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1 1 CDT314 FABER Formal Languages, Automata and Models of Computation Lecture 3 School of Innovation, Design and Engineering Mälardalen University 2012.

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Presentation on theme: "1 1 CDT314 FABER Formal Languages, Automata and Models of Computation Lecture 3 School of Innovation, Design and Engineering Mälardalen University 2012."— Presentation transcript:

1 1 1 CDT314 FABER Formal Languages, Automata and Models of Computation Lecture 3 School of Innovation, Design and Engineering Mälardalen University 2012

2 2 Content Finite Automata, FA Deterministic Finite Automata, DFA Nondeterministic Automata NFA NFA  DFA Equivalence

3 3 Finite Automata FA (Finite State Machines) Based on C Busch, RPI, Models of Computation

4 4 There is no formal general definition for "automaton". Instead, there are various kinds of automata, each with it's own formal definition. has some form of input has some form of output has internal states may or may not have some form of storage is hard-wired rather than programmable Generally, an automaton

5 5 Finite Automaton Input String Output String Finite Automaton

6 6 Finite Accepter Input “Accept” or “Reject” String Finite Automaton Output

7 7 Nodes = States Edges = Transitions An edge with several symbols is a short-hand for several edges: Finite Automaton as Directed Graph

8 8 Deterministic Finite Automata DFA

9 9 Deterministic there is no element of choice Finite only a finite number of states and arcs Acceptors produce only a yes/no answer DFA

10 10 Transition Graph initial state final state “accept” state transition abba -Finite Acceptor Alphabet =

11 11 Formal Definition Deterministic Finite Accepter (DFA) : set of states : input alphabet : transition function : initial state : set of final states

12 12 Set of States

13 13 Input Alphabet

14 14 Initial State

15 15 Set of Final States

16 16 Transition Function

17 17

18 18

19 19

20 20 Transition Function

21 21 Extended Transition Function

22 22

23 23

24 24 Observation: There is a walk from to with the label

25 25 Recursive Definition

26 26      ,,,,,,* ),,(*,* q bq baq baq baq abq          

27 27 String Acceptance Definition: A string w is accepted by DFA M if w drives M to a final state from the initial state. Formally: M accepts w iff

28 28 abbbaa is NOT accepted

29 29 abba is accepted

30 30 Language Accepted by DFA Take a DFA Definition: The language contains all input strings accepted by =  strings that drive to a final state 

31 31 Example accept Alphabet =

32 32 Another Example accept Alphabet =

33 33 Formally For a DFA Language accepted by : alphabet transition function initial state final states

34 34 Observation Language accepted by Language rejected by

35 35 More Examples accept trap state Alphabet =

36 36 = { all strings with prefix } accept Alphabet =

37 37 = { all strings without substring } Alphabet =

38 38 Regular Languages All regular languages form a language family A language is regular if there is a DFA such that

39 39 Example is regular The language Alphabet =

40 40 Nondeterministic Finite Automata NFA

41 41 Nondeterministic there is an element of choice: in a given state NFA can act on a given string in different ways. Several start/final states are allowed. -transitions are allowed. Finite only a finite number of states and arcs Acceptors produce only a yes/no answer NFA

42 42 Two choices Alphabet = Nondeterministic Finite Accepter (NFA)

43 43 First Choice

44 44 First Choice

45 45 First Choice

46 46 “accept” First Choice

47 47 Second Choice

48 48 Second Choice

49 49 Second Choice No transition: the automaton hangs

50 50 Second Choice “reject”

51 51 Observation An NFA accepts a string if there is at least one computation of the NFA that accepts the string

52 52 Example is accepted by the NFA:

53 53 Lambda Transitions

54 54

55 55

56 56 (read head doesn’t move)

57 57

58 58 “accept” String is accepted

59 59 Language accepted:

60 60 Another NFA Example Alphabet =

61 61

62 62

63 63

64 64 “accept”

65 65 Another String Alphabet =

66 66

67 67

68 68

69 69

70 70

71 71

72 72 “accept”

73 73 Language accepted Alphabet =

74 74 Another NFA Example Alphabet =

75 75 Language accepted

76 76 Formal Definition of NFA Set of states, i.e. Input alphabet, i.e. Transition function Initial state Final states

77 77 Transition Function

78 78

79 79

80 80

81 81 Extended Transition Function (Utvidgad övergångsfunktion)

82 82

83 83

84 84 Formally if and only if there is a walk from to with label

85 85 The Language of an NFA Alphabet =

86 86

87 87

88 88

89 89

90 90 Formally The language accepted by NFA (final state) where and there is some (at least one) is:

91 91

92 92 NFA  DFA Equivalence

93 93 Equivalence of NFAs and DFAs Accept the same languages? YES! NFAs  DFAs ? The same power?

