SlideShare a Scribd company logo
1 of 38
Lecture Pushdown Automata
tape




tape head
              stack head




    finite
                  stack
    control
a   l   p   h   a   b   e   t




The tape is divided into finitely many cells.
Each cell contains a symbol in an alphabet
Σ.
The stack head always scans the top
symbol of the stack. It performs two
basic operations:

Push: add a new symbol at the top.

Pop: read and remove the top symbol.


 Alphabet of stack symbols: Γ
a




• The head scans at a cell on the tape and
  can read a symbol on the cell. In each
  move, the head can move to the right cell.
• The finite control has finitely many states
  which form a set Q. For each move, the
  state is changed according to the
  evaluation of a transition function
                                     Q x (Γ U {ε})
  δ : Q x (Σ U {ε}) x (Γ U {ε}) → 2 .
a                      a



     q       v                  p     u


• (p, u) ∈ δ(q, a, v) means that if the tape head
  reads a, the stack head read v, and the finite
  control is in the state q, then one of possible
  moves is that the next state is p, v is replaced
  by u at stack, and the tape head moves one cell
  to the right.
a                         a



     q        v                p       u


• (p, u) ∈ δ(q, ε, v) means that this a ε-move.
a                      a



     q                          p     u




• (p, u) ∈ δ(q, a, ε) means that a push operation
  performs at stack.
a                      a



     q       v                  p




• (p, ε) ∈ δ(q, a, v) means that a pop operation
  performs at stack
s




• There are some special states: an initial
  state s and a final set F of final states.
• Initially, the PDA is in the initial state s and
  the head scans the leftmost cell. The tape
  holds an input string. The stack is empty.
x




              f




• When the head gets off the tape, the PDA
  stops. An input string x is accepted by the
  PDA if the PDA stops at a final state and
  the stack is empty.
• Otherwise, the input string is rejected.
• The PDA can be represented by
        M = (Q, Σ, Γ, δ, s, F)
  where Σ is the alphabet of input symbols
  and Γ is the alphabet of stack symbols.
• The set of all strings accepted by a PDA M
  is denoted by L(M). We also say that the
  language L(M) is accepted by M.
• The transition diagram of a PDA is an
  alternative way to represent the PDA.
• For M = (Q, Σ, Γ, δ, s, F), the transition diagram
  of M is an edge-labeled digraph G=(V, E)
  satisfying the following:

   V = Q (s =        ,f=      for f   ∈   F)

            a, v/u
   E={q               p | (p,u) ∈ δ(q, a, v) }.
Example 1. Construct PDA to accept
                n n
           L= {0 1 | n > 0}

Solution 1.

              1, 0/ε


         0, ε/0        1, 0/ε
Solution 2.

     Consider a CFG
     G = ({S}, {0,1}, {S → ε | 0S1}, S).



                        ε, S/1
          ε, ε/S
                                    ε, ε/S
                           ε, ε/0
               ε, S/ε

               0, 0/ε
               1, 1/ε
Theorem Every CFL can be accepted by a PDA.

Proof.   Consider a CFL L = L(G) for a CFG
         G = (V, Σ, R, S).

                      ε, A/xn     A → x1 x2 …xn in R
           ε, ε/S

                        ε, ε/x1
          a, a/ε for a in Σ

 Theorem
A language L is CFL ⇔ it can be accepted by a PDA.
Sometimes, constructing the PDA is easier than
constructing CFG.
Example 2

Show that {x ∈ {a, b} | #b ( x) ≤ # a ( x) ≤ 2#b ( x)} is a CFL


         Construct a PDA or a CFG?


                     PDA!!!



   Sometimes, constructing the PDA is easier than
   constructing CFG.
Idea
To check if #b ( x) ≤ # a ( x) ≤ 2#b ( x), we need to cancel a with b.
For each b, we need to cancel sometimes one a and sometimes
two a. Do we need to make a deterministic choice at each
cancellation? No, the concept of nondeterministic computation
solves this trouble : As long as a correct choice exists, the input
string x would be accepted!
a                    a



      s                    p   s


• (p, s) ∈ δ(s, ε, ε)


                ε ,ε / s
a                       a



    p                       p   a
             s
                                s
(q, s) ∈ δ(p, a, s)
(p, a) ∈ δ(q, ε, ε)
                 a, s / s

