FINITE STATE AUTOMATA

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1 FINITE STATE AUTOMATA

2 States An FSA has a finite set of states A system has a limited number of configurations Examples {On, Off}, {1,2,3,4,,k} {TV channels} States can be graphically represented as follows: On Off

3 Input An FSA is defined over an alphabet The symbols of the alphabet represent the input of the system Examples {switch_on, switch_off} {incoming==0, 0<incoming<=10, incoming>10}

4 Transitions among states When an input is received, the system changes its state The passage between states is performed through transitions A transition is graphically represented by arrows: On Off

5 Simple examples (flip-flop) On T T Off S On R S Off R

6 FSA FSAs are the simplest model of computation Many useful devices can be modeled using FSAs but they have some limitations

7 Formally An FSA is a triple <Q, A, δ>, where Q is a finite set of states A is the input alphabet δ is a transition function (that can be partial), given by δ: Q A Q Remark if the function is partial, then not all the transitions from all the possible states for all the possible elements of the alphabet are defined

8 Partial vs Total Transition Function switch_on switch_off switch_off On Off switch_on An FSA with a total transition function is called complete

9 Recognizing languages In order to be able to use FSAs for recognizing languages, it is important to identify: the initial conditions of the system the final admissible states Example: The light should be off at the beginning and at the end

10 Elements The elements of the model are States Transitions Input and also Initial state(s) Final state(s)

11 Graphical representation Initial state Off Final state Off

12 Formally An FSA is a tuple <Q, A, δ, q 0, F>, where Q is a finite set of states A is the input alphabet δ is a (partial) transition function, given by δ: Q A Q q 0 Q is called initial state F Q is the set of final states

13 Move sequence A move sequence starts from an initial state and is accepting if it reaches one of the final states switch_on On switch_off Off switch_off switch_on

14 Formally Move sequence: δ : Q A Q δ is inductively defined from δ δ (q,ε) = q δ (q,yi) = δ(δ (q,y), i) Initial state: q 0 Q Final (or accepting) states: F Q x (x L δ (q 0,x) F)

15 A practical example Recognizing Pascal identifiers <letter> <letter> q 0 q 1 <digit> q E <digit> <digit> <letter>

16 FINITE STATE TRANSDUCERS

17 Automata as language translators Input tape Control device (finite state) Output tape A finite state transducer is an FSA that works on two tapes. it is a kind of ``translating machine''.

18 The idea y = τ(x) x: input string y: output τ: function from L 1 to L 2 Examples: τ 1 the occurrences of 1 are doubled (1 --> 11) τ 2 a is swapped with b (a <---> b): but also Compression of files Compiling from high level languages into object languages Translation from English to Italian

19 Informally Transitions with output q i/w q Example: τ halves the number of 0 s and doubles the number of 1 s 1/11 0/ε 1/11 q 0 q 1 0/0

20 Formally A finite state transducer (FST) is a tuple T = <Q, I, δ, q 0, F, O, η> <Q, I, δ, q 0, F>: just like acceptors O: output alphabet η : Q I O * Remark: the condition for acceptance remains the same as in acceptors The translation is performed only on accepted strings

21 Translating a string As we did for δ, we define η* inductively η * (q,ε) = ε η (q,y.i) = η (q,y).η(δ (q,y), i) Remark η*: Q I * O * x (τ(x) = η (q 0,x) iff δ (q 0,x) F)

22 PUMPING LEMMA

23 Cycles There is a cycle: q1 ----aabab---> q1 b a b q0 q1 q2 q9 b b a a q5 a q4 q3 b q8 a b q6 q7 If one goes through the cycle once, then one can also go through it 2,3,, n times

24 More formally If x L and x Q, then there exists a q Q and a w I + such that: x = ywz δ (q,w) = q Therefore the following also holds: n 0 yw n z L This is the Pumping Lemma (one can pump w)

25 Consequences of pumping lemma L =? L =? x L y L y < Q : Just remove all cycles from the FSA accepting x Check by a similar argument whether x L Q <= x < 2 Q Note that in general knowing how to answer the question x L? for a generic x, does not entail knowing how to answer the other questions It works for FSAs, but

26 Impact in practice Are we interested in a programming language consisting of 0 correct programs? Are we interested in a programming language in which one can only write a finite number of programs?...

