Pushdown Automata. October 14, Pushdown Automata

Size: px
Start display at page:

Download "Pushdown Automata. October 14, Pushdown Automata"

Transcription

1 October 14, 2015

2 Rationale

3 Rationale CFG are specification mechanisms, i.e., a CFG generates a context-free language.

4 Rationale CFG are specification mechanisms, i.e., a CFG generates a context-free language. Pushdown-automata are recognizing mechanisms, i.e., a PDA recognizes a context-free language.

5 Rationale CFG are specification mechanisms, i.e., a CFG generates a context-free language. Pushdown-automata are recognizing mechanisms, i.e., a PDA recognizes a context-free language. CFG are like regular expressions and PDA are like FA.

6 A new type of computation model

7 A new type of computation model Pushdown automata, PDA, are a new type of computation model

8 A new type of computation model Pushdown automata, PDA, are a new type of computation model PDAs are like NFAs but have an extra component called a stack

9 A new type of computation model Pushdown automata, PDA, are a new type of computation model PDAs are like NFAs but have an extra component called a stack The stack provides additional memory beyond the finite amount available in the control

10 A new type of computation model Pushdown automata, PDA, are a new type of computation model PDAs are like NFAs but have an extra component called a stack The stack provides additional memory beyond the finite amount available in the control The stack allows PDA to recognize some nonregular languages

11 PDA and CFG

12 PDA and CFG PDA are equivalent in specification power with CFG

13 PDA and CFG PDA are equivalent in specification power with CFG This is useful because it gives us two options for proving that a language is context-free:

14 PDA and CFG PDA are equivalent in specification power with CFG This is useful because it gives us two options for proving that a language is context-free: 1. construct a CFG that generates the language or

15 PDA and CFG PDA are equivalent in specification power with CFG This is useful because it gives us two options for proving that a language is context-free: 1. construct a CFG that generates the language or 2. construct a PDA that recognizes the language

16 Note Some CFL are more easily described in terms of their generators, whereas others are more easily described in terms of their recognizers

17 Schematic representation of a PDA Figure 1 contrasts the schematic representations of PDA and NFA : Schematic of a NFA Finite Control r Input a a b b Schematic of a PDA Input r a a Finite b b Stack Control r/w x y z... Figure 1 : Schematic representations Note: in Figure 1, r stands for read and w stands for write.

18 More on PDA informal

19 More on PDA informal A PDA can write symbols on the stack and read them back later

20 More on PDA informal A PDA can write symbols on the stack and read them back later Writing a symbol pushes down all the other symbols on the stack

21 More on PDA informal A PDA can write symbols on the stack and read them back later Writing a symbol pushes down all the other symbols on the stack The symbol on the top of the stack can be read and removed.

22 More on PDA informal A PDA can write symbols on the stack and read them back later Writing a symbol pushes down all the other symbols on the stack The symbol on the top of the stack can be read and removed. When the top symbol is removed the remaining symbols move up

23 Terminology

24 Terminology Writing a symbol on the stack is referred to as pushing the symbol

25 Terminology Writing a symbol on the stack is referred to as pushing the symbol Removing a symbol from the stack is referred to as popping the symbol

26 Terminology Writing a symbol on the stack is referred to as pushing the symbol Removing a symbol from the stack is referred to as popping the symbol All access to the stack may be done only at the top In other words: a stack is a last-in first-out storage device

27 Benefits of the stack

28 Benefits of the stack A stack can hold an unlimited amount of information

29 Benefits of the stack A stack can hold an unlimited amount of information A PDA can recognize a language like {0 n 1 n n 0} because it can use the stack to remember the number of 0s it has seen

30 PDA recognizing {0 n 1 n n 0} Informal algorithm:

31 PDA recognizing {0 n 1 n n 0} Informal algorithm: 1. Read symbols from the input. As each 0 is read push it onto the stack

32 PDA recognizing {0 n 1 n n 0} Informal algorithm: 1. Read symbols from the input. As each 0 is read push it onto the stack 2. As soon as a 1 is read, pop a 0 off the stack for each 1 read

33 PDA recognizing {0 n 1 n n 0} Informal algorithm: 1. Read symbols from the input. As each 0 is read push it onto the stack 2. As soon as a 1 is read, pop a 0 off the stack for each 1 read 3. If input finishes when stack becomes empty, accept; if stack becomes empty while there is still input or input finishes while stack is not empty, reject

34 Nature of PDA

35 Nature of PDA PDA may be nondeterministic and this is a crucial feature because nondeterminism adds power

36 Nature of PDA PDA may be nondeterministic and this is a crucial feature because nondeterminism adds power Some languages, such as {0 n 1 n n 0} do not require nondeterminism, but other do. Such a language is {ww R w {0, 1} }

37 Toward PDA formalization

38 Toward PDA formalization Formal definition of a PDA is similar to a NFA, except the stack

39 Toward PDA formalization Formal definition of a PDA is similar to a NFA, except the stack PDA my use different alphabets for input and stack, we will denote them by Σ and Γ

40 Toward PDA formalization Formal definition of a PDA is similar to a NFA, except the stack PDA my use different alphabets for input and stack, we will denote them by Σ and Γ Nondeterminism allows PDA to make transitions on empty input. Hence we will use Σ ɛ = Σ {ɛ} and Γ ɛ = Γ {ɛ}

41 Toward PDA formalization Formal definition of a PDA is similar to a NFA, except the stack PDA my use different alphabets for input and stack, we will denote them by Σ and Γ Nondeterminism allows PDA to make transitions on empty input. Hence we will use Σ ɛ = Σ {ɛ} and Γ ɛ = Γ {ɛ} Domain of the PDA transition function is Q Σ ɛ Γ ɛ where Q is the set of states

42 Toward PDA formalization Formal definition of a PDA is similar to a NFA, except the stack PDA my use different alphabets for input and stack, we will denote them by Σ and Γ Nondeterminism allows PDA to make transitions on empty input. Hence we will use Σ ɛ = Σ {ɛ} and Γ ɛ = Γ {ɛ} Domain of the PDA transition function is Q Σ ɛ Γ ɛ where Q is the set of states Since a PDA can write on the stack while performing nondeterministic transitions the range of the PDA transition function is P(Q Γ ɛ)

