AUBER F16

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CDT314 FABER Formal Languages, Automata and
Models of Computation Lecture 13 School of
Innovation, Design and Engineering Mälardalen
University 2011
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ContentAlan Turing and Hilbert Program
Universal Turing Machine Chomsky Hierarchy
DecidabilityReducibilityUncomputable
FunctionsRices TheoremChurch-Turing
ThesisComputation beyond Turing
ModelInteractive Computing, Persistent TMs
(Dina Goldin/Peter Wegner)
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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, London1926-31 Sherborne
  School1930 Death of friend Christopher
  Morcom1931-34 Undergraduate at King's College,
  Cambridge University1932-35 Quantum mechanics,
  probability, logic1935 Elected fellow of King's
  College, Cambridge1936 The Turing machine,
  computability, universal machine1936-38
  Princeton University. Ph.D. Logic, algebra,
  number theory1938-39 Return to Cambridge.
  Introduced to German Enigma cipher
  machine1939-40 The Bombe, machine for Enigma
  decryption1939-42 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, London1946 Computer and software
  design leading the world.1947-48 Programming,
  neural nets, and artificial intelligence1948
  Manchester University1949 First serious
  mathematical use of a computer1950 The Turing
  Test for machine intelligence1951 Elected FRS.
  Non-linear theory of biological growth1952
  Arrested as a homosexual, loss of security
  clearance1953-54 Unfinished work in biology and
  physics1954 (7 June) Death (suicide) by cyanide
  poisoning, Wilmslow, Cheshire.

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Hilberts Program, 1900

• Hilberts 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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• Turings "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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Standard Turing Machine
Tape
......
......
Read-Write head
Control Unit
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The Tape
No boundaries -- infinite length
......
......
Read-Write head
The head moves Left or Right
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......
......
Read-Write head
The head at each time step
1. Reads a symbol 2. Writes a symbol 3. Moves
Left or Right
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Example
3. Moves Left
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The Input String
Input string
Blank symbol
......
......
head
Head starts at the leftmost position of the input
string
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States Transitions
Write
Read
Move Left
Move Right
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Time 1
......
......
Time 2
......
......
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Determinism
Turing Machines are deterministic
Not Allowed
Allowed
No lambda transitions allowed in standard TM!
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Formal Definitions for Turing Machines

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Transition Function
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Turing Machine
Input alphabet
Tape alphabet
States
Final states
Transition function
Initial state
blank
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Universal Turing Machine

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A limitation of Turing Machines
Better are reprogrammable machines.
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Solution
Universal Turing Machine
Characteristics

• Reprogrammable machine
• Simulates any other Turing Machine

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Universal Turing Machine
simulates any other Turing Machine
Input of Universal Turing Machine

• Description of transitions of

• Initial tape contents of

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Tape 1
Three tapes
Description of
Tape 2
Universal Turing Machine
Tape Contents of
Tape 3
State of
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Tape 1
Description of
We describe Turing machine as a string of
symbols We encode as a string of symbols
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State Encoding
Head Move Encoding
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Transition Encoding
Transition
Encoding
separator
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Machine Encoding
Transitions
Encoding
separator
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Tape 1 contents of Universal Turing Machine
encoding of the simulated machine as
a binary string of 0s and 1s
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A Turing Machine is described with a binary
string of 0s and 1s.
Therefore
The set of Turing machines forms a language
Each string of the language is the binary
encoding of a Turing Machine.
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Language of Turing Machines
(Turing Machine 1)
L 010100101, 00100100101111,
111010011110010101,
(Turing Machine 2)

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The Chomsky Hierarchy

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The Chomsky Language Hierarchy
Recursively-enumerable
Recursive
Context-sensitive
Context-free
Regular
Non-recursively enumerable
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Theorem
A language is recursively enumerable if and
only if it is generated by an unrestricted
grammar.
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is context-sensitive
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Theorem
A language is context sensitive if and only
if it is accepted by a Linear-Bounded
automaton.
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Linear Bounded Automata (LBAs) are the same as
Turing Machines with one difference
The input string tape space is the only tape
space allowed to use.
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Observation
There is a language which is context-sensitive but
not recursive.
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Decidability