              ε ,ε / a
a                       a



    p                       p   a
             a
                                a
      ∈
(q1, a) δ(p, a, a)
(p, a) ∈ δ(q1, ε, ε)
                 a, a / a

             ε ,ε / a
a                         a



p                         p
    b




                  (p, ε) ∈ δ(p, a, b)

        a,b / ε
a                           a



p                           p       b’
    b




                    (p, b’)∈ δ(p, a, b)

        a, b / b'
a                            a



p                            p
    b’




                    (p, ε) ∈ δ(p, a, b’)

         a,b' / ε
b                       b



    p                       p   b
             s
                                s
(r1, s)∈ δ(p, b, s)
(p, b) ∈ δ(r1, ε, ε)
                 b, s / s

              ε ,ε / b
b                       b



    p                       p   b
             b
                                b
(r2, b)∈ δ(p, b, b)
(p, b) ∈ δ(r2, ε, ε)
                 b, b / b

              ε ,ε / b
b                      b



    p                      p   b
             b’
                               b’
(r3, b’) ∈ δ(p, b, b’)
(p, b) ∈ δ(r3, ε, ε)
              b, b' / b'

              ε ,ε / b
b                          b



p                          p
    a




                   (p, ε) ∈ δ(p, b, a)

        b, a / ε
b                              b



   p                              p
            a
            a



(t, ε) ∈ δ(p, b, a)        (p, ε) ∈ δ(t, ε, a)
                b, a / ε

             ε,a /ε
b                                     b



       p                                    p   b’
                  a
                  z                             z
                           z≠a

(t, ε) ∈ δ(p, b, a) (u, x) ∈ δ(t, ε, x) (p, b’)∈ δ(u, ε, ε)
                      b, a / ε

                                 ε, z / z
             ε ,b' / ε           z≠a
p                         p
    s




                 (p, ε) ∈ δ(p, ε, s)

        ε,s /ε
a,b / ε
                        a, b / b'
                        a,b' / ε
           ε ,ε / s                a, z / z
                                   z ≠ b,b'

                                   ε ,ε / a
                                               ε,s /ε
               b, a / ε
           b, a / ε                b, z / z
                                    z≠a
           ε,a /ε
ε, z / z                            ε ,ε / b
 z≠a                  ε , ε / b'
Example 3
Construct PDA accepting {x ∈ {a, b} | #b ( x) <# a ( x) ≤ 2#b ( x)}.


 Idea

To have #b ( x) <# a ( x), we must have a b which cancel two a ' s.
Let the first b do the job.
a,b / ε
Solution
                                    a, b / b'                 a, z / z
                                   a,b' / ε              1
                                                             z ≠ b,b'
     ε ,ε / s          a, b / b'             a, z / z
                  11                        z ≠ b,b'         ε ,ε / a
  b, a / ε        ε,a /ε
                             ε , ε / b'
                                            ε ,ε / a
              ε, z / z
               z≠a                                             ε,s /ε
                                 b, a / ε
                       b, a / ε              b, z / z
                                              z≠a
                                                             b, z / z
                        ε,a /ε                           1
           ε, z / z                           ε ,ε / b       z≠a
            z≠a                  ε , ε / b'
                                                             ε ,ε / b
Example 4
Construct PDA accepting {x ∈ {a, b} | #b ( x) ≤# a ( x) < 2#b ( x)}.


 Idea

To have # a ( x) < 2#b ( x), we must have a b which cancel one a.
Let the first b do the job.
a,b / ε
Solution
                          a, b / b'                         a, z / z
                          a,b' / ε                     1
                                                           z ≠ b,b'
   ε ,ε / s      a,b / ε           a, z / z
               1
               1
                                  z ≠ b,b'                 ε ,ε / a
                 b, a / ε
                                   ε ,ε / a
                                                             ε,s /ε
                               b, a / ε
                    b, a / ε                b, z / z
                                            z≠a
                                                           b, z / z
                    ε,a /ε                             1
         ε, z / z                           ε ,ε / b       z≠a
          z≠a                  ε , ε / b'
                                                           ε ,ε / b

More Related Content

What's hot

CONTEXT FREE GRAMMAR
CONTEXT FREE GRAMMAR CONTEXT FREE GRAMMAR
CONTEXT FREE GRAMMAR Zahid Parvez
 
Pushdown Automata Theory
Pushdown Automata TheoryPushdown Automata Theory
Pushdown Automata TheorySaifur Rahman
 