43 Toward PDA formalization Formal definition of a PDA is similar to a NFA, except the stack PDA my use different alphabets for input and stack, we will denote them by Σ and Γ Nondeterminism allows PDA to make transitions on empty input. Hence we will use Σ ɛ = Σ {ɛ} and Γ ɛ = Γ {ɛ} Domain of the PDA transition function is Q Σ ɛ Γ ɛ where Q is the set of states Since a PDA can write on the stack while performing nondeterministic transitions the range of the PDA transition function is P(Q Γ ɛ) In conclusion: δ : Q Σ ɛ Γ ɛ P(Q Γ ɛ)

44 Definition 2.8 A pushdown automaton is a 6-tuple (Q, Σ, Γ, δ, q 0, F ) where Q, Σ, Γ, and F are finite sets, and:

45 Definition 2.8 A pushdown automaton is a 6-tuple (Q, Σ, Γ, δ, q 0, F ) where Q, Σ, Γ, and F are finite sets, and: 1. Q is a set of states

46 Definition 2.8 A pushdown automaton is a 6-tuple (Q, Σ, Γ, δ, q 0, F ) where Q, Σ, Γ, and F are finite sets, and: 1. Q is a set of states 2. Σ is the input alphabet

47 Definition 2.8 A pushdown automaton is a 6-tuple (Q, Σ, Γ, δ, q 0, F ) where Q, Σ, Γ, and F are finite sets, and: 1. Q is a set of states 2. Σ is the input alphabet 3. Γ is the stack alphabet

48 Definition 2.8 A pushdown automaton is a 6-tuple (Q, Σ, Γ, δ, q 0, F ) where Q, Σ, Γ, and F are finite sets, and: 1. Q is a set of states 2. Σ is the input alphabet 3. Γ is the stack alphabet 4. δ : Q Σ ɛ Γ ɛ P(Q Γ ɛ) is the transition function

49 Definition 2.8 A pushdown automaton is a 6-tuple (Q, Σ, Γ, δ, q 0, F ) where Q, Σ, Γ, and F are finite sets, and: 1. Q is a set of states 2. Σ is the input alphabet 3. Γ is the stack alphabet 4. δ : Q Σ ɛ Γ ɛ P(Q Γ ɛ) is the transition function 5. q 0 Q is the start state

50 Definition 2.8 A pushdown automaton is a 6-tuple (Q, Σ, Γ, δ, q 0, F ) where Q, Σ, Γ, and F are finite sets, and: 1. Q is a set of states 2. Σ is the input alphabet 3. Γ is the stack alphabet 4. δ : Q Σ ɛ Γ ɛ P(Q Γ ɛ) is the transition function 5. q 0 Q is the start state 6. F Q is the set of accept states

51 PDA computation A PDA M = (Q, Σ, Γ, δ, q 0, F ) computes as follows:

52 PDA computation A PDA M = (Q, Σ, Γ, δ, q 0, F ) computes as follows: M inputs w = w 1 w 2... w m, where each w i Σ ɛ

53 PDA computation A PDA M = (Q, Σ, Γ, δ, q 0, F ) computes as follows: M inputs w = w 1 w 2... w m, where each w i Σ ɛ There are a sequence of states r 0, r 1,..., r m Q and a sequence of strings s 0, s 1,..., s m Γ that satisfy the conditions:

54 PDA computation A PDA M = (Q, Σ, Γ, δ, q 0, F ) computes as follows: M inputs w = w 1 w 2... w m, where each w i Σ ɛ There are a sequence of states r 0, r 1,..., r m Q and a sequence of strings s 0, s 1,..., s m Γ that satisfy the conditions: 1. r 0 = q 0,s 0 = ɛ, i.e., M starts in the start state with empty stack

55 PDA computation A PDA M = (Q, Σ, Γ, δ, q 0, F ) computes as follows: M inputs w = w 1 w 2... w m, where each w i Σ ɛ There are a sequence of states r 0, r 1,..., r m Q and a sequence of strings s 0, s 1,..., s m Γ that satisfy the conditions: 1. r 0 = q 0,s 0 = ɛ, i.e., M starts in the start state with empty stack 2. For i = 0, 1,..., m 1 we have (r i+1, b) δ(r i, w i+1, a) where s i = at and s i+1 = bt for some a, b Γ ɛ and t Γ, i.e., M moves properly according to the state, stack, and input symbol

56 PDA computation A PDA M = (Q, Σ, Γ, δ, q 0, F ) computes as follows: M inputs w = w 1 w 2... w m, where each w i Σ ɛ There are a sequence of states r 0, r 1,..., r m Q and a sequence of strings s 0, s 1,..., s m Γ that satisfy the conditions: 1. r 0 = q 0,s 0 = ɛ, i.e., M starts in the start state with empty stack 2. For i = 0, 1,..., m 1 we have (r i+1, b) δ(r i, w i+1, a) where s i = at and s i+1 = bt for some a, b Γ ɛ and t Γ, i.e., M moves properly according to the state, stack, and input symbol 3. r m F, i.e., an accept state occurs at the input end

57 Example PDA The PDA that recognizes the language {0 n 1 n n 0} is M 1 = (Q, Σ, Γ, δ, q 1, F ) where: Q = {q 1, q 2, q 3, q 4 }, Σ = {0, 1}, Γ = {0, $}, F = {q 1, q 4 } δ is defined by the table: Σ ɛ 0 1 ɛ Q/Γ ɛ 0 $ ɛ 0 $ ɛ 0 $ ɛ q 1 {(q 2, $)} q 2 {(q 2, 0)} {(q 3, ɛ)} q 3 {(q 3, ɛ)} {(q 4, ɛ)} q 4 Notation: δ(q Q, w Σ ɛ, a Γ ɛ) = {(r Q, b Γ ɛ)} means q w,a b (r, b).

