This document discusses different types of formal languages in the Chomsky hierarchy:
1. Recursively enumerable languages are generated by Turing machines and include type-0 languages. They are closed under union, concatenation, and Kleene star but not difference or complement.
2. Context-sensitive languages are type-1 languages generated by linear-bounded automata. They are closed under union, intersection, concatenation, and Kleene star.
3. Context-free languages are type-2 languages generated by pushdown automata, including programming language grammars. They are closed under union, concatenation, Kleene star, and reversal.
4. Regular languages are type-3 languages generated by
The document discusses algorithms and their analysis. It covers:
1) The definition of an algorithm and its key characteristics like being unambiguous, finite, and efficient.
2) The fundamental steps of algorithmic problem solving like understanding the problem, designing a solution, and analyzing efficiency.
3) Methods for specifying algorithms using pseudocode, flowcharts, or natural language.
4) Analyzing an algorithm's time and space efficiency using asymptotic analysis and orders of growth like best-case, worst-case, and average-case scenarios.
The document discusses finite automata including nondeterministic finite automata (NFAs) and deterministic finite automata (DFAs). It provides examples of NFAs and DFAs that recognize particular strings, including strings containing certain substrings. It also gives examples of DFA state machines and discusses using finite automata to recognize regular languages.
The document discusses compilers and their role in translating high-level programming languages into machine-readable code. It notes that compilers perform several key functions: lexical analysis, syntax analysis, generation of an intermediate representation, optimization of the intermediate code, and finally generation of assembly or machine code. The compiler allows programmers to write code in a high-level language that is easier for humans while still producing efficient low-level code that computers can execute.
The document discusses Turing machines and their properties. It introduces the Church-Turing thesis that any problem that can be solved by an algorithm can be modeled by a Turing machine. It then describes different types of Turing machines, such as multi-track, nondeterministic, two-way, multi-tape, and multidimensional Turing machines. The document provides examples of Turing machines that accept specific languages and evaluate mathematical functions through their transition tables and diagrams.
This document provides an introduction to automata theory and finite automata. It defines an automaton as an abstract computing device that follows a predetermined sequence of operations automatically. A finite automaton has a finite number of states and can be deterministic or non-deterministic. The document outlines the formal definitions and representations of finite automata. It also discusses related concepts like alphabets, strings, languages, and the conversions between non-deterministic and deterministic finite automata. Methods for minimizing deterministic finite automata using Myhill-Nerode theorem and equivalence theorem are also introduced.
This document provides an overview of deterministic finite automata (DFA) through examples and practice problems. It begins with defining the components of a DFA, including states, alphabet, transition function, start state, and accepting states. An example DFA is given to recognize strings ending in "00". Additional practice problems involve drawing minimal DFAs, determining the minimum number of states for a language, and completing partially drawn DFAs. The document aims to help students learn and practice working with DFA models.
The document discusses algorithms and their analysis. It covers:
1) The definition of an algorithm and its key characteristics like being unambiguous, finite, and efficient.
2) The fundamental steps of algorithmic problem solving like understanding the problem, designing a solution, and analyzing efficiency.
3) Methods for specifying algorithms using pseudocode, flowcharts, or natural language.
4) Analyzing an algorithm's time and space efficiency using asymptotic analysis and orders of growth like best-case, worst-case, and average-case scenarios.
The document discusses finite automata including nondeterministic finite automata (NFAs) and deterministic finite automata (DFAs). It provides examples of NFAs and DFAs that recognize particular strings, including strings containing certain substrings. It also gives examples of DFA state machines and discusses using finite automata to recognize regular languages.
The document discusses compilers and their role in translating high-level programming languages into machine-readable code. It notes that compilers perform several key functions: lexical analysis, syntax analysis, generation of an intermediate representation, optimization of the intermediate code, and finally generation of assembly or machine code. The compiler allows programmers to write code in a high-level language that is easier for humans while still producing efficient low-level code that computers can execute.
The document discusses Turing machines and their properties. It introduces the Church-Turing thesis that any problem that can be solved by an algorithm can be modeled by a Turing machine. It then describes different types of Turing machines, such as multi-track, nondeterministic, two-way, multi-tape, and multidimensional Turing machines. The document provides examples of Turing machines that accept specific languages and evaluate mathematical functions through their transition tables and diagrams.
This document provides an introduction to automata theory and finite automata. It defines an automaton as an abstract computing device that follows a predetermined sequence of operations automatically. A finite automaton has a finite number of states and can be deterministic or non-deterministic. The document outlines the formal definitions and representations of finite automata. It also discusses related concepts like alphabets, strings, languages, and the conversions between non-deterministic and deterministic finite automata. Methods for minimizing deterministic finite automata using Myhill-Nerode theorem and equivalence theorem are also introduced.
