1. Introduction to Finite Automata Adapted from the slides of Stanford CS154

2. 2 Informal Explanation Finite automata  finite collections of states with transition rules that take you from one state to another. Original application  sequential switching circuits, where the “state” was the settings of internal bits. Today, several kinds of software can be modeled by FA.

3. 3 Representing FA Simplest representation is often a graph.  Nodes = states.  Arcs indicate state transitions.  Labels on arcs tell what causes the transition.

4. 4 Example: Recognizing Strings Ending in “ing” nothing Saw i i Not i Saw ing g i Not i or g Saw in n i Not i or n Start

5. 5 Automata to Code In C/C++, make a piece of code for each state. This code: 1. Reads the next input. 2. Decides on the next state. 3. Jumps to the beginning of the code for that state.

6. 6 Example: Automata to Code 2: /* i seen */ c = getNextInput(); if (c == ’n’) goto 3; else if (c == ’i’) goto 2; else goto 1; 3: /* ”in” seen */...

7. 7 Deterministic Finite Automata A formalism for defining languages, consisting of: 1. A finite set of states (Q, typically). 2. An input alphabet ( Σ, typically). 3. A transition function ( δ, typically). 4. A start state (q 0, in Q, typically). 5. A set of final states (F ⊆ Q, typically). u “Final” and “accepting” are synonyms.

8. 8 The Transition Function Takes two arguments:  a state and an input symbol. δ (q, a) = the state that the DFA goes to when it is in state q and input a is received.

9. 9 Graph Representation of DFA’s Nodes = states. Arcs represent transition function.  Arc from state p to state q labeled by all those input symbols that have transitions from p to q. Arrow labeled “Start” to the start state. Final states indicated by double circles.

10. 10 Example: Graph of a DFA Start 1 0 ACB 1 0 0,1 Previous string OK, does not end in 1. Previous String OK, ends in a single 1. Consecutive 1’s have been seen. Accepts all strings without two consecutive 1’s.

11. 11 Alternative Representation: Transition Table 01 AABBACCCCAABBACCCC Rows = states Columns = input symbols Final states starred * * Arrow for start state

12. 12 Extended Transition Function We describe the effect of a string of inputs on a DFA  by extending δ to a state and a string. Induction on length of string. Basis: δ (q, ε ) = q Induction: δ (q,wa) = δ ( δ (q,w),a)  w is a string; a is an input symbol.

13. 13 Extended δ : Intuition Convention:  … w, x, y, x are strings.  a, b, c,… are single symbols. Extended δ is computed for state q and inputs a 1 a 2 …a n by following a path in the transition graph, starting at q and selecting the arcs with labels a 1, a 2,…,a n in turn.

14. 14 Example: Extended Delta 01 AABBACCCCAABBACCCC δ (B,011) = δ ( δ (B,01),1) = δ ( δ ( δ (B,0),1),1) = δ ( δ (A,1),1) = δ (B,1) = C

15. 15 Language of a DFA Automata of all kinds define languages. If A is an automaton, L(A) is its language. For a DFA A, L(A) is the set of strings labeling paths from the start state to a final state. Formally: L(A) = the set of strings w such that δ (q 0, w) is in F.

16. 16 Example: String in a Language Start 1 0 ACB 1 0 0,1 String 101 is in the language of the DFA below. Start at A.

17. 17 Example: String in a Language Start 1 0 ACB 1 0 0,1 Follow arc labeled 1. String 101 is in the language of the DFA below.

18. 18 Example: String in a Language Start 1 0 ACB 1 0 0,1 Then arc labeled 0 from current state B. String 101 is in the language of the DFA below.

19. 19 Example: String in a Language Start 1 0 ACB 1 0 0,1 Finally arc labeled 1 from current state A. Result is an accepting state, so 101 is in the language. String 101 is in the language of the DFA below.

20. 20 Example – Concluded The language of our example DFA is: {w | w is in {0,1}* and w does not have two consecutive 1’s} Read a set former as “The set of strings w… Such that… These conditions about w are true.