58 Transition diagrams of PDA

59 Transition diagrams of PDA We can use state transition diagrams to describe PDA

60 Transition diagrams of PDA We can use state transition diagrams to describe PDA Such diagrams are similar to state transition diagrams used to describe finite automata

61 Transition diagrams of PDA We can use state transition diagrams to describe PDA Such diagrams are similar to state transition diagrams used to describe finite automata To show how PDA uses the stack we write a, b c to signify that the machine:

62 Transition diagrams of PDA We can use state transition diagrams to describe PDA Such diagrams are similar to state transition diagrams used to describe finite automata To show how PDA uses the stack we write a, b c to signify that the machine: 1. is reading a from input

63 Transition diagrams of PDA We can use state transition diagrams to describe PDA Such diagrams are similar to state transition diagrams used to describe finite automata To show how PDA uses the stack we write a, b c to signify that the machine: 1. is reading a from input 2. may replace the symbol b on top of the stack

64 Transition diagrams of PDA We can use state transition diagrams to describe PDA Such diagrams are similar to state transition diagrams used to describe finite automata To show how PDA uses the stack we write a, b c to signify that the machine: 1. is reading a from input 2. may replace the symbol b on top of the stack 3. with the symbol c

65 Transition diagrams of PDA We can use state transition diagrams to describe PDA Such diagrams are similar to state transition diagrams used to describe finite automata To show how PDA uses the stack we write a, b c to signify that the machine: 1. is reading a from input 2. may replace the symbol b on top of the stack 3. with the symbol c where any of a, b, c may be ɛ

66 Interpretation If a is ɛ, the machine makes this transaction without reading any symbol from the input If b is ɛ, the machine makes this transaction without popping any symbol from the stack If c is ɛ, the machine makes this transaction without pushing any symbol on the stack

67 Transition diagram of PDA M 1 Figure 2 show the state transition diagram of M 1 recognizing the language {0 n 1 n n 0} 0, ɛ 0 ɛ, ɛ $ q 1 q 2 q 4 ɛ, $ ɛ q 3 Figure 2 : Transition diagram for PDA M 1 1, 0 ɛ 1, 0 ɛ

68 Note 1 The formal definition of a PDA contains no explicit mechanism for testing the empty stack PDA in Figure 2 test empty stack by initially placing a special symbol, $, on the stack If ever it sees $ again on the stack, it knows that the stack is effectively empty This is the procedure we use to test empty stack

69 Note 2 The PDA has no mechanism to test explicitly the reaching of end of the input The PDA in Figure 2 is able to achieve this effect because the accept state takes effect only when the machine is at the end of the output Thus, from now on we assume that PDA can test for end of input, and we know that we can implement it in the same manner

70 Example 2.10 This example provides the PDA M 2 that recognizes the language {a i b j c k i, j, k 0 and i = j or i = k} Informal description:

71 Example 2.10 This example provides the PDA M 2 that recognizes the language {a i b j c k i, j, k 0 and i = j or i = k} Informal description: The PDA first reads and push a s on the stack

72 Example 2.10 This example provides the PDA M 2 that recognizes the language {a i b j c k i, j, k 0 and i = j or i = k} Informal description: The PDA first reads and push a s on the stack When this is done, it can match as with bs or cs

73 Example 2.10 This example provides the PDA M 2 that recognizes the language {a i b j c k i, j, k 0 and i = j or i = k} Informal description: The PDA first reads and push a s on the stack When this is done, it can match as with bs or cs Since machine does not know in advance whether as are matched with bs or cs, nondeterminism helps

74 Example 2.10 This example provides the PDA M 2 that recognizes the language {a i b j c k i, j, k 0 and i = j or i = k} Informal description: The PDA first reads and push a s on the stack When this is done, it can match as with bs or cs Since machine does not know in advance whether as are matched with bs or cs, nondeterminism helps Using nondeterminism the machine can guess: the machine has two branches, one for each possible guess

75 Example 2.10 This example provides the PDA M 2 that recognizes the language {a i b j c k i, j, k 0 and i = j or i = k} Informal description: The PDA first reads and push a s on the stack When this is done, it can match as with bs or cs Since machine does not know in advance whether as are matched with bs or cs, nondeterminism helps Using nondeterminism the machine can guess: the machine has two branches, one for each possible guess If either of these branches match, that branch accepts and the entire machine accepts

76 M 2 recognizing {a i b j c k i, j, k 0 i = j i = k} The transition diagram is in Figure 3 q 1 ɛ, ɛ ɛ b, a ɛ ɛ, $ ɛ q 3 c, ɛ ɛ q 4 ɛ, ɛ $ ɛ, ɛ ɛ ɛ, ɛ ɛ ɛ, $ ɛ q 2 q 5 q 6 a, ɛ a b, ɛ ɛ c, a ɛ q 7 Figure 3 : Transition diagram of PDA M 2

77 Example 2.11 This example provides the PDA M 3 that recognizes the language {ww R w {0, 1} }. Recall: w R means w written backwards.

78 Informal description

79 Informal description 1. M 3 begins by pushing the symbols that are read onto the stack

80 Informal description 1. M 3 begins by pushing the symbols that are read onto the stack 2. At each point M 3 nondeterministically guesses that the middle of the input has been reached

81 Informal description 1. M 3 begins by pushing the symbols that are read onto the stack 2. At each point M 3 nondeterministically guesses that the middle of the input has been reached 3. When middle of the word has reached the machine starts popping off the stack for each symbol read, checking to see that what is read and what is popped off is the same symbol

82 Informal description 1. M 3 begins by pushing the symbols that are read onto the stack 2. At each point M 3 nondeterministically guesses that the middle of the input has been reached 3. When middle of the word has reached the machine starts popping off the stack for each symbol read, checking to see that what is read and what is popped off is the same symbol 4. If the symbol read from the input and popped out from the stack is the same and the stack empties as the same time as input is finished, accept; otherwise reject

83 Transition diagram of PDA M 3 Figure 4 shows the state transition diagram of M 4 recognizing the language {ww R w {0, 1}} ɛ, ɛ $ q 1 q 2 0, ɛ 0 1, ɛ 1 ɛ, ɛ ɛ 0, 0 ɛ q 3 1, 1 ɛ q 4 ɛ, $ ɛ Figure 4 : Transition diagram for PDA M 3

Pushdown automata. Informatics 2A: Lecture 9. Alex Simpson. 3 October, 2014. School of Informatics University of Edinburgh als@inf.ed.ac.