Introduction TO Finite Automata
Introduction TO Finite AutomataIntroduction TO Finite Automata
Introduction TO Finite AutomataRatnakar Mikkili
 
3.1,2,3 pushdown automata definition, moves &amp; id
3.1,2,3 pushdown automata   definition, moves &amp; id3.1,2,3 pushdown automata   definition, moves &amp; id
3.1,2,3 pushdown automata definition, moves &amp; idSampath Kumar S
 
Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)
Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)
Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)Mohammad Ilyas Malik
 
Context free grammars
Context free grammarsContext free grammars
Context free grammarsRonak Thakkar
 
Turing Machine
Turing MachineTuring Machine
Turing MachineRajendran
 
1.7. eqivalence of nfa and dfa
1.7. eqivalence of nfa and dfa1.7. eqivalence of nfa and dfa
1.7. eqivalence of nfa and dfaSampath Kumar S
 
Simplification of cfg ppt
Simplification of cfg pptSimplification of cfg ppt
Simplification of cfg pptShiela Rani
 
push down automata
 push down automata push down automata
push down automatanadiatariq6
 
2.8 normal forms gnf &amp; problems
2.8 normal forms   gnf &amp; problems2.8 normal forms   gnf &amp; problems
2.8 normal forms gnf &amp; problemsSampath Kumar S
 
Formal Languages and Automata Theory Unit 1
Formal Languages and Automata Theory Unit 1Formal Languages and Automata Theory Unit 1
Formal Languages and Automata Theory Unit 1Srimatre K
 

What's hot (20)

CONTEXT FREE GRAMMAR
CONTEXT FREE GRAMMAR CONTEXT FREE GRAMMAR
CONTEXT FREE GRAMMAR
 
Pda
PdaPda
Pda
 
Context free grammar
Context free grammar Context free grammar
Context free grammar
 
Pushdown Automata Theory
Pushdown Automata TheoryPushdown Automata Theory
Pushdown Automata Theory
 
Introduction TO Finite Automata
Introduction TO Finite AutomataIntroduction TO Finite Automata
Introduction TO Finite Automata
 
Finite Automata
Finite AutomataFinite Automata
Finite Automata
 
3.1,2,3 pushdown automata definition, moves &amp; id
3.1,2,3 pushdown automata   definition, moves &amp; id3.1,2,3 pushdown automata   definition, moves &amp; id
3.1,2,3 pushdown automata definition, moves &amp; id
 
Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)
Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)
Finite Automata: Deterministic And Non-deterministic Finite Automaton (DFA)
 
Context free grammars
Context free grammarsContext free grammars
Context free grammars
 
Automata theory
Automata theoryAutomata theory
Automata theory
 
Turing Machine
Turing MachineTuring Machine
Turing Machine
 
Parsing
ParsingParsing
Parsing
 
Chomsky Normal Form
Chomsky Normal FormChomsky Normal Form
Chomsky Normal Form
 
1.7. eqivalence of nfa and dfa
1.7. eqivalence of nfa and dfa1.7. eqivalence of nfa and dfa
1.7. eqivalence of nfa and dfa
 
Simplification of cfg ppt
Simplification of cfg pptSimplification of cfg ppt
Simplification of cfg ppt
 
Pda to cfg h2
Pda to cfg h2Pda to cfg h2
Pda to cfg h2
 
Turing machines
Turing machinesTuring machines
Turing machines
 
push down automata
 push down automata push down automata
push down automata
 
2.8 normal forms gnf &amp; problems
2.8 normal forms   gnf &amp; problems2.8 normal forms   gnf &amp; problems
2.8 normal forms gnf &amp; problems
 
Formal Languages and Automata Theory Unit 1
Formal Languages and Automata Theory Unit 1Formal Languages and Automata Theory Unit 1
Formal Languages and Automata Theory Unit 1
 

Viewers also liked

PUSH DOWN AUTOMATA VS TURING MACHINE
PUSH DOWN AUTOMATA VS TURING MACHINEPUSH DOWN AUTOMATA VS TURING MACHINE
PUSH DOWN AUTOMATA VS TURING MACHINEAbhishek Shivhare
 