This document provides an overview of deterministic finite automata (DFA) through examples and practice problems. It begins with defining the components of a DFA, including states, alphabet, transition function, start state, and accepting states. An example DFA is given to recognize strings ending in "00". Additional practice problems involve drawing minimal DFAs, determining the minimum number of states for a language, and completing partially drawn DFAs. The document aims to help students learn and practice working with DFA models.
This document provides an introduction to finite automata. It defines key concepts like alphabets, strings, languages, and finite state machines. It also describes the different types of automata, specifically deterministic finite automata (DFAs) and nondeterministic finite automata (NFAs). DFAs have a single transition between states for each input, while NFAs can have multiple transitions. NFAs are generally easier to construct than DFAs. The next class will focus on deterministic finite automata in more detail.
The document discusses the role and process of a lexical analyzer in compiler design. A lexical analyzer groups input characters into lexemes and produces a sequence of tokens as output for the syntactic analyzer. It strips out comments and whitespace, correlates line numbers with errors, and interacts with the symbol table. Lexical analysis improves compiler efficiency, portability, and allows for simpler parser design by separating lexical and syntactic analysis.
This document discusses Noam Chomsky's hierarchy of formal languages. It introduces Chomsky's classification of formal languages from Type-0 to Type-3 based on the type of grammar that generates them. Type-0 languages are the most powerful, being generated by unrestricted grammars and equivalent to Turing machines. Type-3 languages are the simplest, being generated by regular grammars and equivalent to finite state automata. Examples are provided for each language type along with the computing models that recognize them, such as pushdown automata for context-free Type-2 languages.
The document provides an introduction to automata theory and finite state automata (FSA). It defines an automaton as an abstract computing device or mathematical model used in computer science and computational linguistics. The reading discusses pioneers in automata theory like Alan Turing and his development of Turing machines. It then gives an overview of finite state automata, explaining concepts like states, transitions, alphabets, and using a example of building an FSA for a "sheeptalk" language to demonstrate these components.
Decision properties of reular languagesSOMNATHMORE2
This document discusses decision properties of regular languages. It defines regular languages as those that can be described by regular expressions and accepted by finite automata. It explains that decision properties are algorithms that take a formal language description and determine properties like emptiness, finiteness, membership in the language, and equivalence to another language. The key decision properties - emptiness, finiteness, membership, and equivalence - are then defined along with the algorithms to determine each. Examples are provided to illustrate the algorithms. Applications of decision properties in areas like data validation and parsing are also mentioned.
This document provides information about the CS416 Compiler Design course, including the instructor details, prerequisites, textbook, grading breakdown, course outline, and an overview of the major parts and phases of a compiler. The course will cover topics such as lexical analysis, syntax analysis using top-down and bottom-up parsing, semantic analysis using attribute grammars, intermediate code generation, code optimization, and code generation.
Context-free languages can be described using context-free grammars, which are recursive rules that generate strings in a language. An example grammar is presented that generates strings of 1s and 0s separated by # symbols. Context-free grammars consist of variables, terminals, rules with variables on the left-hand side replacing with strings on the right-hand side, and a starting variable. Context-free languages can be recognized by pushdown automata using an extra stack. Regular languages are a subset of context-free languages. Context-free languages have closure properties including union, concatenation, and homomorphism. Derivation trees can represent grammar derivations and Backus-Naur form is a notation for compactly representing
Turing machines are a simple mathematical model of computation that was introduced by Alan Turing in 1936. A Turing machine consists of a finite set of states, an infinite tape divided into cells, and a head that can read and write symbols on the tape. It operates based on a transition function that changes the state and head position based on the current state and symbol. Turing machines can be used as language acceptors by accepting inputs that cause them to halt in an accepting state, or as transducers by treating the initial tape as input and final tape as output. Variations include multi-tape, non-deterministic, multi-head, and multi-dimensional Turing machines. Turing machines are useful for determining decid
This document summarizes semantic analysis in compiler design. Semantic analysis computes additional meaning from a program by adding information to the symbol table and performing type checking. Syntax directed translations relate a program's meaning to its syntactic structure using attribute grammars. Attribute grammars assign attributes to grammar symbols and compute attribute values using semantic rules associated with grammar productions. Semantic rules are evaluated in a bottom-up manner on the parse tree to perform tasks like code generation and semantic checking.
This document discusses parsing and context-free grammars. It defines parsing as verifying that tokens generated by a lexical analyzer follow syntactic rules of a language using a parser. Context-free grammars are defined using terminals, non-terminals, productions and a start symbol. Top-down and bottom-up parsing are introduced. Techniques for grammar analysis and improvement like left factoring, eliminating left recursion, calculating first and follow sets are explained with examples.