Pushdown automata. Informatics 2A: Lecture 9. Alex Simpson. 3 October, 2014. School of Informatics University of Edinburgh als@inf.ed.ac. Pushdown automata Informatics 2A: Lecture 9 Alex Simpson School of Informatics University of Edinburgh als@inf.ed.ac.uk 3 October, 2014 1 / 17 Recap of lecture 8 Context-free languages are defined by context-free

More information

Formal Languages and Automata Theory - Regular Expressions and Finite Automata -

Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Samarjit Chakraborty Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology (ETH) Zürich March

More information

Pushdown Automata. place the input head on the leftmost input symbol. while symbol read = b and pile contains discs advance head remove disc from pile

Pushdown Automata. place the input head on the leftmost input symbol. while symbol read = b and pile contains discs advance head remove disc from pile Pushdown Automata In the last section we found that restricting the computational power of computing devices produced solvable decision problems for the class of sets accepted by finite automata. But along

More information

Automata and Computability. Solutions to Exercises

Automata and Computability. Solutions to Exercises Automata and Computability Solutions to Exercises Fall 25 Alexis Maciel Department of Computer Science Clarkson University Copyright c 25 Alexis Maciel ii Contents Preface vii Introduction 2 Finite Automata

More information

Regular Languages and Finite State Machines

Regular Languages and Finite State Machines Regular Languages and Finite State Machines Plan for the Day: Mathematical preliminaries - some review One application formal definition of finite automata Examples 1 Sets A set is an unordered collection

More information

Honors Class (Foundations of) Informatics. Tom Verhoeff. Department of Mathematics & Computer Science Software Engineering & Technology

Honors Class (Foundations of) Informatics. Tom Verhoeff. Department of Mathematics & Computer Science Software Engineering & Technology Honors Class (Foundations of) Informatics Tom Verhoeff Department of Mathematics & Computer Science Software Engineering & Technology www.win.tue.nl/~wstomv/edu/hci c 2011, T. Verhoeff @ TUE.NL 1/20 Information

More information

6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010. Class 4 Nancy Lynch

6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010. Class 4 Nancy Lynch 6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010 Class 4 Nancy Lynch Today Two more models of computation: Nondeterministic Finite Automata (NFAs)

More information

CSC4510 AUTOMATA 2.1 Finite Automata: Examples and D efinitions Definitions

CSC4510 AUTOMATA 2.1 Finite Automata: Examples and D efinitions Definitions CSC45 AUTOMATA 2. Finite Automata: Examples and Definitions Finite Automata: Examples and Definitions A finite automaton is a simple type of computer. Itsoutputislimitedto yes to or no. It has very primitive

More information

Automata Theory. Şubat 2006 Tuğrul Yılmaz Ankara Üniversitesi

Automata Theory. Şubat 2006 Tuğrul Yılmaz Ankara Üniversitesi Automata Theory Automata theory is the study of abstract computing devices. A. M. Turing studied an abstract machine that had all the capabilities of today s computers. Turing s goal was to describe the

More information

Turing Machines: An Introduction

Turing Machines: An Introduction CIT 596 Theory of Computation 1 We have seen several abstract models of computing devices: Deterministic Finite Automata, Nondeterministic Finite Automata, Nondeterministic Finite Automata with ɛ-transitions,

More information

3515ICT Theory of Computation Turing Machines

3515ICT Theory of Computation Turing Machines Griffith University 3515ICT Theory of Computation Turing Machines (Based loosely on slides by Harald Søndergaard of The University of Melbourne) 9-0 Overview Turing machines: a general model of computation

More information

24 Uses of Turing Machines

24 Uses of Turing Machines Formal Language and Automata Theory: CS2004 24 Uses of Turing Machines 24 Introduction We have previously covered the application of Turing Machine as a recognizer and decider In this lecture we will discuss

More information

Introduction to Finite Automata

Introduction to Finite Automata Introduction to Finite Automata Our First Machine Model Captain Pedro Ortiz Department of Computer Science United States Naval Academy SI-340 Theory of Computing Fall 2012 Captain Pedro Ortiz (US Naval

More information

Finite Automata. Reading: Chapter 2

Finite Automata. Reading: Chapter 2 Finite Automata Reading: Chapter 2 1 Finite Automaton (FA) Informally, a state diagram that comprehensively captures all possible states and transitions that a machine can take while responding to a stream

More information

Model 2.4 Faculty member + student

Model 2.4 Faculty member + student Model 2.4 Faculty member + student Course syllabus for Formal languages and Automata Theory. Faculty member information: Name of faculty member responsible for the course Office Hours Office Number Email

More information

Implementation of Recursively Enumerable Languages using Universal Turing Machine in JFLAP

Implementation of Recursively Enumerable Languages using Universal Turing Machine in JFLAP International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 4, Number 1 (2014), pp. 79-84 International Research Publications House http://www. irphouse.com /ijict.htm Implementation

More information

Intrusion Detection via Static Analysis

Intrusion Detection via Static Analysis Intrusion Detection via Static Analysis IEEE Symposium on Security & Privacy 01 David Wagner Drew Dean Presented by Yongjian Hu Outline Introduction Motivation Models Trivial model Callgraph model Abstract

More information

T-79.186 Reactive Systems: Introduction and Finite State Automata

T-79.186 Reactive Systems: Introduction and Finite State Automata T-79.186 Reactive Systems: Introduction and Finite State Automata Timo Latvala 14.1.2004 Reactive Systems: Introduction and Finite State Automata 1-1 Reactive Systems Reactive systems are a class of software

More information

Reading 13 : Finite State Automata and Regular Expressions

Reading 13 : Finite State Automata and Regular Expressions CS/Math 24: Introduction to Discrete Mathematics Fall 25 Reading 3 : Finite State Automata and Regular Expressions Instructors: Beck Hasti, Gautam Prakriya In this reading we study a mathematical model

More information

(IALC, Chapters 8 and 9) Introduction to Turing s life, Turing machines, universal machines, unsolvable problems.

(IALC, Chapters 8 and 9) Introduction to Turing s life, Turing machines, universal machines, unsolvable problems. 3130CIT: Theory of Computation Turing machines and undecidability (IALC, Chapters 8 and 9) Introduction to Turing s life, Turing machines, universal machines, unsolvable problems. An undecidable problem

More information

Properties of Stabilizing Computations

Properties of Stabilizing Computations Theory and Applications of Mathematics & Computer Science 5 (1) (2015) 71 93 Properties of Stabilizing Computations Mark Burgin a a University of California, Los Angeles 405 Hilgard Ave. Los Angeles, CA

More information

CS154. Turing Machines. Turing Machine. Turing Machines versus DFAs FINITE STATE CONTROL AI N P U T INFINITE TAPE. read write move.

CS154. Turing Machines. Turing Machine. Turing Machines versus DFAs FINITE STATE CONTROL AI N P U T INFINITE TAPE. read write move. CS54 Turing Machines Turing Machine q 0 AI N P U T IN TAPE read write move read write move Language = {0} q This Turing machine recognizes the language {0} Turing Machines versus DFAs TM can both write

More information

Automata and Formal Languages. Push Down Automata. Sipser pages 109-114. Lecture 13. Tim Sheard 1

Automata and Formal Languages. Push Down Automata. Sipser pages 109-114. Lecture 13. Tim Sheard 1 Automata and Formal Languages Push Down Automata Sipser pages 109-114 Lecture 13 Tim Sheard 1 Push Down Automata Push Down Automata (PDAs) are ε-nfas with stack memory. Transitions are labeled by an input

More information

The Halting Problem is Undecidable

The Halting Problem is Undecidable 185 Corollary G = { M, w w L(M) } is not Turing-recognizable. Proof. = ERR, where ERR is the easy to decide language: ERR = { x { 0, 1 }* x does not have a prefix that is a valid code for a Turing machine

More information

Regular Expressions and Automata using Haskell

Regular Expressions and Automata using Haskell Regular Expressions and Automata using Haskell Simon Thompson Computing Laboratory University of Kent at Canterbury January 2000 Contents 1 Introduction 2 2 Regular Expressions 2 3 Matching regular expressions

More information

Finite Automata. Reading: Chapter 2

Finite Automata. Reading: Chapter 2 Finite Automata Reading: Chapter 2 1 Finite Automata Informally, a state machine that comprehensively captures all possible states and transitions that a machine can take while responding to a stream (or

More information

SRM UNIVERSITY FACULTY OF ENGINEERING & TECHNOLOGY SCHOOL OF COMPUTING DEPARTMENT OF SOFTWARE ENGINEERING COURSE PLAN

SRM UNIVERSITY FACULTY OF ENGINEERING & TECHNOLOGY SCHOOL OF COMPUTING DEPARTMENT OF SOFTWARE ENGINEERING COURSE PLAN Course Code : CS0355 SRM UNIVERSITY FACULTY OF ENGINEERING & TECHNOLOGY SCHOOL OF COMPUTING DEPARTMENT OF SOFTWARE ENGINEERING COURSE PLAN Course Title : THEORY OF COMPUTATION Semester : VI Course : June

More information

Deterministic Finite Automata

Deterministic Finite Automata 1 Deterministic Finite Automata Definition: A deterministic finite automaton (DFA) consists of 1. a finite set of states (often denoted Q) 2. a finite set Σ of symbols (alphabet) 3. a transition function

More information

Composability of Infinite-State Activity Automata*

Composability of Infinite-State Activity Automata* Composability of Infinite-State Activity Automata* Zhe Dang 1, Oscar H. Ibarra 2, Jianwen Su 2 1 Washington State University, Pullman 2 University of California, Santa Barbara Presented by Prof. Hsu-Chun

More information

Using Hands-On Visualizations to Teach Computer Science from Beginning Courses to Advanced Courses

Using Hands-On Visualizations to Teach Computer Science from Beginning Courses to Advanced Courses Using Hands-On Visualizations to Teach Computer Science from Beginning Courses to Advanced Courses Susan H. Rodger Department of Computer Science Duke University Durham, NC 27705 rodger@cs.duke.edu Abstract

More information

Computer Architecture Syllabus of Qualifying Examination

Computer Architecture Syllabus of Qualifying Examination Computer Architecture Syllabus of Qualifying Examination PhD in Engineering with a focus in Computer Science Reference course: CS 5200 Computer Architecture, College of EAS, UCCS Created by Prof. Xiaobo

More information

Lecture I FINITE AUTOMATA

Lecture I FINITE AUTOMATA 1. Regular Sets and DFA Lecture I Page 1 Lecture I FINITE AUTOMATA Lecture 1: Honors Theory, Spring 02, Yap We introduce finite automata (deterministic and nondeterministic) and regular languages. Some

More information

Data Structures and Algorithms V22.0102. Otávio Braga

Data Structures and Algorithms V22.0102. Otávio Braga Data Structures and Algorithms V22.0102 Otávio Braga We use a stack When an operand is read, output it When an operator is read Pop until the top of the stack has an element of lower precedence Then push

More information

Lecture summary for Theory of Computation

Lecture summary for Theory of Computation Lecture summary for Theory of Computation Sandeep Sen 1 January 8, 2015 1 Department of Computer Science and Engineering, IIT Delhi, New Delhi 110016, India. E- mail:ssen@cse.iitd.ernet.in Contents 1 The

More information

Fast nondeterministic recognition of context-free languages using two queues

Fast nondeterministic recognition of context-free languages using two queues Fast nondeterministic recognition of context-free languages using two queues Burton Rosenberg University of Miami Abstract We show how to accept a context-free language nondeterministically in O( n log

More information

Automata and Formal Languages

Automata and Formal Languages Automata and Formal Languages Winter 2009-2010 Yacov Hel-Or 1 What this course is all about This course is about mathematical models of computation We ll study different machine models (finite automata,

More information

On Winning Conditions of High Borel Complexity in Pushdown Games

On Winning Conditions of High Borel Complexity in Pushdown Games Fundamenta Informaticae (2005) 1 22 1 IOS Press On Winning Conditions of High Borel Complexity in Pushdown Games Olivier Finkel Equipe de Logique Mathématique U.F.R. de Mathématiques, Université Paris

More information

Increasing Interaction and Support in the Formal Languages and Automata Theory Course

Increasing Interaction and Support in the Formal Languages and Automata Theory Course Increasing Interaction and Support in the Formal Languages and Automata Theory Course [Extended Abstract] Susan H. Rodger rodger@cs.duke.edu Jinghui Lim Stephen Reading ABSTRACT The introduction of educational

More information

6.080 / 6.089 Great Ideas in Theoretical Computer Science Spring 2008

6.080 / 6.089 Great Ideas in Theoretical Computer Science Spring 2008 MIT OpenCourseWare http://ocw.mit.edu 6.080 / 6.089 Great Ideas in Theoretical Computer Science Spring 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

Turing Machines, Part I

Turing Machines, Part I Turing Machines, Part I Languages The $64,000 Question What is a language? What is a class of languages? Computer Science Theory 2 1 Now our picture looks like Context Free Languages Deterministic Context

More information

CS 341: Foundations of Computer Science II elearning Section Syllabus, Spring 2015

CS 341: Foundations of Computer Science II elearning Section Syllabus, Spring 2015 CS 341: Foundations of Computer Science II elearning Section Syllabus, Spring 2015 Course Info Instructor: Prof. Marvin K. Nakayama Office: GITC 4312 Phone: 973-596-3398 E-mail: marvin@njit.edu (Be sure

