SlideShare a Scribd company logo
1 of 25
A Presentation
On
“Turing Machine”
Guided By :
Mr. Mohit Saxena
HoD CSE Department
Presented By :
Aniket Kandara
(17EAXCS004)
Session 2019-20
Apex Institute of Engineering and Technology, Jaipur
Department of Computer Science and Technology
CONTENT
S.No. TOPIC SLIDE NO.
1. Introduction of Turing Machine 4
2. What is Turing Machine ? 5
3. Representation of Turing Machine 6
4. Turing Machine Model 7
5. Uses of Turing Machine 8
6. Turing Machine as Language Acceptor 9
7. Turing Machine as Transducer 10
8. Transition Function 11
9. ID of Turing Machine 12
10. Techniques of TM Construction 13
11. Variations of Turing Machine 14
12. Recursive and Recursively Enumerable Language 22
TO BE CONTINUED...
13. Universal Language and Turing Machine 23
14. Properties of Turing Machine 24
INTRODUCTION OF TURING
MACHINES
• Introduced by Alan Turing in
1936.
• A simple mathematical model of
a computer.
• Models the computing capability
of a computer.
4
WHAT IS TURING MACHINE ?
• A Turing machine (TM) is a finite-state
machine with an infinite tape and a tape head
that can read or write one tape cell and move
left or right.
• It normally accepts the input string, or
completes its computation, by entering a final
or accepting state.
• Tape is use for input and working storage.
5
Representation of Turing Machine
Turing Machine is represented by
M=(Q,∑, Γ,δ,q0,B,F)
Where
Q is the finite set of states
∑ a set of Γ not including B, is the set of input symbols,
Γ is the finite set of allowable tape symbols,
δ is the next move function, a mapping from Q × Γ to Q × Γ
×{L,R}
Q0 in Q is the start state,
B a symbol of Γ is the blank,
F is the set of final states.
6
TURING MACHINE MODEL
7
USES OF TURING MACHINE
• Turing machine as a language recognizer.
• Turing machine as a language generator.
• Turing machine as a language evaluator.
• Turing machine as a language decider.
8
TURING MACHINE AS LANGUAGE
ACCEPTORS
• A Turing machine halts when it no longer
has available moves.
• If it halts in a final state, it accepts its
input, otherwise it rejects its input.
9
TRANSITION FUNCTION
One move (denoted by |---) in a TM does the
following:
δ(q , X) = (p ,Y ,R/L)
Where
• q is the current state.
• X is the current tape symbol pointed by tape
head.
• State changes from q to p.
10
INSTANTANEOUS DESCRIPTION (ID)
OF A TURING MACHINE ™
Instantaneous Description or ID :
X1 X2…Xi-1 q Xi Xi+1 … Xn
Means:
q is the current state Tape head is pointing to
Xi X1X2…Xi-1XiXi+1… Xn
are the current tape symbols
• δ (q , Xi ) = (p ,Y , R ) is same as:
X1 X2…Xi-1 q Xi Xi+1 …Xn |---- X1 X2…Xi-1 Y p Xi+1…Xn
• δ (q Xi) = (p, Y, L) is same as:
X1 X2…Xi-1 q Xi Xi+1 …Xn |---- X1 X2…pXi-1Y Xi+1 …Xn
11
TECHNIQUES FOR TM
CONSTRUCTION
• Storage in the finite control
• Using multiple tracks
• Using Check off symbols
• Shifting over
• Implementing Subroutine
12
VARIATIONS OF TURING MACHINES
• Multitape Turing Machines
• Non deterministic Turing machines
• Multihead Turing Machines
• Off-line Turing machines
• Multidimensional Turing machines
13
Multitape Turing Machines
• A Turing Machine with several tapes
• Every Tape’s have their Controlled own R/W Head
• For N- tape TM M=(Q,∑, Γ,δ,q0,B,F)
we define δ : QxΓN → QxΓN X { L , R} N
For e.g., if n=2 , with the current configuration
δ( qo ,a ,e)=(q1, x ,y, L, R)
14
Non Deterministic Turing Machines
• It is similar to DTM except that for any
input symbol and current state it has a
number of choices
• A string is accepted by a NDTM if there is
a sequence of moves that leads to a final
state
15
Multihead Turing Machine
• Multihead TM has a number of heads instead
of one.
• Each head independently read/ write symbols
and move left / right or keep stationery.
16
Off- Line Turing Machine
An Offline Turing Machine has two tapes
1. One tape is read-only and contains the input
2. The other is read-write and is initially blank.
17
Multidimensional Turing Machine
• A Multidimensional TM has a multidimensional tape. For
example, a two-dimensional Turing machine would read and
write on an infinite plane divided into squares, like a
checkerboard.
• For a two- Dimensional Turing Machine transaction function
define as:
δ : Q X Γ→ Q X Γ X { L , R,U,D}
18
Turing Machine With Semi- Infinite
Tape
• A Turing machine may have a “semi-infinite tape”,
the nonblank input is at the extreme left end of the
tape.
• Turing machines with semi-infinite tape are
equivalent to Standard Turing machines.
19
Turing Machine With Stationary
Head
• Here TM head has one another choice of
movement is stay option , S.
• We define new transaction function,
δ : Q X Γ Q X Γ X { L , R, S}
20
RECURSIVE AND RECURSIVELY
ENUMERABLE LANGUAGE
The Turing machine may
1. Halt and accept the input
2. Halt and reject the input, or
3. Never halt /loop.
• Recursively Enumerable Language: There is a TM for
a language which accept every string otherwise not.
• Recursive Language: There is a TM for a language
which halt on every string 21
UNIVERSAL LANGUAGE AND
TURING MACHINE
• The universal language Lu is the set of binary
strings that encode a pair (M , w) where w is
accepted by M
• A Universal Turing machine (UTM) is a Turing
machine that can simulate an arbitrary Turing
machine on arbitrary input.
22
PROPERTIES OF TURING MACHINES
• A Turing machine can recognize a language iff
it can be generated by a phrase-structure
grammar.
• The Church-Turing Thesis: A function can be
computed by an algorithm iff it can be
computed by a Turing machine.
23
Turing Machine
Turing Machine

