Understanding Deterministic Finite Automata (DFA) and Conversion Methods

1. Finite Automata CPSC 388 Ellen Walker Hiram College

2. Machine Model of Computation • Strings in the language are accepted • Strings not in the language are rejected

3. Deterministic Finite Automaton Accepts strings with an odd number of 1’s (e.g. 1011, 110111, 10, 01110)

4. Parts of a DFA • An alphabet (S) • A finite set of states (S) • A deterministic set of transitions from one state to the next (T : S x S S) • One start state (s0 S) • One or more accepting states (A  S)

5. Deterministic Transition Table

6. Testing some strings • 1001 reject • 0101 reject • 10101 accept • 0 reject • 1 accept • 010101011 accept

7. Results

8. Data for DFA in C++ Class State{ Public: string name; int index; // unique integer bool isFinal; //true if final state } State start; State next [MAXSTATES][MAXCHARS];

9. Implementing DFA in C++ //Construct all the states, set isFinal //Create next array from transitions int machine(istream &sin){ //sin.get() reads a single character from sin for(State S=start; c=sin.get(); S=next[S.index][c]); //Action for S would go here... return S.isFinal; }

10. What Language is This?

11. Draw DFA for: • {aa, ab, bb} • (ab)* • (a+ab+aab)*b • All strings with 1 a and 2 b’s • All strings containing ab • All strings that don’t contain ab • All strings that don’t contain abc

12. Deterministic vs. Non-Deterministic • Deterministic: exactly one new state for a given state,character pair • Non-deterministic: 0 or more new states for each state,character pair • Assume machine will always make the right choice! • Add e-transitions (move w/o symbol)

13. NFA: All strings containing “ab”

14. Are NFA’s More Powerful? • Yes if ... • There is at least one language that can be accepted by an NFA but no DFA • No if … • For every NFA, we can make a DFA that accepts the same language

15. From NFA to DFA • Each DFA state corresponds to a set of states in the NFA • DFA has max 2n states if NFA has n states (still finite) • Transition from {s1,s2} on a is union of transitions from s1 on a and s2 on a

16. Conversion Algorithm • Copy the transitions from the start state. • Choose one of the “new state” sets. Create a new row for that set, and construct its transitions (by unioning transitions from original table) • Repeat until every set in the table has a row

17. Example: DFA for (a|b)*ab(a|b)* • Initial NFA table • Copy start state, create transition for {s1,s2}

18. Final NFA

19. Minimizing DFA • If 2 states have different isFinal, they are different • If 2 states have transitions on the same character to states that are known different, they are different • Otherwise, the states are the same and can be merged (repeat until all different)

20. Minimization Example s1 != {s1,s3} s1 != {s1,s2,s3} (final) s1 != {s1,s2} (transition on b) {s1,s2} != {s1,s3} {s1,s2} != {s1,s2,s3} (final) We cannot say {s1,s3} != {s1,s2,s3}, so merge!

21. Why minimize? • Implement machine with smaller table • Compare two machines • If both are minimized, if both compute the same language, then the machines will be identical except for state names

22. DFA and Regular Expression • The set of languages accepted by DFA’s = the set of languages accepted by regular expressions • For every r.e. we can make a DFA that accepts the same language • For every DFA we can make a r.e. that accepts the same language

23. For every r.e. there is a DFA... • If we can construct an NFA, that’s good enough • Prove by induction: • Base cases: , , a (element of ) -- build an NFA for each. • Step cases: r | s , rs , r* -- build resulting NFA from NFA’s from r and/or s