94 94 We will prove: Languages accepted by NFAs Languages accepted by DFAs NFAs and DFAs have the same computation power!

95 95 Languages accepted by NFAs Languages accepted by DFAs Step 1 Proof Every DFA is also an NFA A language accepted by a DFA is also accepted by an NFA

96 96 Languages accepted by NFAs Languages accepted by DFAs Step 2 Proof Any NFA can be converted to an equivalent DFA A language accepted by an NFA is also accepted by a DFA

97 97 Procedure NFA to DFA Idea: Define new states in DFA that collect the states with the same transition 0 1  3  {0} start { 1,3}  {3}  start

98 98 Procedure NFA to DFA 1. Initial state of NFA: Initial state of DFA:

99 99 Example NFA DFA Step 1

100 100 Procedure NFA to DFA 2. For every DFA’s state Compute in the NFA Add transition

101 101 Example NFA DFA Step 2 Alphabet =

102 102 Procedure NFA to DFA Repeat Step 2 for all letters in alphabet, until no more transitions can be added.

103 103 Example NFA DFA Step 3 Alphabet =

104 104 Procedure NFA to DFA 3. For any DFA state If some is a final state in the NFA Then is a final state in the DFA

105 105 Example NFA DFA Step 4 Alphabet =

106 106 Theorem Take NFA Apply the procedure to obtain DFA Then and are equivalent :

107 107 Languages accepted by NFAs Languages accepted by DFAs We have proven (proof by construction): Regular Languages END OF PROOF

108 108 Nondeterministic vs. Deterministic Automata

109 109 Formal Definition of NFA Set of states, i.e. Input alphabet, i.e. Transition function Initial state Final (accepting) states NFA is a mathematical model defined as a quintuple:

110 110 Deterministic Finite Automata A deterministic finite automaton (DFA) is a special case of a nondeterministic finite automaton (NFA) in which 1. no state has an -transition, i.e. a transition on input, and 2. for each state q and input symbol a, there is at most one edge labeled a leaving q.

111 111 STATE INPUT SYMBOL ab {0, 1} - {0} {2} {3} Transition table for the finite automaton above A nondeterministic finite automaton b 0 start 1 a 2 bb 3 a Example: NFA

112 112 NFA accepting aa* + bb* 0 start 1 a 2 a 3 b 4 b Example

113 113 NFA accepting (a+b)*abb 0 start 1 a 2 bb b aa a b 3 a Example

114 114 NFA recognizing three different patterns. (a) NFA for a, abb, and a*b +. (b) Combined NFA. Example 4 1 start a 2 3 a 65 bb 7 b 8 b a start a 2 3 a 65 b b 7 b 8 b a

115 115 Ways to Think of Nondeterminism always make the correct guess “backtracking” (systematically try all possibilities) For a particular string, imagine a tree of possible state transitions: q4q4 a q0q0 q3q3 q0q0 q2q2 q1q1 a a a b a

116 116 Advantages of Nondeterminism An NFA can be smaller, easier to construct and easier to understand than a DFA that accepts the same language NFAs are useful for proving some theorems NFAs are good introduction to nondeterminism in more powerful computational models, where nondeterminism plays an important role

117 117 Space and time taken to recognize regular expressions: - NFA more compact but take time to backtrack all choices - DFA take place, but save time Time-Space Tradeoffs Goal: Given reg. exp. r and input string x, determine whether x is in L(r) Method #1: Build NFA N from r using Thompson's construction, then run previous algorithm construct NFA in O(|r|) time. N has at most twice as many states as |r|, and at most two transitions from each state, so transition table is O(|r|) space. Previous algorithm accepts or rejects x in O(|r|×|x|) time AUTOMATO N SPACETIME NFA DFA O(|r|) O(2 |r| ) O(|r|  |x|) O(|x|) DFA vs. NFA Space and Time Complexity Tradeoffs (Where r is regular expression, and x is input string)

118 118 Equivalent automata Two finite automata M 1 and M 2 are equivalent if L(M 1 ) = L(M 2 ) that is, if they both accept the same language.

