1. Automata theory and formal languages Andrej Bogdanov http://www.cse.cuhk.edu.hk/~andrejb/csc3130 The Chinese University of Hong Kong Fall 2009

2. What are computers good at? In 1997, IBM Deep Blue defeated world chess champion Gary Kasparov in a six match tournament. 3 ½2 ½

3. What are computers good at? The search engine Google indexes 2,000,000,000 web pages. It lets you find pretty much anything you want.

4. What else? Recommend books Fly airplanes Is there anything a computer cannot do?

5. Impossibilities Why do we care about the impossible?

6. Perpetual motion In the middle ages, people wanted a machine that does not use any energy Perpetual motion is a futile endeavor Understanding the impossible helps us channel our energies towards the more useful. Later, discoveries in physics showed that energy cannot be created out of thin air

7. The laws of computation Just like the laws of physics tell us what is (im)possible for nature to do......the laws of computation tell us what is (im)possible for computers.

8. Automata theory Automata theory studies the laws of computation. In reality, the laws of computation are not quite understood, but automata theory is a good start.

9. A simple computer BATTERY SWITCH input: switch output: light bulb actions: flip switch states: on, off

10. A simple “computer” BATTERY SWITCH off on start f f input: switch output: light bulb actions: f for “flip switch” states: on, off bulb is on if and only if there was an odd number of flips

11. Another “computer” BATTERY off start 1 inputs: switches 1 and 2 actions: 1 for “flip switch 1” actions: 2 for “flip switch 2” states: on, off bulb is on if and only if both switches were flipped an odd number of times 1 2 1 off on 1 1 2 2 2 2

12. A design problem Design a circuit where the light is on only when all switches are flipped the same number of times 4 BATTERY 1 2 3 5 =

13. A design problem Such devices are difficult to reason about, because they can be designed in an infinite number of ways By representing them as abstract computational devices, or automata, we will learn how to answer such questions off on f f

14. These devices can model many things They can describe the operation of any “small computer”, like the control component of an alarm clock or a microwave They are also used in lexical analyzers to recognize well formed expressions in programming languages: ab1 is a legal name of a variable in C 5u= is not

15. Different kinds of automata This was only one example of a computational device, and there are others We will look at different devices, and look at these kinds of questions: –What kinds of problems can a given type of device solve? –What things are impossible for this kind of device? –Is one type of device more powerful than another?

16. Some devices we will see finite automataDevices with a finite amount of memory. Used to model “small” computers. push-down automata Devices with infinite memory that can be accessed in a restricted way. Used to model parsers, etc. Turing MachinesDevices with infinite memory. Used to model any computer. time-bounded Turing Machines Infinite memory, but bounded running time. Used to model any computer program that runs in a “reasonable” amount of time.

17. Some highlights of the course Finite automata –We will understand what kinds of things a device with finite memory can do, and what it cannot do –Introduce simulation: the ability of one device to “imitate” another device –Introduce nondeterminism: the ability of a device to make arbitrary choices Push-down automata –These devices are related to grammars, which describe the structure of programming (and natural) languages

18. Some highlights of the course Turing Machines –This is a general model of a computer, capturing anything we could ever hope to compute –But there are many things that computers cannot do: Given the code of a computer program, can you tell if it prints “ banana ”? #include main(t,_,a)char *a;{return!0<t?t<3?main(-79,-13,a+main(-87,1-_, main(-86,0,a+1)+a)):1,t<_?main(t+1,_,a):3,main(-94,-27+t,a)&&t==2?_<13? main(2,_+1,"%s %d %d\n"):9:16:t<0?t<-72?main(_,t, "@n'+,#'/*{}w+/w#cdnr/+,{}r/*de}+,/*{*+,/w{%+,/w#q#n+,/#{l,+,/n{n+,/+#n+,/#\ ;#q#n+,/+k#;*+,/'r :'d*'3,}{w+K w'K:'+}e#';dq#'l \ q#'+d'K#!/+k#;q#'r}eKK#}w'r}eKK{nl]'/#;#q#n'){)#}w'){){nl]'/+#n';d}rw' i;# \ ){nl]!/n{n#'; r{#w'r nc{nl]'/#{l,+'K {rw' iK{;[{nl]'/w#q#n'wk nw' \ iwk{KK{nl]!/w{%'l##w#' i; :{nl]'/*{q#'ld;r'}{nlwb!/*de}'c \ ;;{nl'-{}rw]'/+,}##'*}#nc,',#nw]'/+kd'+e}+;#'rdq#w! nr'/ ') }+}{rl#'{n' ')# \ }'+}##(!!/") :t<-50?_==*a?putchar(31[a]):main(-65,_,a+1):main((*a=='/')+t,_,a+1) :0<t?main(2,2,"%s"):*a=='/'||main(0,main(-61,*a, "!ek;dc i@bK'(q)-[w]*%n+r3#l,{}:\nuwloca-O;m.vpbks,fxntdCeghiry"),a+1);} banana ?