This document provides an overview of algorithms and algorithm analysis. It discusses key concepts like what an algorithm is, different types of algorithms, and the algorithm design and analysis process. Some important problem types covered include sorting, searching, string processing, graph problems, combinatorial problems, geometric problems, and numerical problems. Examples of specific algorithms are given for some of these problem types, like various sorting algorithms, search algorithms, graph traversal algorithms, and algorithms for solving the closest pair and convex hull problems.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
Lecture: Regular Expressions and Regular LanguagesMarina Santini
This document provides an introduction to regular expressions and regular languages. It defines the key operations used in regular expressions: union, concatenation, and Kleene star. It explains how regular expressions can be converted into finite state automata and vice versa. Examples of regular expressions are provided. The document also defines regular languages as those languages that can be accepted by a deterministic finite automaton. It introduces the pumping lemma as a way to determine if a language is not regular. Finally, it includes some practical activities for readers to practice converting regular expressions to automata and writing regular expressions.
A compiler is a program that translates a program written in one language into an equivalent target language. The front end checks syntax and semantics, while the back end translates the source code into assembly code. The compiler performs lexical analysis, syntax analysis, semantic analysis, code generation, optimization, and error handling. It identifies errors at compile time to help produce efficient, error-free code.
Mealy and Moore machines are types of finite state machines. A Mealy machine's output depends on its present state and input, while a Moore machine's output depends only on its present state. Mealy machines can be converted to Moore machines by breaking states with multiple outputs into multiple states, and vice versa by combining states with the same output. Both machine types have advantages and uses, with Mealy machines being faster but more expensive, and Moore machines being simpler but slower.
This presentation discusses context-free grammars. It defines context-free grammars and provides an example. It also discusses parse trees, including how they are generated and different types (top-down and bottom-up). Examples are provided to demonstrate leftmost and rightmost derivations and parse trees. The document concludes that the grammar presented, with production rules of X → X+X | X*X |X| a, is ambiguous as there are two possible parse trees for the string "a+a*a".
Prolog is a declarative logic programming language where programs consist of facts and rules. Facts are terms that are always true, while rules define relationships between terms using logic notation "if-then". A Prolog program is run by asking queries of the program's database. Variables must start with an uppercase letter and are used to represent unknown values, while atoms are constants that represent known values.
Here is the NFA in formal notation:
Q = {q0, q1, q2}
Σ = {a, b}
q0 = q1 (the initial state)
F = {q0} (the single accepting state)
q0
The transition function δ is:
a
q1 → {q0, q2}
q2 → {q0}
b
This NFA accepts ε, a, baba, baa, and aa since there is a path from the initial state q1 to the accepting state q0 under those inputs. It does not accept b, bb, or babba since there is no path from
In automata theory, a deterministic pushdown automaton (DPDA or DPA) is a variation of the pushdown automaton. The DPDA accepts the deterministic context-free languages, a proper subset of context-free languages. Machine transitions are based on the current state and input symbol, and also the current topmost symbol of the stack. Symbols lower in the stack are not visible and have no immediate effect. Machine actions include pushing, popping, or replacing the stack top. A deterministic pushdown automaton has at most one legal transition for the same combination of input symbol, state, and top stack symbol. This is where it differs from the nondeterministic pushdown automaton.
The document discusses context free grammars and related concepts. It defines context free grammars and provides examples. It also discusses Chomsky hierarchy, classifying grammars into types 0-3 (unrestricted to regular) based on production rules. Formal languages generated by each grammar type are described along with their properties and closure properties. Context free grammars are defined in more detail, covering derivation, Backus-Naur form, and leftmost and rightmost derivations.
This document provides an introduction to finite automata. It defines key concepts like alphabets, strings, languages, and finite state machines. It also describes the different types of automata, specifically deterministic finite automata (DFAs) and nondeterministic finite automata (NFAs). DFAs have a single transition between states for each input, while NFAs can have multiple transitions. NFAs are generally easier to construct than DFAs. The next class will focus on deterministic finite automata in more detail.
The document discusses the role and process of a lexical analyzer in compiler design. A lexical analyzer groups input characters into lexemes and produces a sequence of tokens as output for the syntactic analyzer. It strips out comments and whitespace, correlates line numbers with errors, and interacts with the symbol table. Lexical analysis improves compiler efficiency, portability, and allows for simpler parser design by separating lexical and syntactic analysis.
This document discusses Noam Chomsky's hierarchy of formal languages. It introduces Chomsky's classification of formal languages from Type-0 to Type-3 based on the type of grammar that generates them. Type-0 languages are the most powerful, being generated by unrestricted grammars and equivalent to Turing machines. Type-3 languages are the simplest, being generated by regular grammars and equivalent to finite state automata. Examples are provided for each language type along with the computing models that recognize them, such as pushdown automata for context-free Type-2 languages.