More information

CS 3719 (Theory of Computation and Algorithms) Lecture 4

CS 3719 (Theory of Computation and Algorithms) Lecture 4 CS 3719 (Theory of Computation and Algorithms) Lecture 4 Antonina Kolokolova January 18, 2012 1 Undecidable languages 1.1 Church-Turing thesis Let s recap how it all started. In 1990, Hilbert stated a

More information

CS5236 Advanced Automata Theory

CS5236 Advanced Automata Theory CS5236 Advanced Automata Theory Frank Stephan Semester I, Academic Year 2012-2013 Advanced Automata Theory is a lecture which will first review the basics of formal languages and automata theory and then

More information

Today s Agenda. Automata and Logic. Quiz 4 Temporal Logic. Introduction Buchi Automata Linear Time Logic Summary

Today s Agenda. Automata and Logic. Quiz 4 Temporal Logic. Introduction Buchi Automata Linear Time Logic Summary Today s Agenda Quiz 4 Temporal Logic Formal Methods in Software Engineering 1 Automata and Logic Introduction Buchi Automata Linear Time Logic Summary Formal Methods in Software Engineering 2 1 Buchi Automata

More information

Overview of E0222: Automata and Computability

Overview of E0222: Automata and Computability Overview of E0222: Automata and Computability Deepak D Souza Department of Computer Science and Automation Indian Institute of Science, Bangalore. August 3, 2011 What this course is about What we study

More information

Theory of Computation Chapter 2: Turing Machines

Theory of Computation Chapter 2: Turing Machines Theory of Computation Chapter 2: Turing Machines Guan-Shieng Huang Feb. 24, 2003 Feb. 19, 2006 0-0 Turing Machine δ K 0111000a 01bb 1 Definition of TMs A Turing Machine is a quadruple M = (K, Σ, δ, s),

More information

6.080/6.089 GITCS Feb 12, 2008. Lecture 3

6.080/6.089 GITCS Feb 12, 2008. Lecture 3 6.8/6.89 GITCS Feb 2, 28 Lecturer: Scott Aaronson Lecture 3 Scribe: Adam Rogal Administrivia. Scribe notes The purpose of scribe notes is to transcribe our lectures. Although I have formal notes of my

More information

Regular Languages and Finite Automata

Regular Languages and Finite Automata Regular Languages and Finite Automata 1 Introduction Hing Leung Department of Computer Science New Mexico State University Sep 16, 2010 In 1943, McCulloch and Pitts [4] published a pioneering work on a

More information

Computability Theory

Computability Theory CSC 438F/2404F Notes (S. Cook and T. Pitassi) Fall, 2014 Computability Theory This section is partly inspired by the material in A Course in Mathematical Logic by Bell and Machover, Chap 6, sections 1-10.

More information

Push-down Automata and Context-free Grammars

Push-down Automata and Context-free Grammars 14 Push-down Automata and Context-free Grammars This chapter details the design of push-down automata (PDA) for various languages, the conversion of CFGs to PDAs, and vice versa. In particular, after formally

More information

CSE 135: Introduction to Theory of Computation Decidability and Recognizability

CSE 135: Introduction to Theory of Computation Decidability and Recognizability CSE 135: Introduction to Theory of Computation Decidability and Recognizability Sungjin Im University of California, Merced 04-28, 30-2014 High-Level Descriptions of Computation Instead of giving a Turing

More information

Compiler Construction

Compiler Construction Compiler Construction Regular expressions Scanning Görel Hedin Reviderad 2013 01 23.a 2013 Compiler Construction 2013 F02-1 Compiler overview source code lexical analysis tokens intermediate code generation

More information

Lecture 2: Universality

Lecture 2: Universality CS 710: Complexity Theory 1/21/2010 Lecture 2: Universality Instructor: Dieter van Melkebeek Scribe: Tyson Williams In this lecture, we introduce the notion of a universal machine, develop efficient universal

More information

1 Definition of a Turing machine

1 Definition of a Turing machine Introduction to Algorithms Notes on Turing Machines CS 4820, Spring 2012 April 2-16, 2012 1 Definition of a Turing machine Turing machines are an abstract model of computation. They provide a precise,

More information

How To Compare A Markov Algorithm To A Turing Machine

How To Compare A Markov Algorithm To A Turing Machine Markov Algorithm CHEN Yuanmi December 18, 2007 1 Abstract Markov Algorithm can be understood as a priority string rewriting system. In this short paper we give the definition of Markov algorithm and also

More information

Algorithmic Software Verification

Algorithmic Software Verification Algorithmic Software Verification (LTL Model Checking) Azadeh Farzan What is Verification Anyway? Proving (in a formal way) that program satisfies a specification written in a logical language. Formal

More information

Modeling of Graph and Automaton in Database

Modeling of Graph and Automaton in Database 1, 2 Modeling of Graph and Automaton in Database Shoji Miyanaga 1, 2 Table scheme that relational database provides can model the structure of graph which consists of vertices and edges. Recent database

More information

Formal Grammars and Languages

Formal Grammars and Languages Formal Grammars and Languages Tao Jiang Department of Computer Science McMaster University Hamilton, Ontario L8S 4K1, Canada Bala Ravikumar Department of Computer Science University of Rhode Island Kingston,

More information

Informatique Fondamentale IMA S8

Informatique Fondamentale IMA S8 Informatique Fondamentale IMA S8 Cours 1 - Intro + schedule + finite state machines Laure Gonnord http://laure.gonnord.org/pro/teaching/ Laure.Gonnord@polytech-lille.fr Université Lille 1 - Polytech Lille

More information

Genetic programming with regular expressions

Genetic programming with regular expressions Genetic programming with regular expressions Børge Svingen Chief Technology Officer, Open AdExchange bsvingen@openadex.com 2009-03-23 Pattern discovery Pattern discovery: Recognizing patterns that characterize

More information

Pushdown Automata. International PhD School in Formal Languages and Applications Rovira i Virgili University Tarragona, Spain

Pushdown Automata. International PhD School in Formal Languages and Applications Rovira i Virgili University Tarragona, Spain Pushdown Automata transparencies made for a course at the International PhD School in Formal Languages and Applications Rovira i Virgili University Tarragona, Spain Hendrik Jan Hoogeboom, Leiden http://www.liacs.nl/

More information

A formal approach to model changeability of Enterprise Resource Planning Systems

A formal approach to model changeability of Enterprise Resource Planning Systems A formal approach to model changeability of Enterprise Resource Planning Systems Laura Maruster (l.maruster@rug.nl) University of Groningen, Faculty of Economics and Business PO Box 800, 9700 AV Groningen,

More information

7.1 Our Current Model

7.1 Our Current Model Chapter 7 The Stack In this chapter we examine what is arguably the most important abstract data type in computer science, the stack. We will see that the stack ADT and its implementation are very simple.

More information

ÖVNINGSUPPGIFTER I SAMMANHANGSFRIA SPRÅK. 15 april 2003. Master Edition

ÖVNINGSUPPGIFTER I SAMMANHANGSFRIA SPRÅK. 15 april 2003. Master Edition ÖVNINGSUPPGIFTER I SAMMANHANGSFRIA SPRÅK 5 april 23 Master Edition CONTEXT FREE LANGUAGES & PUSH-DOWN AUTOMATA CONTEXT-FREE GRAMMARS, CFG Problems Sudkamp Problem. (3.2.) Which language generates the grammar

More information

Finite Automata and Regular Languages

Finite Automata and Regular Languages CHAPTER 3 Finite Automata and Regular Languages 3. Introduction 3.. States and Automata A finite-state machine or finite automaton (the noun comes from the Greek; the singular is automaton, the Greek-derived

More information

Software Model Checking: Theory and Practice

Software Model Checking: Theory and Practice Software Model Checking: Theory and Practice Lecture: Specification Checking - LTL Model Checking Copyright 2004, Matt Dwyer, John Hatcliff, and Robby. The syllabus and all lectures for this course are

More information

Reliably computing cellular automaton, in 1-sparse noise

Reliably computing cellular automaton, in 1-sparse noise Reliably computing cellular automaton, in 1-sparse noise Peter Gács Boston University Peter Gács (Boston University) TRG Spring 2010 1 / 23 Ths is the second one of a series of three lectures on reliable

More information

Introduction to Automata Theory. Reading: Chapter 1

Introduction to Automata Theory. Reading: Chapter 1 Introduction to Automata Theory Reading: Chapter 1 1 What is Automata Theory? Study of abstract computing devices, or machines Automaton = an abstract computing device Note: A device need not even be a

More information

Notes on Complexity Theory Last updated: August, 2011. Lecture 1

Notes on Complexity Theory Last updated: August, 2011. Lecture 1 Notes on Complexity Theory Last updated: August, 2011 Jonathan Katz Lecture 1 1 Turing Machines I assume that most students have encountered Turing machines before. (Students who have not may want to look

More information

NP-Completeness and Cook s Theorem

NP-Completeness and Cook s Theorem NP-Completeness and Cook s Theorem Lecture notes for COM3412 Logic and Computation 15th January 2002 1 NP decision problems The decision problem D L for a formal language L Σ is the computational task:

More information

Diagonalization. Ahto Buldas. Lecture 3 of Complexity Theory October 8, 2009. Slides based on S.Aurora, B.Barak. Complexity Theory: A Modern Approach.

Diagonalization. Ahto Buldas. Lecture 3 of Complexity Theory October 8, 2009. Slides based on S.Aurora, B.Barak. Complexity Theory: A Modern Approach. Diagonalization Slides based on S.Aurora, B.Barak. Complexity Theory: A Modern Approach. Ahto Buldas Ahto.Buldas@ut.ee Background One basic goal in complexity theory is to separate interesting complexity

More information

CAs and Turing Machines. The Basis for Universal Computation

CAs and Turing Machines. The Basis for Universal Computation CAs and Turing Machines The Basis for Universal Computation What We Mean By Universal When we claim universal computation we mean that the CA is capable of calculating anything that could possibly be calculated*.

More information

Reliability Guarantees in Automata Based Scheduling for Embedded Control Software

Reliability Guarantees in Automata Based Scheduling for Embedded Control Software 1 Reliability Guarantees in Automata Based Scheduling for Embedded Control Software Santhosh Prabhu, Aritra Hazra, Pallab Dasgupta Department of CSE, IIT Kharagpur West Bengal, India - 721302. Email: {santhosh.prabhu,

More information

DATA STRUCTURES USING C

DATA STRUCTURES USING C DATA STRUCTURES USING C QUESTION BANK UNIT I 1. Define data. 2. Define Entity. 3. Define information. 4. Define Array. 5. Define data structure. 6. Give any two applications of data structures. 7. Give

More information

Rewriting of Visibly Pushdown Languages for XML Data Integration

Rewriting of Visibly Pushdown Languages for XML Data Integration Rewriting of Visibly Pushdown Languages for XML Data Integration Alex Thomo University of Victoria British Columbia, Canada thomo@cs.uvic.ca S. Venkatesh University of Victoria British Columbia, Canada

More information

MACM 101 Discrete Mathematics I

MACM 101 Discrete Mathematics I MACM 101 Discrete Mathematics I Exercises on Combinatorics, Probability, Languages and Integers. Due: Tuesday, November 2th (at the beginning of the class) Reminder: the work you submit must be your own.

More information

Theoretical Computer Science (Bridging Course) Complexity

Theoretical Computer Science (Bridging Course) Complexity Theoretical Computer Science (Bridging Course) Complexity Gian Diego Tipaldi A scenario You are a programmer working for a logistics company Your boss asks you to implement a program that optimizes the

More information

Automata on Infinite Words and Trees

Automata on Infinite Words and Trees Automata on Infinite Words and Trees Course notes for the course Automata on Infinite Words and Trees given by Dr. Meghyn Bienvenu at Universität Bremen in the 2009-2010 winter semester Last modified:

More information

Web Data Extraction: 1 o Semestre 2007/2008

Web Data Extraction: 1 o Semestre 2007/2008 Web Data : Given Slides baseados nos slides oficiais do livro Web Data Mining c Bing Liu, Springer, December, 2006. Departamento de Engenharia Informática Instituto Superior Técnico 1 o Semestre 2007/2008

More information

Introduction to Theory of Computation

Introduction to Theory of Computation Introduction to Theory of Computation Prof. (Dr.) K.R. Chowdhary Email: kr.chowdhary@iitj.ac.in Formerly at department of Computer Science and Engineering MBM Engineering College, Jodhpur Tuesday 28 th

More information

Philadelphia University Faculty of Information Technology Department of Computer Science First Semester, 2007/2008.