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Consider problems with answer YES or NO.
Examples
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A problem is decidable if some Turing
machine solves (decides) the problem.
Decidable problems
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The Turing machine that solves a problem answers
YES or NO for each instance.
YES
Input problem instance
Turing Machine
NO
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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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Turing Machine that decides a problem
YES
NO
YES and NO states are halting states
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Difference between
Recursive Languages (Acceptera) and Decidable
problems (Avgöra)
For decidable problems
The YES states may not be final states.
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Some problems are undecidable
There is no Turing Machine that solves all
instances of the problem.
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A famous undecidable problem
The halting problem
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The Halting Problem
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Theorem
The halting problem is undecidable.
Proof
Assume to the contrary that the halting problem
is decidable.
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YES
halts on
doesnt halt on
NO
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Input initial tape contents
YES
NO
Encoding of
String
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Loop forever
YES
NO
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Construct machine
Input
(machine )
halts on input
If
Then loop forever
Else halt
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copy
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Run machine with input itself
(machine )
Input
halts on input
If
Then loop forever
Else halt
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on input
If halts then loops forever.
If doesnt halt then it halts.
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 then every
recursively enumerable language would be
recursive.
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Theorem
The halting problem is undecidable.
Proof
Assume to the contrary that the halting problem
is decidable.
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There exists Turing Machine that solves the
halting problem.
YES
halts on
doesnt halt on
NO
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Turing Machine that accepts and halts on any input
NO
reject
halts on ?
YES
accept
Halts on final state
Run with input
reject
Halts on non-final state
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Therefore
is recursive.
Since is chosen arbitrarily, we have
proven that every recursively enumerable language
is also recursive.
But there are recursively enumerable languages
which are not recursive.
Contradiction!
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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

• String

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Theorem
The membership problem is undecidable.
Proof
Assume to the contrary that the membership
problem is decidable.
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There exists a Turing Machine that solves the
membership problem
accepts
YES
NO
rejects
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We will prove that is also recursive
We will describe a Turing machine that accepts
and halts on any input.
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Turing Machine that accepts and halts on any input
YES
accept
accepts ?
NO
reject
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But there are recursively enumerable languages
which are not recursive.
Contradiction!
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Therefore, the membership problem is undecidable.
END OF PROOF
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Reducibility

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Example
the halting problem
reduced to
the state-entry problem.
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The state-entry problem
Inputs
Question
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Theorem
The state-entry problem is undecidable.
Proof
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Suppose we have an algorithm (Turing
Machine) that solves the state-entry problem.
We will construct an algorithm that solves the
halting problem.
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Assume we have the state-entry algorithm
YES
enters
Algorithm for state-entry problem
NO
doesnt enter
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We want to design the halting algorithm
YES
halts on
Algorithm for Halting problem
NO
doesnt halt on
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Modify input machine
Single halt state
halting states
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halts
if and only if
halts on state
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Algorithm for halting problem
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Halting problem algorithm
YES
YES
Generate
State-entry
NO
algorithm
NO
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We reduced the halting problem to the state-entry
problem.
Since the halting problem is undecidable, it must
be that the state-entry problem is also
undecidable.
END OF PROOF
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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
Turing Machine
Question
Does
halt when started with
a blank tape?
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Suppose we have an algorithm for the blank-tape
halting problem.
We will construct an algorithm for the halting
problem.
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Assume we have the blank-tape halting algorithm
halts on blank tape
YES
Algorithm for blank-tape halting problem
NO
doesnt halt on blank tape
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We want to design the halting algorithm
YES
halts on
Algorithm for halting problem
NO
doesnt halt on
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step 1
step2
if blank tape
execute
then write
with input
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halts on input string
if and only if
halts when started with blank tape.
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Algorithm for halting problem
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Halting problem algorithm
YES
Blank-tape halting algorithm
YES
Generate
NO
NO
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We reduced the halting problem to the blank-tape
halting problem.
Since the halting problem is undecidable, the
blank-tape halting problem is also undecidable.
END OF PROOF
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Summary of Undecidable Problems
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Blank-tape halting problem
Does machine halt when starting on blank
tape?
State-entry Problem
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Uncomputable Functions

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Uncomputable Functions
Domain
Range
A function is uncomputable if it cannot be
computed for all of its domain.
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Example
An uncomputable function
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Then the blank-tape halting problem is
decidable.
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Algorithm for blank-tape halting problem
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Therefore, the blank-tape halting problem must be
decidable.
However, we know that the blank-tape halting
problem is undecidable.
Contradiction!
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END OF PROOF
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Rices Theorem

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Definition
Non-trivial property of recursively enumerable
languages
Any property possessed by some (not
all) recursively enumerable languages.
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Some non-trivial properties of recursively
enumerable languages
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Rices Theorem
Any non-trivial property of a recursively
enumerable language is undecidable.
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Summary in Swedish 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 TMer vars accepterade
strängar har egenskapen i fråga, och det finns
TMer vars accepterade strängar inte har
egenskapen.
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Formellt

• ? L TM accepterbara språk vars strängar
  börjar och slutar på samma tecken.

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Now we will prove some non-trivial
properties without using Rices theorem.
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Theorem
For any recursively enumerable language
it is undecidable whether it is empty.
Proof
We will reduce the membership problem to the
problem of deciding whether is empty.
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Membership problem Does machine accept
string ?
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Assume we have the empty language algorithm
YES
empty
Algorithm for empty language problem
NO
not empty
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We will design the membership algorithm
YES
accepts
Algorithm for membership problem
NO
rejects
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First construct machine
When enters a final state, compare original
input string with .
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if and only if
is not empty
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Algorithm for membership problem
1. Construct
2. Determine if is empty
YES then
NO then
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Membership algorithm
NO
YES
Check if
construct
YES
is empty
NO
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We reduced the empty language problem to the
membership problem.
Since the membership problem is undecidable, the
empty language problem is also undecidable.
END OF PROOF
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Decidabilitycontinued
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Theorem
Proof
We will reduce the halting problem to the finite
language problem.
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Assume we have the finite language algorithm
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We will design the halting problem algorithm
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Otherwise accept nothing (finite language).
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Machine for halting problem
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We reduced the finite language problem to the
halting problem.
Since the halting problem is undecidable, the
finite language problem is also undecidable.
END OF PROOF
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Theorem
Proof
We will reduce the halting problem to the two
strings of equal length- problem.
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Assume we have the two-strings algorithm
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We will design the halting problem algorithm
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Initially, simulates on input .
When enters a halt state, accept symbols
or .
(two equal length strings)
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Algorithm for halting problem
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Machine for halting problem
YES
YES
Check if
construct
NO
NO
has two equal length strings
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We reduced the two strings of equal length -
problem to the halting problem.
Since the halting problem is undecidable, the two
strings of equal length problem is also
undecidable.
END OF PROOF
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Church-Turing Thesis

Source Stanford Encyclopaedia of Philosophy
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A Turing machine is an abstract representation of
a computing device.
It is more like a computer program (software)
than a computer (hardware).
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LCMs Logical Computing Machines Turings
expression for Turing machines were first
proposed by Alan Turing, in an attempt to give
a mathematically precise definition of
"algorithm" or "mechanical procedure".
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• The Church-Turing thesis
• concerns an
• effective or mechanical method
• in logic and mathematics.

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• A method, M, is called effective or
  mechanical just in case
• M is set out in terms of a finite number of exact
  instructions (each instruction being expressed by
  means of a finite number of symbols)
• M will, if carried out without error, always
  produce the desired result in a finite number of
  steps
• M can (in practice or in principle) be carried
  out by a human being unaided by any machinery
  except for paper and pencil
• M demands no insight or ingenuity on the part of
  the human being carrying it out.

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• Turings thesis LCMs logical computing
  machines TMs can do anything that could be
  described as "rule of thumb" or "purely
  mechanical". (Turing 1948)
• He adds This is sufficiently well
  established that it is now agreed amongst
  logicians that "calculable by means of an LCM" is
  the correct accurate rendering of such phrases.

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• Turing introduced this thesis in the course of
  arguing that the Entscheidungsproblem, or
  decision problem, for the predicate calculus -
  posed by Hilbert (1928) - is unsolvable.

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• Churchs account of the Entscheidungsproblem
• By the Entscheidungsproblem of a system of
  symbolic logic is here understood the problem to
  find an effective method by which, given any
  expression Q in the notation of the system, it
  can be determined whether or not Q is provable in
  the system.

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• Churchs thesis A function of positive integers
  is effectively calculable only if recursive.

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Misunderstandings of the Turing Thesis

• Turing did not show that his machines can solve
  any problem that can be solved "by instructions,
  explicitly stated rules, or procedures" and nor
  did he prove that a universal Turing machine "can
  compute any function that any computer, with any
  architecture, can compute".

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• Turing proved that his universal machine can
  compute any function that any Turing machine can
  compute and he put forward, and advanced
  philosophical arguments in support of, the thesis
  here called Turings thesis.

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• A thesis concerning the extent of effective
  methods - procedures that a human being unaided
  by machinery is capable of carrying out - has no
  implication concerning the extent of the
  procedures that machines are capable of carrying
  out, even machines acting in accordance with
  explicitly stated rules.

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• Among a machines repertoire of atomic operations
  there may be those that no human being unaided by
  machinery can perform.

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• Turing introduces his machines as an idealised
  description of a certain human activity, the
  tedious one of numerical computation, which until
  the advent of automatic computing machines was
  the occupation of many thousands of people in
  commerce, government, and research
  establishments.

