Theory of Computer Science C8. Type-1 and Type-0 Languages: Closure - - PowerPoint PPT Presentation

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Theory of Computer Science C8. Type-1 and Type-0 Languages: Closure - - PowerPoint PPT Presentation

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Theory of Computer Science C8. Type-1 and Type-0 Languages: Closure & Decidability Gabriele R oger University of Basel April 15, 2020 Turing Machines vs. Grammars Closure and Decidability Summary Overview Languages & Grammars

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Theory of Computer Science

  • C8. Type-1 and Type-0 Languages: Closure & Decidability

Gabriele R¨

  • ger

University of Basel

April 15, 2020

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Turing Machines vs. Grammars Closure and Decidability Summary

Overview

Automata & Formal Languages Languages & Grammars Regular Languages Context-free Languages Context-sensitive & Type-0 Languages Turing machines Closure properties & decidability

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Turing Machines vs. Grammars Closure and Decidability Summary

Turing Machines vs. Grammars

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Turing Machines vs. Grammars Closure and Decidability Summary

Reminder: Turing Machines – Conceptually

. . . b a c a c a c a . . . infinite tape read-write head

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Turing Machines vs. Grammars Closure and Decidability Summary

Reminder: Nondeterministic Turing Machine

Definition (Nondeterministic Turing Machine) A nondeterministic Turing machine (NTM) is given by a 7-tuple M = Q, Σ, Γ, δ, q0, , E with: Q finite non-empty set of states Σ = ∅ finite input alphabet Γ ⊃ Σ finite tape alphabet δ : (Q \ E) × Γ → P(Q × Γ × {L, R, N}) transition function q0 ∈ Q start state ∈ Γ \ Σ blank symbol E ⊆ Q end states

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Turing Machines vs. Grammars Closure and Decidability Summary

One Automata Model for Two Grammar Types?

Don’t we need different automata models for context-sensitive and type-0 languages?

Picture courtesy of stockimages / FreeDigitalPhotos.net

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Turing Machines vs. Grammars Closure and Decidability Summary

Linear Bounded Automata: Idea

Linear bounded automata are NTMs that may only use the part of the tape occupied by the input word.

  • ne way of formalizing this: NTMs where blank symbol

may never be replaced by a different symbol

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Turing Machines vs. Grammars Closure and Decidability Summary

Linear Bounded Turing Machines: Definition

Definition (Linear Bounded Automata) An NTM M = Q, Σ, Γ, δ, q0, , E is called a linear bounded automaton (LBA) if for all q ∈ Q \ E and all transition rules q′, c, y ∈ δ(q, ) we have c = .

German: linear beschr¨ ankte Turingmaschine

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Turing Machines vs. Grammars Closure and Decidability Summary

LBAs Accept Type-1 Languages

Theorem The languages that can be accepted by linear bounded automata are exactly the context-sensitive (type-1) languages. Without proof. proof sketch for grammar ⇒ NTM direction: computation of the NTM follows the production of the word in the grammar in opposite order accept when only start symbol (and blanks) are left on the tape because language is context-sensitive, we never need additional space on the tape (empty word needs special treatment)

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Turing Machines vs. Grammars Closure and Decidability Summary

LBAs Accept Type-1 Languages

Theorem The languages that can be accepted by linear bounded automata are exactly the context-sensitive (type-1) languages. Without proof. proof sketch for grammar ⇒ NTM direction: computation of the NTM follows the production of the word in the grammar in opposite order accept when only start symbol (and blanks) are left on the tape because language is context-sensitive, we never need additional space on the tape (empty word needs special treatment)

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Turing Machines vs. Grammars Closure and Decidability Summary

NTMs Accept Type-0 Languages

Theorem The languages that can be accepted by nondeterministic Turing machines are exactly the type-0 languages. Without proof. proof sketch for grammar ⇒ NTM direction: analogous to previous proof for grammar rules w1 → w2 with |w1| > |w2|, we must “insert” symbols into the existing tape content; this is a bit tedious, but not very difficult

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Turing Machines vs. Grammars Closure and Decidability Summary

NTMs Accept Type-0 Languages

Theorem The languages that can be accepted by nondeterministic Turing machines are exactly the type-0 languages. Without proof. proof sketch for grammar ⇒ NTM direction: analogous to previous proof for grammar rules w1 → w2 with |w1| > |w2|, we must “insert” symbols into the existing tape content; this is a bit tedious, but not very difficult

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Turing Machines vs. Grammars Closure and Decidability Summary

Deterministic Turing Machines

Definition (Deterministic Turing Machine) A deterministic Turing machine (DTM) is a Turing machine M = Q, Σ, Γ, δ, q0, , E with δ : (Q \ E) × Γ → Q × Γ × {L, R, N}.