Philadelphia University Faculty of Information Technology Department of Computer Science First Semester, 2007/2008. Philadelphia University Faculty of Information Technology Department of Computer Science First Semester, 2007/2008 Course Syllabus Course Title: Theory of Computation Course Level: 3 Lecture Time: Course

More information

C H A P T E R Regular Expressions regular expression

C H A P T E R Regular Expressions regular expression 7 CHAPTER Regular Expressions Most programmers and other power-users of computer systems have used tools that match text patterns. You may have used a Web search engine with a pattern like travel cancun

More information

Automata, languages, and grammars

Automata, languages, and grammars Automata, languages, and grammars Cristopher Moore January 24, 2015 Abstract These lecture notes are intended as a supplement to Moore and Mertens The Nature of Computation, and are available to anyone

More information

Formal Deænition of Finite Automaton. 1. Finite set of states, typically Q. 2. Alphabet of input symbols, typically æ.

Formal Deænition of Finite Automaton. 1. Finite set of states, typically Q. 2. Alphabet of input symbols, typically æ. Formal Denition of Finite Automaton 1. Finite set of states, typically Q. 2. Alphabet of input symbols, typically. 3. One state is the startèinitial state, typically q 0. 4. Zero or more nalèaccepting

More information

ASSIGNMENT ONE SOLUTIONS MATH 4805 / COMP 4805 / MATH 5605

ASSIGNMENT ONE SOLUTIONS MATH 4805 / COMP 4805 / MATH 5605 ASSIGNMENT ONE SOLUTIONS MATH 4805 / COMP 4805 / MATH 5605 (1) (a) (0 + 1) 010 (finite automata below). (b) First observe that the following regular expression generates the binary strings with an even

More information

DATA STRUCTURE - STACK

DATA STRUCTURE - STACK DATA STRUCTURE - STACK http://www.tutorialspoint.com/data_structures_algorithms/stack_algorithm.htm Copyright tutorialspoint.com A stack is an abstract data type ADT, commonly used in most programming

More information

ML-Flex Implementation Notes

ML-Flex Implementation Notes ML-Flex Implementation Notes Aaron Turon adrassi@uchicago.edu February 17, 2006 Contents 1 Organization 2 2 Theory 3 2.1 Regular expressions................................ 3 2.2 Derivatives.....................................

More information

Scanner. tokens scanner parser IR. source code. errors

Scanner. tokens scanner parser IR. source code. errors Scanner source code tokens scanner parser IR errors maps characters into tokens the basic unit of syntax x = x + y; becomes = + ; character string value for a token is a lexeme

More information

Computational Models Lecture 8, Spring 2009

Computational Models Lecture 8, Spring 2009 Slides modified by Benny Chor, based on original slides by Maurice Herlihy, Brown Univ. p. 1 Computational Models Lecture 8, Spring 2009 Encoding of TMs Universal Turing Machines The Halting/Acceptance

More information

Algorithms and Data Structures

Algorithms and Data Structures Algorithms and Data Structures Part 2: Data Structures PD Dr. rer. nat. habil. Ralf-Peter Mundani Computation in Engineering (CiE) Summer Term 2016 Overview general linked lists stacks queues trees 2 2

More information

Data Structures Using C++ 2E. Chapter 5 Linked Lists

Data Structures Using C++ 2E. Chapter 5 Linked Lists Data Structures Using C++ 2E Chapter 5 Linked Lists Doubly Linked Lists Traversed in either direction Typical operations Initialize the list Destroy the list Determine if list empty Search list for a given

More information

CS103B Handout 17 Winter 2007 February 26, 2007 Languages and Regular Expressions

CS103B Handout 17 Winter 2007 February 26, 2007 Languages and Regular Expressions CS103B Handout 17 Winter 2007 February 26, 2007 Languages and Regular Expressions Theory of Formal Languages In the English language, we distinguish between three different identities: letter, word, sentence.

More information

1. Nondeterministically guess a solution (called a certificate) 2. Check whether the solution solves the problem (called verification)

1. Nondeterministically guess a solution (called a certificate) 2. Check whether the solution solves the problem (called verification) Some N P problems Computer scientists have studied many N P problems, that is, problems that can be solved nondeterministically in polynomial time. Traditionally complexity question are studied as languages:

More information

A logical approach to dynamic role-based access control

A logical approach to dynamic role-based access control A logical approach to dynamic role-based access control Philippe Balbiani Yannick Chevalier Marwa El Houri Abstract Since its formalization RBAC has become the yardstick for the evaluation of access control

More information

Section 1.4 Place Value Systems of Numeration in Other Bases

Section 1.4 Place Value Systems of Numeration in Other Bases Section.4 Place Value Systems of Numeration in Other Bases Other Bases The Hindu-Arabic system that is used in most of the world today is a positional value system with a base of ten. The simplest reason

More information

CS 301 Course Information

CS 301 Course Information CS 301: Languages and Automata January 9, 2009 CS 301 Course Information Prof. Robert H. Sloan Handout 1 Lecture: Tuesday Thursday, 2:00 3:15, LC A5 Weekly Problem Session: Wednesday, 4:00 4:50 p.m., LC

More information

COMPUTER SCIENCE STUDENTS NEED ADEQUATE MATHEMATICAL BACKGROUND

COMPUTER SCIENCE STUDENTS NEED ADEQUATE MATHEMATICAL BACKGROUND COMPUTER SCIENCE STUDENTS NEED ADEQUATE MATHEMATICAL BACKGROUND Hasmik GHARIBYAN PAULSON Computer Science Department, California Polytechnic State University, 1 Grand Avenue, San Luis Obispo, CA 93407,

More information

CGT 581 G Procedural Methods L systems

CGT 581 G Procedural Methods L systems CGT 581 G Procedural Methods L systems Bedrich Benes, Ph.D. Purdue University Department of Computer Graphics Technology L systems Lindenmayer systems Lindenmayer system or D0L systems [d zero l system]

More information

NFAs with Tagged Transitions, their Conversion to Deterministic Automata and Application to Regular Expressions

NFAs with Tagged Transitions, their Conversion to Deterministic Automata and Application to Regular Expressions NFAs with Tagged Transitions, their Conversion to Deterministic Automata and Application to Regular Expressions Ville Laurikari Helsinki University of Technology Laboratory of Computer Science PL 9700,

More information

CS143 Handout 08 Summer 2008 July 02, 2007 Bottom-Up Parsing

CS143 Handout 08 Summer 2008 July 02, 2007 Bottom-Up Parsing CS143 Handout 08 Summer 2008 July 02, 2007 Bottom-Up Parsing Handout written by Maggie Johnson and revised by Julie Zelenski. Bottom-up parsing As the name suggests, bottom-up parsing works in the opposite

More information