More Related Content

What's hot

What's hot (20)

Turing machine
Turing machineTuring machine
Turing machine
 
Turing machine - theory of computation
Turing machine - theory of computationTuring machine - theory of computation
Turing machine - theory of computation
 
Turing Machine
Turing MachineTuring Machine
Turing Machine
 
Turing Machine
Turing MachineTuring Machine
Turing Machine
 
Variants of Turing Machine
Variants of Turing MachineVariants of Turing Machine
Variants of Turing Machine
 
Turing machine-TOC
Turing machine-TOCTuring machine-TOC
Turing machine-TOC
 
Automata Theory - Turing machine
Automata Theory - Turing machineAutomata Theory - Turing machine
Automata Theory - Turing machine
 
Working principle of Turing machine
Working principle of Turing machineWorking principle of Turing machine
Working principle of Turing machine
 
Turing machines
Turing machinesTuring machines
Turing machines
 
Church Turing Thesis
Church Turing ThesisChurch Turing Thesis
Church Turing Thesis
 
Turing machines
Turing machinesTuring machines
Turing machines
 
Turing Machine
Turing MachineTuring Machine
Turing Machine
 
Lecture: Automata
Lecture: AutomataLecture: Automata
Lecture: Automata
 
Introduction to Turing Machine
Introduction to Turing MachineIntroduction to Turing Machine
Introduction to Turing Machine
 
Turing machine
Turing machineTuring machine
Turing machine
 
Turing machine Introduction
Turing machine IntroductionTuring machine Introduction
Turing machine Introduction
 
Finite automata
Finite automataFinite automata
Finite automata
 
Turing machine
Turing machineTuring machine
Turing machine
 
LINEAR BOUNDED AUTOMATA (LBA).pptx
LINEAR BOUNDED AUTOMATA (LBA).pptxLINEAR BOUNDED AUTOMATA (LBA).pptx
LINEAR BOUNDED AUTOMATA (LBA).pptx
 