Push Down Automata (PDA)
Push Down Automata (PDA)Push Down Automata (PDA)
Push Down Automata (PDA)dhea zafarina
 
Class8
 Class8 Class8
Class8issbp
 
Pushdown Automata - Materi 8 - TBO
Pushdown Automata - Materi 8 - TBOPushdown Automata - Materi 8 - TBO
Pushdown Automata - Materi 8 - TBOahmad haidaroh
 
Automata languages and computation
Automata languages and computationAutomata languages and computation
Automata languages and computationKarthik Velou
 
Turing Machine
Turing MachineTuring Machine
Turing MachineAyAn KhAn
 
Theory of automata and formal languages Unit 4
Theory of automata and formal languages Unit 4Theory of automata and formal languages Unit 4
Theory of automata and formal languages Unit 4Abhimanyu Mishra
 
Lecture: Context-Free Grammars
Lecture: Context-Free GrammarsLecture: Context-Free Grammars
Lecture: Context-Free GrammarsMarina Santini
 
Formal methods 3 - languages and machines
Formal methods   3 - languages and machinesFormal methods   3 - languages and machines
Formal methods 3 - languages and machinesVlad Patryshev
 
Copi and cohen's introduction to logic
Copi and cohen's introduction to logicCopi and cohen's introduction to logic
Copi and cohen's introduction to logicDr. Asif Kamran
 
Regular expressions and languages pdf
Regular expressions and languages pdfRegular expressions and languages pdf
Regular expressions and languages pdfDilouar Hossain
 
Cs2303 theory of computation all anna University question papers
Cs2303 theory of computation all anna University question papersCs2303 theory of computation all anna University question papers
Cs2303 theory of computation all anna University question papersappasami
 

Viewers also liked (20)

Pushdown automata
Pushdown automataPushdown automata
Pushdown automata
 
PUSH DOWN AUTOMATA VS TURING MACHINE
PUSH DOWN AUTOMATA VS TURING MACHINEPUSH DOWN AUTOMATA VS TURING MACHINE
PUSH DOWN AUTOMATA VS TURING MACHINE
 
Pushdown autometa
Pushdown autometaPushdown autometa
Pushdown autometa
 
Push Down Automata (PDA)
Push Down Automata (PDA)Push Down Automata (PDA)
Push Down Automata (PDA)
 
Class8
 Class8 Class8
Class8
 
Theory of computation Lec7 pda
Theory of computation Lec7 pdaTheory of computation Lec7 pda
Theory of computation Lec7 pda
 
Turing machine by_deep
Turing machine by_deepTuring machine by_deep
Turing machine by_deep
 
Pushdown Automata - Materi 8 - TBO
Pushdown Automata - Materi 8 - TBOPushdown Automata - Materi 8 - TBO
Pushdown Automata - Materi 8 - TBO
 
Automata languages and computation
Automata languages and computationAutomata languages and computation
Automata languages and computation
 
Turing Machine
Turing MachineTuring Machine
Turing Machine
 
Push Down Automata (PDA)
Push Down Automata (PDA)Push Down Automata (PDA)
Push Down Automata (PDA)
 
Theory of automata and formal languages Unit 4
Theory of automata and formal languages Unit 4Theory of automata and formal languages Unit 4
Theory of automata and formal languages Unit 4
 
Lecture: Context-Free Grammars
Lecture: Context-Free GrammarsLecture: Context-Free Grammars
Lecture: Context-Free Grammars
 
Turing machine
Turing machineTuring machine
Turing machine
 
Formal methods 3 - languages and machines
Formal methods   3 - languages and machinesFormal methods   3 - languages and machines
Formal methods 3 - languages and machines
 
Copi and cohen's introduction to logic
Copi and cohen's introduction to logicCopi and cohen's introduction to logic
Copi and cohen's introduction to logic
 
Regular expressions and languages pdf
Regular expressions and languages pdfRegular expressions and languages pdf
Regular expressions and languages pdf
 
Page replacement
Page replacementPage replacement
Page replacement
 
Lexical 2
Lexical 2Lexical 2
Lexical 2
 
Cs2303 theory of computation all anna University question papers
Cs2303 theory of computation all anna University question papersCs2303 theory of computation all anna University question papers
Cs2303 theory of computation all anna University question papers
 

