- series
- Prentice Hall
- author
- John E. Hopcroft / Rajeev Motwani / Jeffrey D. Ullman
- publisher
- Pearson
- cover
- Softcover
- edition
- 3
- language
- English
- pub.-date
- Mai 2007
- ISBN13
- 9780321514486
- ISBN
- 0321514483
- Related Titles

title no longer available

ISBN | product | product | edition | cover | date | price CHF | Available |
---|---|---|---|---|---|---|---|

Introduction to Automata Theory, Languages, and Computation | 9780321455369 Introduction to Automata Theory, Languages, and Computation |
3 | Hardcover | August 2006 | 183.80 | ||

Introduction to Automata Theory, Languages, and Computation: Pearson New International Edition | 9781292039053 Introduction to Automata Theory, Languages, and Computation: Pearson New International Edition |
3 | Softcover | November 2013 | 88.70 |

This classic book on formal languages, automata theory, and computational complexity has been updated to present theoretical concepts in a concise and straightforward manner with the increase of hands-on, practical applications. This new edition comes with Gradiance, an online assessment tool developed for computer science.

Gradiance is the most advanced online assessment tool developed for the computer science discipline. With its innovative underlying technology, Gradiance turns basic homework assignments and programming labs into an interactive learning experience for students. By using a series of “root questions” and hints, it not only tests a student's capability, but actually simulates a one-on-one teacher-student tutorial that allows for the student to more easily learn the material. Through the programming labs, instructors are capable of testing, tracking, and honing their students' skills, both in terms of syntax and semantics, with an unprecedented level of assessment never before offered.

For more information about Gradiance, please visit www.aw.com/gradiance.

- Presents theoretical concepts in a concise and accessible style.
- Emphasizes modern applications of the theory
- Uses numerous figures to help convey ideas
- Provides more detail and intuition for definitions and proofs
- Challenges readers with extensive exercises at various levels of difficulty at the end of each chapter
- Includes additional practice and tests comprehension of important concepts with
an online homework and tutorial system*Gradiance*

Gradiance is the most advanced online assessment tool developed for the computer science discipline. With its innovative underlying technology, Gradiance turns basic homework assignments and programming labs into an interactive learning experience for students. By using a series of “root questions” and hints, it not only tests a student's capability, but actually simulates a one-on-one teacher-student tutorial that allows for the student to more easily learn the material. Through the programming labs, instructors are capable of testing, tracking, and honing their students' skills, both in terms of syntax and semantics, with an unprecedented level of assessment never before offered. For more information about Gradiance, please visit www.aw.com/gradiance.

- Provides more detail and intuition for definitions and proofs
- Challenges readers with extensive exercises at various levels of difficulty at the end of each chapter
- Includes additional practice and tests comprehension of important concepts with
an online homework and tutorial system*Gradiance*

Gradiance is the most advanced online assessment tool developed for the computer science discipline. With its innovative underlying technology, Gradiance turns basic homework assignments and programming labs into an interactive learning experience for students. By using a series of “root questions” and hints, it not only tests a student's capability, but actually simulates a one-on-one teacher-student tutorial that allows for the student to more easily learn the material. Through the programming labs, instructors are capable of testing, tracking, and honing their students' skills, both in terms of syntax and semantics, with an unprecedented level of assessment never before offered. For more information about Gradiance, please visit www.aw.com/gradiance.

Table of Contents

**Chapter 1 - Automata: The Methods and the Madness**

1.1 Why Study Automata Theory

1.2 Introduction to Formal Proof

1.3 Additional Forms of Proof

1.4 Inductive Proofs

1.5 The Central Concepts of Automata Theory

1.6 Summary of Chapter 1

1.7 Gradiance Problems for Chapter 1

1.8 References for Chapter 1

**Chapter 2 - Finite Automata**

2.1 An Informal Picture of Finite Automata

2.2 Deterministic Finite Automata

2.3 Nondeterministic Finite Automata

2.4 An Application_ Text Search

2.5 Finite Automata With EpsilonTransitions

2.6 Summary of Chapter 2

2.7 Gradiance Problems for Chapter 2

2.8 References for Chapter 2

**Chapter 3 - Regular Expressions and Languages**

3.1 Regular Expressions

3.2 Finite Automata and Regular Expressions

3.3 Applications of Regular Expressions

3.4 Algebraic Laws for Regular Expressions

3.5 Summary of Chapter 3

3.6 Gradiance Problems for Chapter 3

3.7 References for Chapter 3

**Chapter 4 - Properties of Regular Languages**

4.1 Proving Languages Not to Be Regular

4.2 Closure Properties of Regular Languages

4.3 Decision Properties of Regular Languages

4.4 Equivalence and Minimization of Automata

4.5 Summary of Chapter 4

4.6 Gradiance Problems for Chapter 4

4.7 References for Chapter 4

**Chapter 5 - Context-Free Grammars and Languages**

5.1 Context-Free Grammars

5.2 Parse Trees

5.3 Applications of Context-Free Grammars

5.4 Ambiguity in Grammars and Languages

5.5 Summary of Chapter 5

5.6 Gradiance Problems for Chapter 5

5.7 References for Chapter 5

**Chapter 6 - Pushdown Automata**

6.1 Definition of the Pushdown Automaton

6.2 The Languages of a PDA

6.3 Equivalence of PDA's and CFG's

6.4 Deterministic Pushdown Automata

6.5 Summary of Chapter 6

6.6 Gradiance Problems for Chapter 6

6.7 References for Chapter 6

**Chapter 7 - Properties of Context-Free Languages**

7.1 Normal Forms for Context-Free Grammars

7.2 The Pumping Lemma for Context-Free Languages

7.3 Closure Properties of Context-Free Languages

7.4 Decision Properties of CFL's

7.5 Summary of Chapter 7

7.6 Gradiance Problems for Chapter 7

7.7 References for Chapter 7

**Chapter 8 - Introduction to Turing Machines**

8.1 Problems That Computers Cannot Solve

8.2 The Turing Machine

8.3 Programming Techniques for Turing Machines

8.4 Extensions to the Basic Turing Machine

8.5 Restricted Turing Machines

8.6 Turing Machines and Computers

8.7 Summary of Chapter 8

8.8 Gradiance Problems for Chapter 8

8.9 References for Chapter 8

**Chapter 9 - Undecidability**

9.1 A Language That Is Not Recursively Enumerable

9.2 An Undecidable Problem That Is RE

9.3 Undecidable Problems About Turing Machines

9.4 Post's Correspondence Problem

9.5 Other Undecidable Problems

9.6 Summary of Chapter 9

9.7 Gradiance Problems for Chapter 9

9.8 References for Chapter 9

**Chapter 10 - Intractable Problems**

10.1 The Classes P and NP

10.2 An NP-Complete Problem

10.3 A Restricted Satisfiability Problem

10.4 Additional NP-Complete Problems

10.5 Summary of Chapter 10

10.6 Gradiance Problems for Chapter 10

10.7 References for Chapter 10

**Chapter 11 - Additional Classes of Problems**

11.1 Complements of Languages in NP

11.2 Problems Solvable in Polynomial Space

11.3 A Problem That Is Complete for PS

11.4 Language Classes Based on Randomization

11.5 The Complexity of Primality Testing

11.6 Summary of Chapter 11

11.7 Gradiance Problems for Chapter 11

11.8 References for Chapter 11

**Index**