Automata theory
Automata theoryAutomata theory
Automata theory
 

Similar to Turing Machine

Winter 8 TM.pptx
Winter 8 TM.pptxWinter 8 TM.pptx
Winter 8 TM.pptxHarisPrince
 
Turing machine
Turing machineTuring machine
Turing machineAshik Khan
 
Turing machine seminar report
Turing machine seminar reportTuring machine seminar report
Turing machine seminar reportYashwant Dagar
 
Volume 2-issue-6-2205-2207
Volume 2-issue-6-2205-2207Volume 2-issue-6-2205-2207
Volume 2-issue-6-2205-2207Editor IJARCET
 
Volume 2-issue-6-2205-2207
Volume 2-issue-6-2205-2207Volume 2-issue-6-2205-2207
Volume 2-issue-6-2205-2207Editor IJARCET
 
Theory of Automata and formal languages Unit 5
Theory of Automata and formal languages Unit 5Theory of Automata and formal languages Unit 5
Theory of Automata and formal languages Unit 5Abhimanyu Mishra
 
TM - Techniques
TM - TechniquesTM - Techniques
TM - TechniquesRajendran
 
01. Theory of Computing
01. Theory of Computing01. Theory of Computing
01. Theory of Computingrabiul souvon
 
MOC2.PPT[1].pptx introduction to mealy machine and moore machine with eample ...
MOC2.PPT[1].pptx introduction to mealy machine and moore machine with eample ...MOC2.PPT[1].pptx introduction to mealy machine and moore machine with eample ...
MOC2.PPT[1].pptx introduction to mealy machine and moore machine with eample ...PranavHendre1
 
souraj Toc.pptx
souraj Toc.pptxsouraj Toc.pptx
souraj Toc.pptxLifo4
 
Moore and mealy machines
Moore and mealy machinesMoore and mealy machines
Moore and mealy machinesAYESHA JAVED
 
Automata presentation.
Automata presentation.Automata presentation.
Automata presentation.RehanZafar13
 

Similar to Turing Machine (20)

Turing machine
Turing machineTuring machine
Turing machine
 
TOA.docx
TOA.docxTOA.docx
TOA.docx
 
TOA.docx
TOA.docxTOA.docx
TOA.docx
 
Winter 8 TM.pptx
Winter 8 TM.pptxWinter 8 TM.pptx
Winter 8 TM.pptx
 
TuringMachines.pdf
TuringMachines.pdfTuringMachines.pdf
TuringMachines.pdf
 
Turing machine
Turing machineTuring machine
Turing machine
 
Unit iv
Unit ivUnit iv
Unit iv
 
Turing machine seminar report
Turing machine seminar reportTuring machine seminar report
Turing machine seminar report
 
Volume 2-issue-6-2205-2207
Volume 2-issue-6-2205-2207Volume 2-issue-6-2205-2207
Volume 2-issue-6-2205-2207
 
Volume 2-issue-6-2205-2207
Volume 2-issue-6-2205-2207Volume 2-issue-6-2205-2207
Volume 2-issue-6-2205-2207
 
Theory of Automata and formal languages Unit 5
Theory of Automata and formal languages Unit 5Theory of Automata and formal languages Unit 5
Theory of Automata and formal languages Unit 5
 
QB104544.pdf
QB104544.pdfQB104544.pdf
QB104544.pdf
 
TM - Techniques
TM - TechniquesTM - Techniques
TM - Techniques
 
01. Theory of Computing
01. Theory of Computing01. Theory of Computing
01. Theory of Computing
 
Universal Turing Machine.pptx
Universal Turing Machine.pptxUniversal Turing Machine.pptx
Universal Turing Machine.pptx
 
MOC2.PPT[1].pptx introduction to mealy machine and moore machine with eample ...
MOC2.PPT[1].pptx introduction to mealy machine and moore machine with eample ...MOC2.PPT[1].pptx introduction to mealy machine and moore machine with eample ...
MOC2.PPT[1].pptx introduction to mealy machine and moore machine with eample ...
 