Similar to pushdown automata

Discrete mathematic question answers
Discrete mathematic question answersDiscrete mathematic question answers
Discrete mathematic question answersSamet öztoprak
 
590-Article Text.pdf
590-Article Text.pdf590-Article Text.pdf
590-Article Text.pdfBenoitValea
 
590-Article Text.pdf
590-Article Text.pdf590-Article Text.pdf
590-Article Text.pdfBenoitValea
 
On Hadamard Product of P-Valent Functions with Alternating Type
On Hadamard Product of P-Valent Functions with Alternating TypeOn Hadamard Product of P-Valent Functions with Alternating Type
On Hadamard Product of P-Valent Functions with Alternating TypeIOSR Journals
 
The Persistent Homology of Distance Functions under Random Projection
The Persistent Homology of Distance Functions under Random ProjectionThe Persistent Homology of Distance Functions under Random Projection
The Persistent Homology of Distance Functions under Random ProjectionDon Sheehy
 
Owf 2013 rii veri t fontaine speaker4
Owf 2013 rii veri t fontaine speaker4Owf 2013 rii veri t fontaine speaker4
Owf 2013 rii veri t fontaine speaker4Patrick MOREAU
 
A sharp nonlinear Hausdorff-Young inequality for small potentials
A sharp nonlinear Hausdorff-Young inequality for small potentialsA sharp nonlinear Hausdorff-Young inequality for small potentials
A sharp nonlinear Hausdorff-Young inequality for small potentialsVjekoslavKovac1
 
Sensors and Samples: A Homological Approach
Sensors and Samples:  A Homological ApproachSensors and Samples:  A Homological Approach
Sensors and Samples: A Homological ApproachDon Sheehy
 
11.[29 35]a unique common fixed point theorem under psi varphi contractive co...
11.[29 35]a unique common fixed point theorem under psi varphi contractive co...11.[29 35]a unique common fixed point theorem under psi varphi contractive co...
11.[29 35]a unique common fixed point theorem under psi varphi contractive co...Alexander Decker
 
slides CIRM copulas, extremes and actuarial science
slides CIRM copulas, extremes and actuarial scienceslides CIRM copulas, extremes and actuarial science
slides CIRM copulas, extremes and actuarial scienceArthur Charpentier
 
String Matching with Finite Automata and Knuth Morris Pratt Algorithm
String Matching with Finite Automata and Knuth Morris Pratt AlgorithmString Matching with Finite Automata and Knuth Morris Pratt Algorithm
String Matching with Finite Automata and Knuth Morris Pratt AlgorithmKiran K
 
Lect1 No 873503264
Lect1 No 873503264Lect1 No 873503264
Lect1 No 873503264yangrong
 
Approximate Bayesian Computation with Quasi-Likelihoods
Approximate Bayesian Computation with Quasi-LikelihoodsApproximate Bayesian Computation with Quasi-Likelihoods
Approximate Bayesian Computation with Quasi-LikelihoodsStefano Cabras
 
L03 ai - knowledge representation using logic
L03 ai - knowledge representation using logicL03 ai - knowledge representation using logic
L03 ai - knowledge representation using logicManjula V
 
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...IOSR Journals
 
Lundi 16h15-copules-charpentier
Lundi 16h15-copules-charpentierLundi 16h15-copules-charpentier
Lundi 16h15-copules-charpentierArthur Charpentier
 
Optimization of probabilistic argumentation with Markov processes
Optimization of probabilistic argumentation with Markov processesOptimization of probabilistic argumentation with Markov processes
Optimization of probabilistic argumentation with Markov processesEmmanuel Hadoux
 
Bayesian adaptive optimal estimation using a sieve prior
Bayesian adaptive optimal estimation using a sieve priorBayesian adaptive optimal estimation using a sieve prior
Bayesian adaptive optimal estimation using a sieve priorJulyan Arbel
 

Similar to pushdown automata (20)

Discrete mathematic question answers
Discrete mathematic question answersDiscrete mathematic question answers
Discrete mathematic question answers
 
590-Article Text.pdf
590-Article Text.pdf590-Article Text.pdf
590-Article Text.pdf
 
590-Article Text.pdf
590-Article Text.pdf590-Article Text.pdf
590-Article Text.pdf
 