The document provides an introduction to automata theory and finite state automata (FSA). It defines an automaton as an abstract computing device or mathematical model used in computer science and computational linguistics. The reading discusses pioneers in automata theory like Alan Turing and his development of Turing machines. It then gives an overview of finite state automata, explaining concepts like states, transitions, alphabets, and using a example of building an FSA for a "sheeptalk" language to demonstrate these components.
Decision properties of reular languagesSOMNATHMORE2
This document discusses decision properties of regular languages. It defines regular languages as those that can be described by regular expressions and accepted by finite automata. It explains that decision properties are algorithms that take a formal language description and determine properties like emptiness, finiteness, membership in the language, and equivalence to another language. The key decision properties - emptiness, finiteness, membership, and equivalence - are then defined along with the algorithms to determine each. Examples are provided to illustrate the algorithms. Applications of decision properties in areas like data validation and parsing are also mentioned.
This document provides information about the CS416 Compiler Design course, including the instructor details, prerequisites, textbook, grading breakdown, course outline, and an overview of the major parts and phases of a compiler. The course will cover topics such as lexical analysis, syntax analysis using top-down and bottom-up parsing, semantic analysis using attribute grammars, intermediate code generation, code optimization, and code generation.
Context-free languages can be described using context-free grammars, which are recursive rules that generate strings in a language. An example grammar is presented that generates strings of 1s and 0s separated by # symbols. Context-free grammars consist of variables, terminals, rules with variables on the left-hand side replacing with strings on the right-hand side, and a starting variable. Context-free languages can be recognized by pushdown automata using an extra stack. Regular languages are a subset of context-free languages. Context-free languages have closure properties including union, concatenation, and homomorphism. Derivation trees can represent grammar derivations and Backus-Naur form is a notation for compactly representing
Turing machines are a simple mathematical model of computation that was introduced by Alan Turing in 1936. A Turing machine consists of a finite set of states, an infinite tape divided into cells, and a head that can read and write symbols on the tape. It operates based on a transition function that changes the state and head position based on the current state and symbol. Turing machines can be used as language acceptors by accepting inputs that cause them to halt in an accepting state, or as transducers by treating the initial tape as input and final tape as output. Variations include multi-tape, non-deterministic, multi-head, and multi-dimensional Turing machines. Turing machines are useful for determining decid
This document summarizes semantic analysis in compiler design. Semantic analysis computes additional meaning from a program by adding information to the symbol table and performing type checking. Syntax directed translations relate a program's meaning to its syntactic structure using attribute grammars. Attribute grammars assign attributes to grammar symbols and compute attribute values using semantic rules associated with grammar productions. Semantic rules are evaluated in a bottom-up manner on the parse tree to perform tasks like code generation and semantic checking.
This document discusses parsing and context-free grammars. It defines parsing as verifying that tokens generated by a lexical analyzer follow syntactic rules of a language using a parser. Context-free grammars are defined using terminals, non-terminals, productions and a start symbol. Top-down and bottom-up parsing are introduced. Techniques for grammar analysis and improvement like left factoring, eliminating left recursion, calculating first and follow sets are explained with examples.
This document provides an overview of algorithms and algorithm analysis. It discusses key concepts like what an algorithm is, different types of algorithms, and the algorithm design and analysis process. Some important problem types covered include sorting, searching, string processing, graph problems, combinatorial problems, geometric problems, and numerical problems. Examples of specific algorithms are given for some of these problem types, like various sorting algorithms, search algorithms, graph traversal algorithms, and algorithms for solving the closest pair and convex hull problems.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
Lecture: Regular Expressions and Regular LanguagesMarina Santini
This document provides an introduction to regular expressions and regular languages. It defines the key operations used in regular expressions: union, concatenation, and Kleene star. It explains how regular expressions can be converted into finite state automata and vice versa. Examples of regular expressions are provided. The document also defines regular languages as those languages that can be accepted by a deterministic finite automaton. It introduces the pumping lemma as a way to determine if a language is not regular. Finally, it includes some practical activities for readers to practice converting regular expressions to automata and writing regular expressions.
A compiler is a program that translates a program written in one language into an equivalent target language. The front end checks syntax and semantics, while the back end translates the source code into assembly code. The compiler performs lexical analysis, syntax analysis, semantic analysis, code generation, optimization, and error handling. It identifies errors at compile time to help produce efficient, error-free code.
Mealy and Moore machines are types of finite state machines. A Mealy machine's output depends on its present state and input, while a Moore machine's output depends only on its present state. Mealy machines can be converted to Moore machines by breaking states with multiple outputs into multiple states, and vice versa by combining states with the same output. Both machine types have advantages and uses, with Mealy machines being faster but more expensive, and Moore machines being simpler but slower.
This presentation discusses context-free grammars. It defines context-free grammars and provides an example. It also discusses parse trees, including how they are generated and different types (top-down and bottom-up). Examples are provided to demonstrate leftmost and rightmost derivations and parse trees. The document concludes that the grammar presented, with production rules of X → X+X | X*X |X| a, is ambiguous as there are two possible parse trees for the string "a+a*a".