W16.pptx
W16.pptxW16.pptx
W16.pptx
 
souraj Toc.pptx
souraj Toc.pptxsouraj Toc.pptx
souraj Toc.pptx
 
Moore and mealy machines
Moore and mealy machinesMoore and mealy machines
Moore and mealy machines
 
Automata presentation.
Automata presentation.Automata presentation.
Automata presentation.
 

Recently uploaded

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Victor Rentea
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Jeffrey Haguewood
 
Introduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDMIntroduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDMKumar Satyam
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Victor Rentea
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfOrbitshub
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyKhushali Kathiriya
 
Decarbonising Commercial Real Estate: The Role of Operational Performance
Decarbonising Commercial Real Estate: The Role of Operational PerformanceDecarbonising Commercial Real Estate: The Role of Operational Performance
Decarbonising Commercial Real Estate: The Role of Operational PerformanceIES VE
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusZilliz
 
Stronger Together: Developing an Organizational Strategy for Accessible Desig...
Stronger Together: Developing an Organizational Strategy for Accessible Desig...Stronger Together: Developing an Organizational Strategy for Accessible Desig...
Stronger Together: Developing an Organizational Strategy for Accessible Desig...caitlingebhard1
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistandanishmna97
 
Simplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptxSimplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptxMarkSteadman7
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingEdi Saputra
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 

Recently uploaded (20)

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
Introduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDMIntroduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDM
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
Decarbonising Commercial Real Estate: The Role of Operational Performance
Decarbonising Commercial Real Estate: The Role of Operational PerformanceDecarbonising Commercial Real Estate: The Role of Operational Performance
Decarbonising Commercial Real Estate: The Role of Operational Performance
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Stronger Together: Developing an Organizational Strategy for Accessible Desig...
Stronger Together: Developing an Organizational Strategy for Accessible Desig...Stronger Together: Developing an Organizational Strategy for Accessible Desig...
Stronger Together: Developing an Organizational Strategy for Accessible Desig...
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Simplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptxSimplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptx
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 