On Hadamard Product of P-Valent Functions with Alternating Type
On Hadamard Product of P-Valent Functions with Alternating TypeOn Hadamard Product of P-Valent Functions with Alternating Type
On Hadamard Product of P-Valent Functions with Alternating Type
 
The Persistent Homology of Distance Functions under Random Projection
The Persistent Homology of Distance Functions under Random ProjectionThe Persistent Homology of Distance Functions under Random Projection
The Persistent Homology of Distance Functions under Random Projection
 
Owf 2013 rii veri t fontaine speaker4
Owf 2013 rii veri t fontaine speaker4Owf 2013 rii veri t fontaine speaker4
Owf 2013 rii veri t fontaine speaker4
 
A sharp nonlinear Hausdorff-Young inequality for small potentials
A sharp nonlinear Hausdorff-Young inequality for small potentialsA sharp nonlinear Hausdorff-Young inequality for small potentials
A sharp nonlinear Hausdorff-Young inequality for small potentials
 
Sensors and Samples: A Homological Approach
Sensors and Samples:  A Homological ApproachSensors and Samples:  A Homological Approach
Sensors and Samples: A Homological Approach
 
11.[29 35]a unique common fixed point theorem under psi varphi contractive co...
11.[29 35]a unique common fixed point theorem under psi varphi contractive co...11.[29 35]a unique common fixed point theorem under psi varphi contractive co...
11.[29 35]a unique common fixed point theorem under psi varphi contractive co...
 
slides CIRM copulas, extremes and actuarial science
slides CIRM copulas, extremes and actuarial scienceslides CIRM copulas, extremes and actuarial science
slides CIRM copulas, extremes and actuarial science
 
String Matching with Finite Automata and Knuth Morris Pratt Algorithm
String Matching with Finite Automata and Knuth Morris Pratt AlgorithmString Matching with Finite Automata and Knuth Morris Pratt Algorithm
String Matching with Finite Automata and Knuth Morris Pratt Algorithm
 
Lect1 No 873503264
Lect1 No 873503264Lect1 No 873503264
Lect1 No 873503264
 
Approximate Bayesian Computation with Quasi-Likelihoods
Approximate Bayesian Computation with Quasi-LikelihoodsApproximate Bayesian Computation with Quasi-Likelihoods
Approximate Bayesian Computation with Quasi-Likelihoods
 
L03 ai - knowledge representation using logic
L03 ai - knowledge representation using logicL03 ai - knowledge representation using logic
L03 ai - knowledge representation using logic
 
BAYSM'14, Wien, Austria
BAYSM'14, Wien, AustriaBAYSM'14, Wien, Austria
BAYSM'14, Wien, Austria
 
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
 
Lundi 16h15-copules-charpentier
Lundi 16h15-copules-charpentierLundi 16h15-copules-charpentier
Lundi 16h15-copules-charpentier
 
Logic
LogicLogic
Logic
 
Optimization of probabilistic argumentation with Markov processes
Optimization of probabilistic argumentation with Markov processesOptimization of probabilistic argumentation with Markov processes
Optimization of probabilistic argumentation with Markov processes
 
Bayesian adaptive optimal estimation using a sieve prior
Bayesian adaptive optimal estimation using a sieve priorBayesian adaptive optimal estimation using a sieve prior
Bayesian adaptive optimal estimation using a sieve prior
 

Recently uploaded

The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13Steve Thomason
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Sapana Sha
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdfQucHHunhnh
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionSafetyChain Software
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptxVS Mahajan Coaching Centre
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxheathfieldcps1
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104misteraugie
 
A Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformA Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformChameera Dedduwage
 
Hybridoma Technology ( Production , Purification , and Application )
Hybridoma Technology  ( Production , Purification , and Application  ) Hybridoma Technology  ( Production , Purification , and Application  )
Hybridoma Technology ( Production , Purification , and Application ) Sakshi Ghasle
 
Separation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and ActinidesSeparation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and ActinidesFatimaKhan178732
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Celine George
 
Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfchloefrazer622
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxiammrhaywood
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...Marc Dusseiller Dusjagr
 
Student login on Anyboli platform.helpin
Student login on Anyboli platform.helpinStudent login on Anyboli platform.helpin
Student login on Anyboli platform.helpinRaunakKeshri1
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxpboyjonauth
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Krashi Coaching
 