19. Some highlights of the course Time-bounded Turing Machines –Many problems are possible to solve on a computer in principle, but take too much time in practice –Traveling salesman: Given a list of cities, find the shortest way to visit them and come back home –Easy in principle: Try the cities in every possible order –Hard in practice: For 100 cities, this would take 100+ years even on the fastest computer! Hong Kong Beijing Shanghai Xian Guangzhou Chengdu

20. Preliminaries of automata theory How do we formalize the question First, we need a way of describing the problems that we are interested in solving Can device A solve problem B?

21. Problems Examples of problems we will consider –Given a word s, does it contain the subword “ fool ”? –Given a number n, is it divisible by 7? –Given a pair of words s and t, are they the same? –Given an expression with brackets, e.g. (()()), does every left bracket match with a subsequent right bracket? All of these have “yes/no” answers. There are other types of problems, like “Find this” or “How many of that” but we won’t look at them.

22. Alphabets and strings A common way to talk about words, numbers, pairs of words, etc. is by representing them as strings To define strings, we start with an alphabet Examples An alphabet is a finite set of symbols.  1 = {a, b, c, d, …, z} : the set of letters in English  2 = {0, 1, …, 9} : the set of (base 10) digits  3 = {a, b, …, z, #} : the set of letters plus the special symbol #  4 = { (, ) } : the set of open and closed brackets

23. Strings The empty string will be denoted by  Examples A string over alphabet  is a finite sequence of symbols in . abfbz is a string over  1 = {a, b, c, d, …, z} 9021 is a string over  2 = {0, 1, …, 9} ab#bc is a string over  3 = {a, b, …, z, #} ))()(() is a string over  4 = { (, ) }

24. Languages Languages can be used to describe problems with “yes/no” answers, for example: A language is a set of strings over an alphabet. L 1 = The set of all strings over  1 that contain the substring “fool” L 2 = The set of all strings over  2 that are divisible by 7 ={7, 14, 21, …} L 3 = The set of all strings of the form s#s where s is any string over {a, b, …, z} L 4 = The set of all strings over  4 where every ( can be matched with a subsequent )

25. Finite Automata

26. Example of a finite automaton There are states off and on, the automaton starts in off and tries to reach the “accept state” on What sequences of f’ s lead to the accept state? Answer: {f, fff, fffff, …} = {f n : n is odd } This is a finite automaton over alphabet {f} off on f f

27. Deterministic finite automata A deterministic finite automaton (DFA) is a 5-tuple (Q, , , q 0, F) where – Q is a finite set of states –  is an alphabet –  : Q ×  → Q is a transition function – q 0  Q is the initial state – F  Q is a set of accepting states (or final states). In diagrams, the accepting states will be denoted by double loops

28. Example q0q0 q1q1 q2q2 10 00,11 alphabet  = {0, 1} states Q = {q 0, q 1, q 2 } initial state q 0 accepting states F = {q 0, q 1 } states inputs 0 1 q0q0 q1q1 q2q2 q0q0 q1q1 q2q2 q2q2 q2q2 q1q1 table of transition function 

29. Language of a DFA The language of a DFA (Q, , , q 0, F) is the set of all strings over  that, starting from q 0 and following the transitions as the string is read left to right, will reach some accepting state. Language of M is {f, fff, fffff, …} = {f n : n is odd } off on f f M:M:

30. q0q0 q1q1 0 1 1 0 What are the languages of these automata? Examples q0q0 q1q1 q2q2 q3q3 q4q4 a a a a a b b b b b q0q0 q1q1 0 1 1 0 q2q2 0, 1  = {0, 1}  = {a, b}

31. Examples Construct a DFA over alphabet {0, 1} that accepts all strings with at most three 1 s

32. Examples Construct a DFA over alphabet {0, 1} that accepts all strings with at most three 1 s Answer q0q0 q1q1 0 1 1 q2q2 0 q3q3 1 q 4+ 0, 1 0 1 0

33. Examples Construct a DFA that accepts the language L = {010, 1} (  = {0, 1} )

34. Examples Construct a DFA that accepts the language Answer L = {010, 1} (  = {0, 1} ) qq q0q0 q1q1 q 01 q 010 q die 0, 1 0 1 0 1 01

35. Examples Construct a DFA over alphabet {0, 1} that accepts all strings that end in 101

36. Examples Construct a DFA over alphabet {0, 1} that accepts all strings that end in 101 Hint: The DFA must “remember” the last 3 bits of the string it is reading

37. Examples 0 1 … … … … qq qq qq q  q  q  q  q  q  q  q  0 1 0 1 0 1 1 1 1 1 0 Construct a DFA over alphabet {0, 1} that accepts all strings that end in 101 Sketch of answer: