Theory of Computation
計算理論
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Instructor: 顏嗣鈞
E-mail: yen@ee.ntu.edu.tw
Web: http://www.ee.ntu.edu.tw/~yen
Time: 9:10-12:10 PM, Monday
Place: BL 103
Office hours: by appointment
Class web page:
http://www.ee.ntu.edu.tw/~yen/courses/TOC-2008.htm
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Textbook
Automata,
Computability and
Complexity: Theory
and Applications
by Elaine A. Rich
Publisher: Pearson
Pub. Date: October 2007
ISBN-13: 9780132288064
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Reference
•
:
Introduction to Automata
Theory, Languages, and
Computation
John E. Hopcroft,
Rajeev Motwani,
Jeffrey D. Ullman,
(2nd Ed. Addison-Wesley, 2001)
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1st Edition
Introduction to
Automata Theory,
Languages, and
Computation
John E. Hopcroft,
Jeffrey D. Ullman,
(Addison-Wesley, 1979)
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•
:
3rd Edition
Introduction to
Automata Theory,
Languages, and
Computation
John E. Hopcroft,
Rajeev Motwani,
Jeffrey D. Ullman,
(Addison-Wesley, 2006)
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Grading
HW/Project : 20%
Midterm exam.: 40%
Final exam.: 40%
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Why Study Automata Theory?
Finite automata are a useful model for
important kinds of hardware and software:
• Software for designing and checking digital
circuits.
• Lexical analyzer of compilers.
• Finding words and patterns in large bodies of
text, e.g. in web pages.
• Verification of systems with finite number of
states, e.g. communication protocols.
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Why Study Automata Theory? (2)
The study of Finite Automata and Formal
Languages are intimately connected.
Methods for specifying formal languages
are very important in many areas of CS,
e.g.:
• Context Free Grammars are very useful when
designing software that processes data with
recursive structure, like the parser in a compiler.
• Regular Expressions are very useful for
specifying lexical aspects of programming
languages and search patterns.
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Why Study Automata Theory? (3)
Automata are essential for the study of
the limits of computation. Two issues:
• What can a computer do at all?
(Decidability)
• What can a computer do efficiently?
(Intractability)
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Applications
Compiler
Prog. languages
Comm. protocols
circuits
Pattern
recognition
Supervisory
control
Quantum
computing
Computer-Aided
Verification
...
Theoretical Computer Science
Automata Theory, Formal Languages,
Computability, Complexity …
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Aims of the Course
•
To familiarize you with key Computer Science
concepts in central areas like
- Automata Theory
- Formal Languages
- Models of Computation
- Complexity Theory
•
To equip you with tools with wide applicability
in the fields of CS and EE, e.g. for
- Complier Construction
- Text Processing
- XML
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Fundamental Theme
• What are the capabilities and
limitations of computers and computer
programs?
– What can we do with
computers/programs?
– Are there things we cannot do with
computers/programs?
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Studying the Theme
• How do we prove something CAN be done
by SOME program?
• How do we prove something CANNOT be
done by ANY program?
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Example: The Halting Problem (1)
Consider the following program. Does it
terminate for all values of n  1?
while (n > 1) {
if even(n) {
n = n / 2;
} else {
n = n * 3 + 1;
}
}
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Example: The Halting Problem (2)
Not as easy to answer as it might first seem.
Say we start with n = 7, for example:
7, 22, 11, 34, 17, 52, 26, 13, 40, 20, 10, 5,
16, 8, 4, 2, 1
In fact, for all numbers that have been tried
(a lot!), it does terminate . . .
. . . but in general?
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Example: The Halting Problem (3)
Then the following important undecidability
result should perhaps not come as a total
surprise:
It is impossible to write a program that
decides if another, arbitrary, program
terminates (halts) or not.
What might be surprising is that it is possible
to prove such a result. This was first done
by the British mathematician Alan Turing.
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Our focus
Automata
Computability
Complexity
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Topics
1.
Finite automata, Regular languages, Regular
grammars: deterministic vs. nondeterministic,
one-way vs. two-way finite automata,
minimization, pumping lemma for regular
sets, closure properties.
2. Pushdown automata, Context-free languages,
Context-free grammars: deterministic vs.
nondeterministic, one-way vs. two-way PDAs,
reversal bounded PDAs, linear grammars, counter
machines, pumping lemma for CFLs, Chomsky
normal form, Greibach normal form, closure
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properties.
Topics (cont’d)
3. Linear
bounded automata, Contextsensitive languages, Context-sensitive
grammars.
4. Turing machines, Recursively
enumerable sets, Type 0 grammars:
variants of Turing machines, halting
problem, undecidability, Post
correspondence problem, valid and
invalid computations of TMs.
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Topics (cont’d)
5. Basic recursive function theory
6. Basic complexity theory: Various
resource bounded complexity classes,
including NLOGSPACE, P, NP,
PSPACE, EXPTIME, and many more.
reducibility, completeness.
7. Advanced topics: Tree Automata,
quantum automata, probabilistic
automata, interactive proof systems,
oracle computations, cryptography.
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Who should take this course?
YOU
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Languages
The terms language and word are used in
a strict technical sense in this course:
A language is a set of words.
A word is a sequence (or string) of
symbols.
 (or ) denotes the empty word, the
sequence of zero symbols.
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Symbols and Alphabets
• What is a symbol, then?
• Anything, but it has to come from an
alphabet which is a finite set.
• A common (and important) instance is
= {0, 1}.
•  , the empty word, is never an symbol
of an alphabet.
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Computation
CPU
memory
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temporary memory
input memory
CPU
output memory
Program memory
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Example:
f ( x)  x
3
temporary memory
input memory
CPU
output memory
Program memory
compute
xx
compute
x x
2
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f ( x)  x
temporary memory
3
input memory
x2
CPU
output memory
Program memory
compute
xx
compute
x x
2
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temporary memory
z  2*2  4
f ( x)  z * 2  8
f ( x)  x
3
input memory
x2
CPU
output memory
Program memory
compute
xx
compute
x x
2
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temporary memory
z  2*2  4
f ( x)  z * 2  8
f ( x)  x
3
input memory
x2
CPU
Program memory
compute
xx
compute
x x
f ( x)  8
output memory
2
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Automaton
temporary memory
Automaton
input memory
CPU
output memory
Program memory
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Different Kinds of Automata
Automata are distinguished by the temporary memory
• Finite Automata:
no temporary memory
• Pushdown Automata:
stack
• Turing Machines:
random access memory
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Finite Automaton
temporary memory
Finite
Automaton
input memory
output memory
Example: Vending Machines
(small computing power)
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Pushdown Automaton
Stack
Pushdown
Automaton
Push, Pop
input memory
output memory
Example: Compilers for Programming Languages
(medium computing power)
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Turing Machine
Random Access Memory
Turing
Machine
input memory
output memory
Examples: Any Algorithm
(highest computing power)
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Power of Automata
Finite
Pushdown
Automata
Turing
Automata
Machine
Less power
More power
Solve more
computational problems
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