Privatization and Disinvestment - Meaning, Objectives, Advantages and Disadva...
Privatization and Disinvestment - Meaning, Objectives, Advantages and Disadva...Privatization and Disinvestment - Meaning, Objectives, Advantages and Disadva...
Privatization and Disinvestment - Meaning, Objectives, Advantages and Disadva...RKavithamani
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxGaneshChakor2
 

Recently uploaded (20)

The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory Inspection
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
A Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformA Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy Reform
 
Hybridoma Technology ( Production , Purification , and Application )
Hybridoma Technology  ( Production , Purification , and Application  ) Hybridoma Technology  ( Production , Purification , and Application  )
Hybridoma Technology ( Production , Purification , and Application )
 
Separation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and ActinidesSeparation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and Actinides
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdf
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
 
Student login on Anyboli platform.helpin
Student login on Anyboli platform.helpinStudent login on Anyboli platform.helpin
Student login on Anyboli platform.helpin
 
Staff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSDStaff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSD
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptx
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
 
Privatization and Disinvestment - Meaning, Objectives, Advantages and Disadva...
Privatization and Disinvestment - Meaning, Objectives, Advantages and Disadva...Privatization and Disinvestment - Meaning, Objectives, Advantages and Disadva...
Privatization and Disinvestment - Meaning, Objectives, Advantages and Disadva...
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptx
 