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• Turings "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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Computation Beyond Turing Model

• Traditionally, the dynamics of computing
  systems, their unfolding behavior in space and
  time has been a mere means to the end of
  computing the function which specifies the
  algorithmic problem which the system is solving.
  In much of contemporary computing, the situation
  is reversed the purpose of the computing system
  is to exhibit certain behaviour. () We need a
  theory of the dynamics of informatic processes,
  of interaction, and information flow, as a basis
  for answering such fundamental questions as What
  is computed? What is a process? What are the
  analogues to Turing completeness and universality
  when we are concerned with processes and their
  behaviours, rather than the functions which they
  compute? (Abramsky, 2008)
• Abramsky, S. (2008) Information, processes and
  games. In Philosophy of Information van Benthem,
  J., Adriaans, P., Eds. North Holland Amsterdam,
  The Netherlands pp. 483549.

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Computation Beyond Turing Model

• According to Abramsky, there is the need for
  second generation models of computation, and in
  particular there is the need for process models
  such as Petri nets, Process Algebra, and similar.
  
• The first generation models of computation were
  originating from problems of formalization of
  mathematics and logic, while processes or agents,
  interaction, and information flow are genuine
  product of the modern development of computing.
• In the second generation models of computation,
  previous isolated systems with limited
  interactions with the environment are replaced by
  processes or agents for which the interactions
  with each other and with the environment are
  fundamental.

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Computation Beyond Turing Model

• As a result of interactions among agents and with
  the environment, complex behavior emerges.
• The basic building block of this interactive
  approach is the agent, and the fundamental
  operation is interaction.
• The ideal features sought for are computational
  qualities of computational agents such as
  biological organisms such as resilience and
  self- capabilities.
• This approach works at both macro-scale (such as
  processes in operating systems, software agents
  on the Internet, transactions, etc.) and on
  micro-scale (program implementation down to
  hardware).

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 Interaction Conjectures, Results, and Myths
Additional reading

• Dina GoldinUniv. of Connecticut, Brown
  Universityhttp//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
  output
• input 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 data
• Church-Turing thesis Turing Machines capture
  this (algorithmic) notion of computation

Mathematical worldview All computable problems
are function-based.
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The Mathematical Worldview

• 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

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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, its
useless but arent OSs useful??
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Rethinking Shared Wisdom(what do computers do?)

• 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
• Wegners conjecture Interaction is more powerful
  than algorithms

• computation finite transformation of input to
  output
• input 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 data
• Church-Turing thesis Turing Machines capture
  this (algorithmic) notion of computation

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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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Driving home from work (cont.)
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
thats detailed enough?
Worse yet the domain is dynamic. The output
depends on weather conditions, and on other
drivers and pedestrians. We cant possibly be
expected to predict that in advance!
Nevertheless the problem is solvable! Google
autonomous vehicle research
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Driving home from work (cont.)
The problem is solvable interactively. Interactiv
e 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.
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Outline

• 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

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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
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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.

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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.

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Origins of the Church-Turing Thesis Myth

• A TM can do anything that a computer can do.
• Based on several claims
• A problem is solvable if there exists a Turing
  Machine for computing it.
• A problem is solvable if it can be specified by
  an algorithm.
• Algorithms are what computers do.
• Each claim is correct in isolation
• provided we understand the underlying assumptions
• Together, they induce an incorrect conclusion
• TMs solvable problems algorithms computation

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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.

181
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. Euclids 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. Knuth68

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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. RiceRice69
• An algorithm is a collection of simple
  instructions for carrying out some task.
  Commonplace in everyday life, algorithms
  sometimes are called procedures or recipes...
  Sipser97

183
Outline

• Rethinking the mathematical worldview
• Persistent Turing Machines (PTMs)
• PTM expressiveness
• Sequential Interaction
• The Myth of the Church-Turing Thesis
• Conclusions and future work

184
The Shift to Interaction in CS
Algorithmic
Interactive
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 systems
Compositional behavior Emergent behavior
Rule-based reasoning Simulation, control, semi-Markov processes
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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

186
Modeling Interactive Computation PTMs in
Perspective

• 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.

187
Future Work 3 conjectures

• Theory of Sequential Interaction
• conjecture 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 Wegner97
• 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

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Referenceshttp//www.cse.uconn.edu/dqg/papers/
Wegner97 Peter WegnerWhy Interaction is more
Powerful than AlgorithmsCommunications of the
ACM, May 1997 EGW04 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 IC04 Dina Goldin, Scott Smolka, Paul
Attie, Elaine SondereggerTuring Machines,
Transition Systems, and InteractionInformation
Computation Journal, 2004 GW04 Dina Goldin,
Peter WegnerThe Church-Turing Thesis Breaking
the Mythpresented at CiE 2005, Amsterdam, June
2005 to be published in LNCS