21. 21 Regular Languages A language L is regular if it is the language accepted by some DFA.  Note: the DFA must accept only the strings in L, no others. Some languages are not regular.  Intuitively, regular languages “cannot count” to arbitrarily high integers.

22. 22 Example: A Nonregular Language L 1 = {0 n 1 n | n ≥ 1} Note: a i is conventional for i a’s.  Thus, 0 4 = 0000, e.g. Read: “The set of strings consisting of n 0’s followed by n 1’s, such that n is at least 1. Thus, L 1 = {01, 0011, 000111,…}

23. 23 Another Example L 2 = {w | w in {(, )}* and w is balanced }  Note: alphabet consists of the parenthesis symbols ’(’ and ’)’.  Balanced parens are those that can appear in an arithmetic expression. E.g.: (), ()(), (()), (()()),…

24. 24 But Many Languages are Regular Regular Languages can be described in many ways, e.g., regular expressions. They appear in many contexts and have many useful properties. Example: the strings that represent floating point numbers in your favorite language is a regular language.

25. 25 Nondeterminism A nondeterministic finite automaton has the ability to be in several states at once. Transitions from a state on an input symbol can be to any set of states.

26. 26 Nondeterminism – (2) Start in one start state. Accept if any sequence of choices leads to a final state. Intuitively: the NFA always “guesses right.”

27. 27 Formal NFA A finite set of states, typically Q. An input alphabet, typically Σ. A transition function, typically δ. A start state in Q, typically q 0. A set of final states F ⊆ Q.

28. 28 Transition Function of an NFA δ (q, a) is a set of states. Extend to strings as follows: Basis: δ (q, ε ) = {q} Induction: δ (q, wa) = the union over all states p in δ (q, w) of δ (p, a)

29. 29 Language of an NFA A string w is accepted by an NFA if δ (q 0, w) contains at least one final state. The language of the NFA is the set of strings it accepts.

30. 30 Equivalence of DFA’s, NFA’s A DFA can be turned into an NFA that accepts the same language. If δ D (q, a) = p, let the NFA have δ N (q, a) = {p}. Then the NFA is always in a set containing exactly one state – the state the DFA is in after reading the same input.

31. 31 Equivalence – (2) Surprisingly, for any NFA there is a DFA that accepts the same language. Proof is the subset construction. The number of states of the DFA can be exponential in the number of states of the NFA. Thus, NFA’s accept exactly the regular languages.

32. 32 Subset Construction Given an NFA with states Q, inputs Σ, transition function δ N, state state q 0, and final states F, construct equivalent DFA with:  States 2 Q (Set of subsets of Q).  Inputs Σ.  Start state {q 0 }.  Final states = all those with a member of F.

33. 33 Subset Construction – (2) The transition function δ D is defined by: δ D ({q 1,…,q k }, a) is the union over all i = 1,…,k of δ N (q i, a).

34. 34 Proof of Equivalence: Subset Construction Show by induction on |w| that δ N (q 0, w) = δ D ({q 0 }, w) Basis: w = ε : δ N (q 0, ε ) = δ D ({q 0 }, ε ) = {q 0 }.

35. 35 Induction Assume IH (Induction Hypothesis) holds for strings shorter than w. Let w = xa; IH holds for x. Let δ N (q 0, x) = δ D ({q 0 }, x) = S. Let T = the union over all states p in S of δ N (p, a). Then δ N (q 0, w) = δ D ({q 0 }, w) = T.  For NFA: the extension of δ N.  For DFA: definition of δ D plus extension of δ D. That is, δ D (S, a) = T; then extend δ D to w = xa.

36. 36 NFA’s With ε -Transitions We can allow state-to-state transitions on ε input. These transitions are done spontaneously, without looking at the input string. A convenience at times, but still only regular languages are accepted.

37. 37 Example: ε -NFA C EF A BD 1 11 0 0 0 ε ε ε

38. Subset construction in the Textbook

39. Subset construction in the Textbook (cont.)