119 119 Equivalence of NFAs and DFAs To show that NFAs and DFAs accept the same class of languages, we show two things: –Any language accepted by a DFA can also be accepted by some NFA (As DFA is a special case of NFA) –Any language accepted by a NFA can also be accepted by some (corresponding, specially constructed) DFA

120 120 Proof Strategy To show that any language accepted by a NFA is also accepted by some DFA, we describe an algorithm that takes any NFA and converts it into a DFA that accepts the same language. The algorithm is called the “subset construction algorithm”. We can use mathematical induction (on the length of a string accepted by the automaton) to prove the DFA that is constructed accepts the same language as the NFA.

121 121 Converting NFA to DFA by Subset Construction

122 122 Subset construction Given a NFA construct a DFA that accepts the same language. The equivalent DFA simulates the NFA by keeping track of the possible states it could be in. Each state of the DFA is a subset of the set of states of the NFA -hence, the name of the algorithm. If the NFA has n states, the DFA can have as many as 2 n states, although it usually has many less.

123 123 Steps of subset construction The initial state of the DFA is the set of all states the NFA can be in without reading any input. For any state {q i,q j,…,q k } of the DFA and any input a, the next state of the DFA is the set of all states of the NFA that can result as next states if the NFA is in any of the states q i,q j,…,q k when it reads a. This includes states that can be reached by reading a, followed by any number of -moves. Use this rule to keep adding new states and transitions until it is no longer possible to do so. The accepting states of the DFA are those states that contain an accepting state of the NFA.

124 124 Example Here is a NFA that we want to convert to an equivalent DFA Alphabet =

125 125 {0,1} The start state of the DFA is the set of states the NFA can be in before reading any input. This includes the start state of the NFA and any states that can be reached by a -transition. NFA DFA Alphabet =

126 126 {0,1} a b {2} For start state {0,1}, make transitions for each possible input, here a and b. Reading b from start {0,1}, we reach state {2}. Means from either {0}, or {1} we reach {2}. NFA DFA

127 127 For state {2}, we create a transition for each possible input, a and b. From {2}, with b we are either back to {2} (loop) or we reach {1}- see the little framed original NFA. So from {2}, with b we end in state {1, 2}. Reading a leads us from {2} to {0} in the original NFA, which means state {0, 1} in the new DFA. {0,1} {1,2} {2} NFA DFA

128 128 For state {1, 2}, we make again transition for each possible input, a and b. From {2} a leads us to {0}. From {1} with a we are back to {1}. So, we reach {0, 1} with a from {1,2}. With b we are back to {1,2}. At this point, a transition is defined for every state-input pair. {0,1} {1,2} {2} DFA NFA

129 129 The last step is to mark the final states of the DFA. As {1} was the accepting state in NFA, all states containing {1} in DFA will be accepting states: ({0, 1} and {1, 2}). {0,1} {1,2} {2} DFA NFA

130 130 Subset Construction Algorithm

131 131 Subset Construction States of nondeterministic M´ will correspond to sets of states of deterministic M Where q 0 is start state of M, use {q 0 } as start state of M´. Accepting states of M´ will be those state-sets containing at least one accepting state of M.

132 132 Subset Construction (cont.) For each state-set S and for each s in alphabet of M, we draw an arc labeled s from state S to that state-set consisting of all and only the s- successors of members of S. Eliminate any state-set, as well as all arcs incident upon it, such that there is no path leading to it from {q 0 }.

133 133 The power set of a finite set, Q, consists of 2 |Q| elements The DFA corresponding to a given NFA with Q states have a finite number of states, 2 |Q|. If |Q| = 0 then Q is the empty set, | P(Q)| = 1 = 2 0. If |Q| = N and N  1, we construct subset of a given set so that for each element of the initial set there are two alternatives, either is the element member of a subset or not. So we have 2 · 2 · 2 · 2 · 2 · 2 · 2…. ·2 = 2 N N times

134 134 From an NFA to a DFA Subset Construction Operation Description - closure(s) - closure(T) Move(T,a) Set of NFA states reachable from an NFA state s on -transitions Set of NFA states reachable from some NFA state s in T on - transitions Set of NFA states reachable from some NFA state set with a transition on input symbol a

135 135 From an NFA to a DFA Subset Construction Initially, -closure (s 0 ) is the only states in D and it is unmarked while there is an unmarked state T in D do mark T; for each input symbol a do U:= -closure(move(T,a)); if U is not in D then add U as an unmarked state to D Dtran[T,a]:=U; end(for) end(while)


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