Turing Machine

  • 1. A Presentation On “Turing Machine” Guided By : Mr. Mohit Saxena HoD CSE Department Presented By : Aniket Kandara (17EAXCS004) Session 2019-20 Apex Institute of Engineering and Technology, Jaipur Department of Computer Science and Technology
  • 2. CONTENT S.No. TOPIC SLIDE NO. 1. Introduction of Turing Machine 4 2. What is Turing Machine ? 5 3. Representation of Turing Machine 6 4. Turing Machine Model 7 5. Uses of Turing Machine 8 6. Turing Machine as Language Acceptor 9 7. Turing Machine as Transducer 10 8. Transition Function 11 9. ID of Turing Machine 12 10. Techniques of TM Construction 13 11. Variations of Turing Machine 14 12. Recursive and Recursively Enumerable Language 22 TO BE CONTINUED...
  • 3. 13. Universal Language and Turing Machine 23 14. Properties of Turing Machine 24
  • 4. INTRODUCTION OF TURING MACHINES • Introduced by Alan Turing in 1936. • A simple mathematical model of a computer. • Models the computing capability of a computer. 4
  • 5. WHAT IS TURING MACHINE ? • A Turing machine (TM) is a finite-state machine with an infinite tape and a tape head that can read or write one tape cell and move left or right. • It normally accepts the input string, or completes its computation, by entering a final or accepting state. • Tape is use for input and working storage. 5
  • 6. Representation of Turing Machine Turing Machine is represented by M=(Q,∑, Γ,δ,q0,B,F) Where Q is the finite set of states ∑ a set of Γ not including B, is the set of input symbols, Γ is the finite set of allowable tape symbols, δ is the next move function, a mapping from Q × Γ to Q × Γ ×{L,R} Q0 in Q is the start state, B a symbol of Γ is the blank, F is the set of final states. 6
  • 8. USES OF TURING MACHINE • Turing machine as a language recognizer. • Turing machine as a language generator. • Turing machine as a language evaluator. • Turing machine as a language decider. 8
  • 9. TURING MACHINE AS LANGUAGE ACCEPTORS • A Turing machine halts when it no longer has available moves. • If it halts in a final state, it accepts its input, otherwise it rejects its input. 9
  • 10. TRANSITION FUNCTION One move (denoted by |---) in a TM does the following: δ(q , X) = (p ,Y ,R/L) Where • q is the current state. • X is the current tape symbol pointed by tape head. • State changes from q to p. 10
  • 11. INSTANTANEOUS DESCRIPTION (ID) OF A TURING MACHINE ™ Instantaneous Description or ID : X1 X2…Xi-1 q Xi Xi+1 … Xn Means: q is the current state Tape head is pointing to Xi X1X2…Xi-1XiXi+1… Xn are the current tape symbols • δ (q , Xi ) = (p ,Y , R ) is same as: X1 X2…Xi-1 q Xi Xi+1 …Xn |---- X1 X2…Xi-1 Y p Xi+1…Xn • δ (q Xi) = (p, Y, L) is same as: X1 X2…Xi-1 q Xi Xi+1 …Xn |---- X1 X2…pXi-1Y Xi+1 …Xn 11
  • 12. TECHNIQUES FOR TM CONSTRUCTION • Storage in the finite control • Using multiple tracks • Using Check off symbols • Shifting over • Implementing Subroutine 12
  • 13. VARIATIONS OF TURING MACHINES • Multitape Turing Machines • Non deterministic Turing machines • Multihead Turing Machines • Off-line Turing machines • Multidimensional Turing machines 13
  • 14. Multitape Turing Machines • A Turing Machine with several tapes • Every Tape’s have their Controlled own R/W Head • For N- tape TM M=(Q,∑, Γ,δ,q0,B,F) we define δ : QxΓN → QxΓN X { L , R} N For e.g., if n=2 , with the current configuration δ( qo ,a ,e)=(q1, x ,y, L, R) 14
  • 15. Non Deterministic Turing Machines • It is similar to DTM except that for any input symbol and current state it has a number of choices • A string is accepted by a NDTM if there is a sequence of moves that leads to a final state 15
  • 16. Multihead Turing Machine • Multihead TM has a number of heads instead of one. • Each head independently read/ write symbols and move left / right or keep stationery. 16
  • 17. Off- Line Turing Machine An Offline Turing Machine has two tapes 1. One tape is read-only and contains the input 2. The other is read-write and is initially blank. 17
  • 18. Multidimensional Turing Machine • A Multidimensional TM has a multidimensional tape. For example, a two-dimensional Turing machine would read and write on an infinite plane divided into squares, like a checkerboard. • For a two- Dimensional Turing Machine transaction function define as: δ : Q X Γ→ Q X Γ X { L , R,U,D} 18
  • 19. Turing Machine With Semi- Infinite Tape • A Turing machine may have a “semi-infinite tape”, the nonblank input is at the extreme left end of the tape. • Turing machines with semi-infinite tape are equivalent to Standard Turing machines. 19
  • 20. Turing Machine With Stationary Head • Here TM head has one another choice of movement is stay option , S. • We define new transaction function, δ : Q X Γ Q X Γ X { L , R, S} 20
  • 21. RECURSIVE AND RECURSIVELY ENUMERABLE LANGUAGE The Turing machine may 1. Halt and accept the input 2. Halt and reject the input, or 3. Never halt /loop. • Recursively Enumerable Language: There is a TM for a language which accept every string otherwise not. • Recursive Language: There is a TM for a language which halt on every string 21
  • 22. UNIVERSAL LANGUAGE AND TURING MACHINE • The universal language Lu is the set of binary strings that encode a pair (M , w) where w is accepted by M • A Universal Turing machine (UTM) is a Turing machine that can simulate an arbitrary Turing machine on arbitrary input. 22
  • 23. PROPERTIES OF TURING MACHINES • A Turing machine can recognize a language iff it can be generated by a phrase-structure grammar. • The Church-Turing Thesis: A function can be computed by an algorithm iff it can be computed by a Turing machine. 23