German: deterministische Turingmaschine

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Turing Machines vs. Grammars Closure and Decidability Summary

Deterministic Turing Machines vs. Type-0 Languages

Theorem For every type-0 language L there is a deterministic Turing machine M with L(M) = L. Without proof. proof sketch: Let M′ be an NTM with L(M′) = L. It is possible to construct a DTM that systematically searches for an accepting configuration in the computation tree of M′. Note: It is an open problem whether an analogous theorem Note: holds for type-1 languages and deterministic LBAs.

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Turing Machines vs. Grammars Closure and Decidability Summary

Deterministic Turing Machines vs. Type-0 Languages

Theorem For every type-0 language L there is a deterministic Turing machine M with L(M) = L. Without proof. proof sketch: Let M′ be an NTM with L(M′) = L. It is possible to construct a DTM that systematically searches for an accepting configuration in the computation tree of M′. Note: It is an open problem whether an analogous theorem Note: holds for type-1 languages and deterministic LBAs.

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Turing Machines vs. Grammars Closure and Decidability Summary

Questions Questions?

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Turing Machines vs. Grammars Closure and Decidability Summary

Closure Properties and Decidability

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Turing Machines vs. Grammars Closure and Decidability Summary

Overview

Automata & Formal Languages Languages & Grammars Regular Languages Context-free Languages Context-sensitive & Type-0 Languages Turing machines Closure properties & decidability

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Turing Machines vs. Grammars Closure and Decidability Summary

Closure Properties

Intersection Union Complement Concatenation Star Type 3 Yes Yes Yes Yes Yes Type 2 No Yes No Yes Yes Type 1 Yes(2) Yes(1) Yes(2) Yes(1) Yes(1) Type 0 Yes(2) Yes(1) No(3) Yes(1) Yes(1) Proofs? (1) proof via grammars, similar to context-free cases (2) without proof (3) proof in later chapters (part D)

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Turing Machines vs. Grammars Closure and Decidability Summary

Decidability

Word problem Emptiness problem Equivalence problem Intersection problem Type 3 Yes Yes Yes Yes Type 2 Yes Yes No No Type 1 Yes(1) No(3) No(2) No(2) Type 0 No(4) No(4) No(4) No(4) Proofs? (1) same argument we used for context-free languages (2) because already undecidable for context-free languages (3) without proof (4) proofs in later chapters (part D)

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Turing Machines vs. Grammars Closure and Decidability Summary

Questions Questions?

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Turing Machines vs. Grammars Closure and Decidability Summary

Summary

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Turing Machines vs. Grammars Closure and Decidability Summary

Summary

Turing machines accept exactly the type-0 languages. This is also true for deterministic Turing machines. Linear bounded automata accept exactly the context-sensitive languages. The context-sensitive and type-0 languages are closed under almost all usual operations.

exception: type-0 not closed under complement

For context-sensitive and type-0 languages almost no problem is decidable.

exception: word problem for context-sensitive lang. decidable

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Turing Machines vs. Grammars Closure and Decidability Summary

What’s Next?

contents of this course:

  • A. background

⊲ mathematical foundations and proof techniques

  • B. logic

⊲ How can knowledge be represented? ⊲ How can reasoning be automated?

  • C. automata theory and formal languages

⊲ What is a computation?

  • D. Turing computability

⊲ What can be computed at all?

  • E. complexity theory

⊲ What can be computed efficiently?

  • F. more computability theory

⊲ Other models of computability

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Turing Machines vs. Grammars Closure and Decidability Summary

What’s Next?

contents of this course:

  • A. background

⊲ mathematical foundations and proof techniques

  • B. logic

⊲ How can knowledge be represented? ⊲ How can reasoning be automated?

  • C. automata theory and formal languages

⊲ What is a computation?

  • D. Turing computability

⊲ What can be computed at all?

  • E. complexity theory

⊲ What can be computed efficiently?

  • F. more computability theory

⊲ Other models of computability