying computability with the computation of functions, as well as with Turing Machines.
– The batch-based modus operandi of original computers did not lend itself to other conceptualizations of computation.
163
Deconstructing the Turing Thesis Myth (2)
solvable problems = algorithms
Assumes:- Algorithmic computation is also function based;
i.e., the computational role of an algorithm is to transform input data to output data.
• Reasons: – Original (mathematical) meaning of “algorithms”
E.g. Euclid’s greatest common divisor algorithm
– Original (Knuthian) meaning of “algorithms” “An algorithm has zero or more inputs, i.e., quantities which are given to it initially before the algorithm begins.“ [Knuth’68]
164
Deconstructing the Turing Thesis Myth (3)
algorithms = computation
• Reasons: – The ACM Curriculum (1968): Adopted algorithms as the central
concept of CS without explicit agreement on the meaning of this term.
– Textbooks: When defining algorithms, the assumption of their closed function-based nature was often left implicit, if not forgotten
“An algorithm is a recipe, a set of instructions or the specifications of a process for doing something. That something is usually solving a problem of some sort.” [Rice&Rice’69]
“An algorithm is a collection of simple instructions for carrying out some task. Commonplace in everyday life, algorithms sometimes are called procedures or recipes...” [Sipser’97]
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• Rethinking the mathematical worldview • Persistent Turing Machines (PTMs)• PTM expressiveness• Sequential Interaction • The Myth of the Church-Turing Thesis• Conclusions and future work
Outline
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The Shift to Interaction in CS
Computation = transforming input to output
Computation = carrying out a task over time
Logic and search in AI Intelligent agents, partially observable environments, learning
Procedure-oriented programming
Object-oriented programming
Closed systems Open systemsCompositional behavior Emergent behaviorRule-based reasoning Simulation, control, semi-Markov
processes
Algorithmic Interactive
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The Interactive Turing Test
• From answering questions to holding discussions.• Learning from -- and adapting to -- the questioner.• “Book intelligence” vs. “street smarts”.
“It is hard to draw the line at what is intelligence and what is environmental interaction. In a sense, it does not really matter which is which, as all intelligent systems must be situated in some world or other if they are to be useful entities.” [Brooks]
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• Many other interactive models– Reactive [MP] and embedded systems– Dataflow, I/O automata [Lynch], synchronous languages, finite/pushdown
automata over infinite words– Interaction games [Abramsky], online algorithms [Albers]– TM extensions: on-line Turing machines [Fischer], interactive Turing machines
[Goldreich]...
• Concurrency Theory– Focuses on communication (between concurrent agents/processes) rather than
computation [Milner]– Orthogonal to the theory of computation and TMs.
• What makes PTMs unique?– Provably more expressive than TMs.– Bridging the gap between concurrency theory (labeled transition systems) and
traditional TOC.
Modeling Interactive Computation: PTMs in Perspective
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• Theory of Sequential Interactionconjecture: notions analogous to computational complexity, logic, and recursive functions can be developed for sequential interaction computation
• Multi-stream interaction– From hidden variables to hidden interfaces conjecture: multi-stream interaction is more
powerful than sequential interaction [Wegner’97]• Formalizing indirect interaction
– Interaction via persistent, observable changes to the common environment
– In contrast to direct interaction (via message passing) conjecture: direct interaction does not capture all
forms of multi-agent behaviors
Future Work: 3 conjectures
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Referenceshttp://www.cse.uconn.edu/~dqg/papers/
[Wegner’97] Peter WegnerWhy Interaction is more Powerful than AlgorithmsCommunications of the ACM, May 1997
[EGW’04] Eugene Eberbach, Dina Goldin, Peter Wegner Turing's Ideas and Models of Computationbook chapter, in Alan Turing: Life and Legacy of a Great Thinker, Springer 2004
[I&C’04] Dina Goldin, Scott Smolka, Paul Attie, Elaine SondereggerTuring Machines, Transition Systems, and InteractionInformation & Computation Journal, 2004
[GW’04] Dina Goldin, Peter WegnerThe Church-Turing Thesis: Breaking the Mythpresented at CiE 2005, Amsterdam, June 2005 to be published in LNCS