27 A negative consequence of pumping lemma Is the language L = {a n b n n > 0} recognized by some FSA? Let us suppose it is. Then: Consider x = a m b m, m > Q and let us apply P.L. Possible cases: x = ywz, w = a k, k > 0 ====> a m + r k b m L, r : NO x = ywz, w = b k, k > 0 ====> same x = ywz, w = a k b s, k, s > 0 ====> a m-k a k b s a k b s b m-s L: NO

28 Intuitively In order to count an arbitrary n we need an infinite memory! Rigorously speaking, every computer is an FSA, but it is the wrong abstraction: intractable number of states! (same thing as studying every single molecule in the flight of an airplane) Importance of an abstract notion of infinity From the toy example {a n b n } to more concrete cases: Checking well-balancing of brackets (typically used in programming languages) cannot be done with finite memory We therefore need more powerful models

29 OPERATIONS ON FSA

30 Closure in math A set S is closed w.r.t. an operation OP if, when operation OP is applied to elements of S, the result is still an element of S Examples: Natural numbers are closed w.r.t. sum (but not subtraction) Integers are closed w.r.t. sum, subtraction, multiplication (but not division) Rationals Reals

31 Closure for languages L = {L i }: family of languages L is closed w.r.t. operation OP if and only if, for every L 1, L 2 L, L 1 OP L 2 L. R: regular languages (recognized by FSAs) R is closed w.r.t. set-theoretic operations, concatenation, *,

32 Intersection A The parallel run of A and B can be simulated by coupling them b a a q 0 q 1 q 2 q 9 B p 0 b a a p 1 p 2 p 9 p a a <A,B> q b 0 0 q 1 p 1 q 2 p 2 q 9 p 9

33 Example A 1 : a A 2 : a q 0 q 1 p 0 a a q 0 p 0 q 1 p 0 a 33

34 Formally Given A 1 = <Q 1, I, δ 1, q 01, F 1 > A 2 = <Q 2, I, δ 2, q 02, F 2 > < A 1, A 2 > = <Q 1 Q 2,I,δ,<q 01,q 02 >,F 1 F 2 > δ(<q 1,q 2 >, i) = <δ 1 (q 1, i), δ 2 (q 2,i)> One can show (by simple induction) that L(< A 1,A 2 >) = L(A 1 ) L( A 2 ) Can we do the same for union?

35 Union The union is built analogously Given A 1 = <Q 1, I, δ 1, q 01, F 1 > A 2 = <Q 2, I, δ 2, q 02, F 2 > <A1, A2>= <Q 1 xq 2,I,δ,<q 01,q 02 >,F 1 xq 2 UQ 1 xf 2 > δ(<q 1,q 2 >, i) = <δ 1 (q 1, i), δ 2 (q 2,i)>

36 Complement (1) Basic idea F c = Q-F q 0 q 1 1 but in general the transition function is partial!

37 Complement (2) Before swapping final and non final states it is necessary to complete the FSA q 0 q q 0 q q 0 q 1 q E 0,1 1 0,1 q E 1

38 Union again Another possibility is to use complement and De Morgan s laws: A B = ( A B)

39 Philosophy of complement If I scan the entire input string, then it suffices to swap yes and no (F with Q-F) If I cannot reach the end of the string, then swapping F with Q-F does not work In the case of FSAs there is an easy workaround (completing the FSA) In general we cannot consider the negative answer to a question as equivalent to the positive answer to the opposite question!

Deterministic Finite Automata (DFAs)

Algorithms & Models of Computation CS/ECE 374, Fall 27 Deterministic Finite Automata (DFAs) Lecture 3 Tuesday, September 5, 27 Sariel Har-Peled (UIUC) CS374 Fall 27 / 36 Part I DFA Introduction Sariel