Prolog is a declarative logic programming language where programs consist of facts and rules. Facts are terms that are always true, while rules define relationships between terms using logic notation "if-then". A Prolog program is run by asking queries of the program's database. Variables must start with an uppercase letter and are used to represent unknown values, while atoms are constants that represent known values.
Here is the NFA in formal notation:
Q = {q0, q1, q2}
Σ = {a, b}
q0 = q1 (the initial state)
F = {q0} (the single accepting state)
q0
The transition function δ is:
a
q1 → {q0, q2}
q2 → {q0}
b
This NFA accepts ε, a, baba, baa, and aa since there is a path from the initial state q1 to the accepting state q0 under those inputs. It does not accept b, bb, or babba since there is no path from
In automata theory, a deterministic pushdown automaton (DPDA or DPA) is a variation of the pushdown automaton. The DPDA accepts the deterministic context-free languages, a proper subset of context-free languages. Machine transitions are based on the current state and input symbol, and also the current topmost symbol of the stack. Symbols lower in the stack are not visible and have no immediate effect. Machine actions include pushing, popping, or replacing the stack top. A deterministic pushdown automaton has at most one legal transition for the same combination of input symbol, state, and top stack symbol. This is where it differs from the nondeterministic pushdown automaton.
The document discusses context free grammars and related concepts. It defines context free grammars and provides examples. It also discusses Chomsky hierarchy, classifying grammars into types 0-3 (unrestricted to regular) based on production rules. Formal languages generated by each grammar type are described along with their properties and closure properties. Context free grammars are defined in more detail, covering derivation, Backus-Naur form, and leftmost and rightmost derivations.
This document discusses regular languages and grammars. It begins by defining formal languages and describing two approaches to describing languages: the generative approach using grammars and the recognition approach using automata. It then discusses Noam Chomsky's hierarchy of formal grammars and how this classifies the expressive power of grammars. Regular languages are those described by regular grammars and recognized by finite automata. Regular expressions provide another way to describe regular languages. The document proves the equivalence between regular expressions, regular grammars, and finite automata by showing how to systematically construct automata from regular expressions and vice versa.
Are Natural Languages Regular? This is an important question for two reasons: first, it places an upper bound on the running time of algorithms that process natural language; second, it may tell us something about human language processing and language acquisition.
The document discusses context-free languages and context-free grammars. It defines context-free languages as languages generated by context-free grammars. Context-free grammars can be defined as a 4-tuple consisting of variables, terminals, production rules, and a start symbol. The document lists some properties of context-free languages, including that they are closed under union, concatenation, and Kleene star, but not intersection or complement. It also provides examples of languages that are and aren't context-free.
This document provides an introduction to formal language theory and computational linguistics concepts. It defines key terms like formal grammars, automata, regular expressions, and Chomsky hierarchy. Finite state automata and regular grammars are discussed as the simplest types that can recognize formal languages. Context-free grammars allow more complex languages but are still parseable efficiently, unlike the most complex unrestricted grammars. Transition graphs and diagrams are presented as ways to visually represent automata and their state transitions.
This document provides an introduction to formal language theory and defines several key concepts:
1) It defines formal languages and grammars, including Chomsky hierarchy which categorizes languages based on grammar complexity.
2) It introduces finite state automata as machines that read input sequences and transition between states, accepting or rejecting words based on reaching an accepting state.
3) It defines concepts like Kleene closure and regular expressions which are used to describe languages recognized by finite state automata.
This document discusses the Chomsky hierarchy of formal grammars and languages. It provides biographies of Noam Chomsky and Marcel Schützenberger who collaborated in developing the hierarchy. The hierarchy consists of four classes - type-0 unrestricted grammars generating recursively enumerable languages, type-1 context-sensitive grammars, type-2 context-free grammars, and type-3 regular grammars. Each class is more restrictive and generates a proper subset of the languages of the previous class. The classes are defined by the types of rules allowed in their grammars and the automata able to recognize their languages. The hierarchy provides a framework for understanding the expressive capabilities of grammar systems.
The document describes the syllabus for the course "Formal Languages and Automata Theory". It contains:
- 8 units covering topics like introduction to finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, and more.
- Details of each unit including hours, chapters covered from the textbook, and topics discussed.
- Information about internal assessment and exams, including marks distribution.
- Names of two recommended textbooks and their relevant chapters.
- A table of contents listing the topics covered in each unit and their page numbers.
The document discusses various topics related to formal languages and automata theory including:
- Definitions of alphabets, strings, regular expressions, and formal languages. Regular expressions can be used to represent regular languages.