pushdown automata

  • 2. tape tape head stack head finite stack control
  • 3. a l p h a b e t The tape is divided into finitely many cells. Each cell contains a symbol in an alphabet Σ.
  • 4. The stack head always scans the top symbol of the stack. It performs two basic operations: Push: add a new symbol at the top. Pop: read and remove the top symbol. Alphabet of stack symbols: Γ
  • 5. a • The head scans at a cell on the tape and can read a symbol on the cell. In each move, the head can move to the right cell.
  • 6. • The finite control has finitely many states which form a set Q. For each move, the state is changed according to the evaluation of a transition function Q x (Γ U {ε}) δ : Q x (Σ U {ε}) x (Γ U {ε}) → 2 .
  • 7. a a q v p u • (p, u) ∈ δ(q, a, v) means that if the tape head reads a, the stack head read v, and the finite control is in the state q, then one of possible moves is that the next state is p, v is replaced by u at stack, and the tape head moves one cell to the right.
  • 8. a a q v p u • (p, u) ∈ δ(q, ε, v) means that this a ε-move.
  • 9. a a q p u • (p, u) ∈ δ(q, a, ε) means that a push operation performs at stack.
  • 10. a a q v p • (p, ε) ∈ δ(q, a, v) means that a pop operation performs at stack
  • 11. s • There are some special states: an initial state s and a final set F of final states. • Initially, the PDA is in the initial state s and the head scans the leftmost cell. The tape holds an input string. The stack is empty.
  • 12. x f • When the head gets off the tape, the PDA stops. An input string x is accepted by the PDA if the PDA stops at a final state and the stack is empty. • Otherwise, the input string is rejected.
  • 13. • The PDA can be represented by M = (Q, Σ, Γ, δ, s, F) where Σ is the alphabet of input symbols and Γ is the alphabet of stack symbols. • The set of all strings accepted by a PDA M is denoted by L(M). We also say that the language L(M) is accepted by M.
  • 14. • The transition diagram of a PDA is an alternative way to represent the PDA. • For M = (Q, Σ, Γ, δ, s, F), the transition diagram of M is an edge-labeled digraph G=(V, E) satisfying the following: V = Q (s = ,f= for f ∈ F) a, v/u E={q p | (p,u) ∈ δ(q, a, v) }.
  • 15. Example 1. Construct PDA to accept n n L= {0 1 | n > 0} Solution 1. 1, 0/ε 0, ε/0 1, 0/ε
  • 16. Solution 2. Consider a CFG G = ({S}, {0,1}, {S → ε | 0S1}, S). ε, S/1 ε, ε/S ε, ε/S ε, ε/0 ε, S/ε 0, 0/ε 1, 1/ε
  • 17. Theorem Every CFL can be accepted by a PDA. Proof. Consider a CFL L = L(G) for a CFG G = (V, Σ, R, S). ε, A/xn A → x1 x2 …xn in R ε, ε/S ε, ε/x1 a, a/ε for a in Σ Theorem A language L is CFL ⇔ it can be accepted by a PDA.
  • 18. Sometimes, constructing the PDA is easier than constructing CFG.
  • 19. Example 2 Show that {x ∈ {a, b} | #b ( x) ≤ # a ( x) ≤ 2#b ( x)} is a CFL Construct a PDA or a CFG? PDA!!! Sometimes, constructing the PDA is easier than constructing CFG.
  • 20. Idea To check if #b ( x) ≤ # a ( x) ≤ 2#b ( x), we need to cancel a with b. For each b, we need to cancel sometimes one a and sometimes two a. Do we need to make a deterministic choice at each cancellation? No, the concept of nondeterministic computation solves this trouble : As long as a correct choice exists, the input string x would be accepted!
  • 21. a a s p s • (p, s) ∈ δ(s, ε, ε) ε ,ε / s
  • 22. a a p p a s s (q, s) ∈ δ(p, a, s) (p, a) ∈ δ(q, ε, ε) a, s / s ε ,ε / a
  • 23. a a p p a a a ∈ (q1, a) δ(p, a, a) (p, a) ∈ δ(q1, ε, ε) a, a / a ε ,ε / a
  • 24. a a p p b (p, ε) ∈ δ(p, a, b) a,b / ε
  • 25. a a p p b’ b (p, b’)∈ δ(p, a, b) a, b / b'
  • 26. a a p p b’ (p, ε) ∈ δ(p, a, b’) a,b' / ε
  • 27. b b p p b s s (r1, s)∈ δ(p, b, s) (p, b) ∈ δ(r1, ε, ε) b, s / s ε ,ε / b
  • 28. b b p p b b b (r2, b)∈ δ(p, b, b) (p, b) ∈ δ(r2, ε, ε) b, b / b ε ,ε / b
  • 29. b b p p b b’ b’ (r3, b’) ∈ δ(p, b, b’) (p, b) ∈ δ(r3, ε, ε) b, b' / b' ε ,ε / b
  • 30. b b p p a (p, ε) ∈ δ(p, b, a) b, a / ε
  • 31. b b p p a a (t, ε) ∈ δ(p, b, a) (p, ε) ∈ δ(t, ε, a) b, a / ε ε,a /ε
  • 32. b b p p b’ a z z z≠a (t, ε) ∈ δ(p, b, a) (u, x) ∈ δ(t, ε, x) (p, b’)∈ δ(u, ε, ε) b, a / ε ε, z / z ε ,b' / ε z≠a
  • 33. p p s (p, ε) ∈ δ(p, ε, s) ε,s /ε
  • 34. a,b / ε a, b / b' a,b' / ε ε ,ε / s a, z / z z ≠ b,b' ε ,ε / a ε,s /ε b, a / ε b, a / ε b, z / z z≠a ε,a /ε ε, z / z ε ,ε / b z≠a ε , ε / b'
  • 35. Example 3 Construct PDA accepting {x ∈ {a, b} | #b ( x) <# a ( x) ≤ 2#b ( x)}. Idea To have #b ( x) <# a ( x), we must have a b which cancel two a ' s. Let the first b do the job.
  • 36. a,b / ε Solution a, b / b' a, z / z a,b' / ε 1 z ≠ b,b' ε ,ε / s a, b / b' a, z / z 11 z ≠ b,b' ε ,ε / a b, a / ε ε,a /ε ε , ε / b' ε ,ε / a ε, z / z z≠a ε,s /ε b, a / ε b, a / ε b, z / z z≠a b, z / z ε,a /ε 1 ε, z / z ε ,ε / b z≠a z≠a ε , ε / b' ε ,ε / b
  • 37. Example 4 Construct PDA accepting {x ∈ {a, b} | #b ( x) ≤# a ( x) < 2#b ( x)}. Idea To have # a ( x) < 2#b ( x), we must have a b which cancel one a. Let the first b do the job.
  • 38. a,b / ε Solution a, b / b' a, z / z a,b' / ε 1 z ≠ b,b' ε ,ε / s a,b / ε a, z / z 1 1 z ≠ b,b' ε ,ε / a b, a / ε ε ,ε / a ε,s /ε b, a / ε b, a / ε b, z / z z≠a b, z / z ε,a /ε 1 ε, z / z ε ,ε / b z≠a z≠a ε , ε / b' ε ,ε / b