- Four types of grammars (Type-0 to Type-3) with Type-3 grammars generating regular languages and Type-2 grammars generating context-free languages.
- Components of a grammar including nonterminal symbols, terminal symbols, rules, and a starting symbol.
- Turing machines and their components including states, tape alphabet, transition function, initial/final states, and blank symbol.
- Decidability and reducibility. The halting problem is un
Deterministic context free grammars &non-deterministicLeyo Stephen
Deterministic context-free grammars are always unambiguous, while there are non-deterministic unambiguous grammars. The problem of determining if a grammar is ambiguous is undecidable in general. Many languages can have both ambiguous and unambiguous grammars, but some languages only admit ambiguous grammars and are considered inherently ambiguous.
Natural Language Processing Topics for Engineering studentsRosnaPHaroon
The document discusses various types of linguistic knowledge required for natural language processing, including knowledge of morphology, syntax, semantics, pragmatics, and discourse. It provides examples to illustrate morphological concepts like inflection, derivation, and compounding. It also describes how finite state automata can be used to model parts of a language's formal grammar and recognize strings through state transition tables and algorithms.
Regular expressions provide a powerful method for describing patterns in text and strings. They represent formal languages through operations like union, intersection, and Kleene closure. A regular language is one that can be expressed by a regular expression and defines a set of strings over a specific alphabet that satisfy certain properties. Regular languages play an important role in computer science applications for tasks like text searching, parsing, and pattern recognition.
The document discusses the Chomsky hierarchy, which classifies formal languages according to the type of grammar used to generate them. The hierarchy consists of 4 types: type-0 languages are partially computable, type-1 are context sensitive, type-2 are context free, and type-3 are regular. The document also discusses properties of regular, context free, computable, and partially computable languages, including examples, methods of proving a language's type, and closure properties.
Undecidability refers to problems that cannot be solved algorithmically. Alan Turing first proved the existence of undecidable problems in 1936. There are decidable problems that can be solved by a Turing machine in finite time, and undecidable problems for which no Turing machine can provide a definitive yes or no answer. Examples of undecidable problems include determining if a context-free grammar is ambiguous or if two context-free languages are equal. Rice's theorem states that any non-trivial semantic property of a language recognized by a Turing machine is undecidable. Undecidability has important implications in language theory and for analyzing programs and computational problems.
Introduction to the theory of computationprasadmvreddy
This document provides an introduction and overview of topics in the theory of computation including automata, computability, and complexity. It discusses the following key points in 3 sentences:
Automata theory, computability theory, and complexity theory examine the fundamental capabilities and limitations of computers. Different models of computation are introduced including finite automata, context-free grammars, and Turing machines. The document then provides definitions and examples of regular languages and context-free grammars, the basics of finite automata and regular expressions, properties of regular languages, and limitations of finite state machines.
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The document discusses theoretical models of computation called machines. It explains that machines will be defined mathematically and their capabilities analyzed by considering the types of inputs they can successfully operate on, called the machine's language. The relationship between machines and the languages they correspond to will be used to investigate problems and potential algorithmic solutions.
Similar to Types of Language in Theory of Computation (20)
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
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How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
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Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
1. TYPESOF LANGUAGES
Name : Ankur
Enrollment Number : 140950107005
Subject :Theory Of Computation
Class : 6th A
Computer Science and Engineering
2. INTRODUCTION
Language theory is a branch of mathematics
concerned with describing languages as a set of
operations over an alphabet. It is closely linked with
automata theory, as automata are used to generate
and recognize formal languages.There are several
classes of formal languages, each allowing more
complex language specification than the one
before it, i.e. Chomsky hierarchy, and each
corresponding to a class of automata which
recognizes it. Because automata are used as
models for computation, formal languages are the
preferred mode of specification for any problem
that must be computed.
3. In the formal languages of computer
science and linguistics, the Chomsky hierarchy is
a containment hierarchy of classes of formal
grammars.This hierarchy of grammars was described
by Noam Chomsky in 1956.
A formal grammar of this type consists of a finite set
of production rules (left-hand side → right-hand side),
where each side consists of a finite sequence of the
following symbols:
a finite set of nonterminal symbols (indicating that
some production rule can yet be applied)
a finite set of terminal symbols (indicating that no
production rule can be applied)
a start symbol (a distinguished nonterminal symbol)
4. TYPESOF
LANGUAGES
Grammar Languages Automaton
Type-0 Recursively enumerable Turing machine
Type-1 Context-sensitive
Linear-bounded non-
deterministicTuring
machine
Type-2 Context-free
Non-
deterministic pushdown
automaton
Type-3 Regular Finite state automaton
5. RECURSIVELY
ENUMERABL
E LANGUAGE
A recursively enumerable language is a recursively
enumerable subset in the set of all possible words over the
alphabet of the language. A recursively enumerable
language is a formal language for which there exists a
Turing machine (or other computable function) which will
enumerate all valid strings of the language.
In mathematics, logic and computer science, a formal
language is called recursively
enumerable also recognizable, partially
decidable, semidecidable, Turing-acceptable or Turing-
recognizable, if it is a recursively enumerable subset in
the set of all possible words over the alphabet of the
language, i.e., if there exists aTuring machine which will
enumerate all valid strings of the language.
Recursively enumerable languages are known as type-
0 languages in the Chomsky hierarchy of formal languages.
All regular, context-free, context-
sensitive and recursive languages are recursively
enumerable.The class of all recursively enumerable
languages is called RE.
6. There are three equivalent definitions of a recursively enumerable
language:
A recursively enumerable language is a recursively
enumerable subset in the set of all possible words over
the alphabet of the language.
A recursively enumerable language is a formal language for which
there exists aTuring machine (or other computable function) which
will enumerate all valid strings of the language. Note that if the
language is infinite, the enumerating algorithm provided can be
chosen so that it avoids repetitions, since we can test whether the
string produced for number n is "already" produced for a number
which is less than n. If it already is produced, use the output for
input n+1 instead (recursively), but again, test whether it is "new".
A recursively enumerable language is a formal language for which
there exists aTuring machine (or other computable function) that
will halt and accept when presented with any string in the language
as input but may either halt and reject or loop forever when
presented with a string not in the language. Contrast this
to recursive languages, which require that theTuring machine halts
in all cases.
All regular, context-free, context-sensitive and recursive languages are
recursively enumerable.
7. Example
The halting problem is recursively enumerable but
not recursive. Indeed one can run theTuring
Machine and accept if the machine halts, hence it is
recursively enumerable. On the other hand the
problem is undecidable. In computability theory,
the Halting problem is the problem of
determining, from a description of an
arbitrary computer program and an input, whether
the program will finish running or continue to run
forever.
For example, in pseudocode, the program
while (true) continue
does not halt; rather, it goes on forever in an infinite
loop. On the other hand, the program
print "Hello, world!“
does halt.
8. Closure
Properties
Recursively enumerable languages are closed under
the following operations.That is, if L and P are two
recursively enumerable languages, then the
following languages are recursively enumerable as
well:
the Kleene star L* of L
the concatenation L.P of L and P
the union L U P
the intersection L ∩ P
Recursively enumerable languages are not closed
under set difference or complementation.The set
difference L - P may or may not be recursively
enumerable. If L is recursively enumerable, then the
complement of L is recursively enumerable if and
only if L is also recursive.
9. CONTEXT-
SENSITIVE
LANGUAGE
Type-1 grammars generate the context-sensitive
languages.These grammars have rules of the
form αAβ -> αγβ with A a nonterminal and α,
β and γ strings of terminals and/or non-terminals.
The strings α and β may be empty, but γ must be
nonempty.The rule S -> ε is allowed if S does not
appear on the right side of any rule.The languages
described by these grammars are exactly all
languages that can be recognized by a linear
bounded automaton (a nondeterministicTuring
machine whose tape is bounded by a constant
times the length of the input.)
In theoretical computer science, a context-
sensitive language is a formal language that can
be defined by a context-sensitive grammar (and
equivalently by a noncontracting grammar).
10. Computational
Properties
Computationally, a context-sensitive language is
equivalent with a linear bounded nondeterministic
Turing machine, also called a linear bounded
automaton.That is a non-deterministicTuring
machine with a tape of only kn cells, where n is the
size of the input and k is a constant associated with
the machine.This means that every formal
language that can be decided by such a machine is
a context-sensitive language, and every context-
sensitive language can be decided by such a
machine.
This set of languages is also known
as NLINSPACE or NSPACE(O(n)), because they can
be accepted using linear space on a non-
deterministicTuring machine.
11. Example
One of the simplest context-sensitive but not context-
free languages is L = {anbncn : n ≥ 1}: the language of all
strings consisting of n occurrences of the symbol "a",
then n "b"'s, then n "c"'s (abc, aabbcc, aaabbbccc,
etc.). A superset of this language, called the Bach
language, is defined as the set of all strings where "a",
"b" and "c" (or any other set of three symbols) occurs
equally often (aabccb, baabcaccb, etc.) and is also
context-sensitive.
L can be shown to be a context-sensitive language by
constructing a linear bounded automaton which
accepts L.The language can easily be shown to be
neither regular nor context free by applying the
respective pumping lemmas for each of the language
classes to L.
An example of recursive language that is not context-
sensitive is any recursive language whose decision is
an EXPSPACE-hard problem, say, the set of pairs of
equivalent regular expressions with exponentiation.
12. Closure
Properties
The union, intersection, concatenation of two
context-sensitive languages is context-sensitive;
the Kleene plus of a context-sensitive language is
context-sensitive.
The complement of a context-sensitive language is
itself context-sensitive a result known as
the Immerman–Szelepcsényi theorem.
Membership of a string in a language defined by an
arbitrary context-sensitive grammar, or by an
arbitrary deterministic context-sensitive grammar,
is a PSPACE-complete problem.
13. CONTEXT-
FREE
LANGUAGE
Type-2 grammars generate the context-free
languages.These are defined by rules of the form A ->
γ with A a nonterminal and γ a string of terminals
and/or non-terminals.These languages are exactly all
languages that can be recognized by a non-
deterministic pushdown automaton. Context-free
languages—or rather its subset of deterministic
context-free language—are the theoretical basis for
the phrase structure of most programming languages,
though their syntax also includes context-sensitive
name resolution due to declarations and scope.Often
a subset of grammars are used to make parsing easier,
such as by an LL parser. In formal language theory,
a context-free language (CFL) is
a language generated by a context-free
grammar (CFG).
The set of all context-free languages is identical to the
set of languages accepted by pushdown automata,
which makes these languages amenable to parsing.
Further, for a given a CFG, there is a direct way to
produce a pushdown automaton for the grammar,
though going the other way (producing a grammar
given an automaton) is not as direct.
14. Example
A model context-free language is L = {anbncn : n ≥ 1} : the
language of all non-empty even-length strings, the
entire first halves of which are a's, and the entire second
halves of which are b's. L is generated by the grammar S
-> aSb|ab. This language is not regular. It is accepted by
the pushdown automaton M=({q0,q1,qf},{a,b},{a,z},δ
,q0,z,{qf}) where δ is defined as follows:
δ(q0,a,z)=(q0,az)
δ(q0,a,a)=(q0,aa)
δ(q0,b,a)=(q1, ε)
δ(q1,b,a)=(q1,ε)
Unambiguous CFLs are a proper subset of all CFLs: there
are inherently ambiguous CFLs. An example of an
inherently ambiguous CFL is the union of {anbmcmdm|n,m
> 0 } with {anbncmdm|n,m > 0 }.This set is context-free,
since the union of two context-free languages is always
context-free. But there is no way to unambiguously
parse strings in the (non-context-free)
subset {anbncndn|n,m > 0 } which is the intersection of
these two languages.
15. Properties
• Context-free parsing:
The context-free nature of the language makes it simple to
parse with a pushdown automaton.
Determining an instance of the membership problem; i.e.
given a string ω, determine whether ω ϵ L(G) where L is
the language generated by a given grammar G is also
known as recognition.
Practical uses of context-free languages require also to
produce a derivation tree that exhibits the structure that
the grammar associates with the given string.The process
of producing this tree is called parsing. Known parsers have
a time complexity that is cubic in the size of the string that
is parsed.
Formally, the set of all context-free languages is identical to
the set of languages accepted by pushdown
automata(PDA).
A special subclass of context-free languages are
the deterministic context-free languages which are defined
as the set of languages accepted by a deterministic
pushdown automaton and can be parsed by a LR(k) parser.
16. • Closure Properties:
Context-free languages are closed under the
following operations.That is, if L and P are
context-free languages, the following
languages are context-free as well:
1.the union L U P of L and P
2.the reversal of L
3.the concatenation L.P of L and P
4.the Kleene star L* of L
5.the cyclic shift of L (the language { vu : uv ϵ
L })
Context-free languages are not closed
under complement, intersection,
or difference.
17. REGULAR
LANGUAGE
• In theoretical computer science and formal language
theory, a regular language (also called a rational
language) is a formal language that can be expressed
using a regular expression.
• Type-3 grammars generate the regular languages.
Such a grammar restricts its rules to a single
nonterminal on the left-hand side and a right-hand
side consisting of a single terminal, possibly followed
by a single nonterminal (right regular).Alternatively,
the right-hand side of the grammar can consist of a
single terminal, possibly preceded by a single
nonterminal (left regular).These generate the same
languages. However, if left-regular rules and right-
regular rules are combined, the language need no
longer be regular.The rule S -> ε is also allowed here
if S does not appear on the right side of any rule.
These languages are exactly all languages that can be
decided by a finite state automaton.Additionally, this
family of formal languages can be obtained by regular
expressions. Regular languages are commonly used to
define search patterns and the lexical structure of
programming languages.
18. • Alternatively, a regular language can be defined
as a language recognized by a finite automaton.
The equivalence of regular expressions and finite
automata is known as Kleene's theorem.
• The collection of regular languages over
an alphabet Σ is defined recursively as follows:
1. The empty languageØ, and the empty string
language {ε} are regular languages.
2. For each a ∈ Σ (a belongs to Σ),
the singleton language {a} is a regular
language.
3. If A and B are regular languages,
then A ∪ B (union), A • B (concatenation),
and A* (Kleene star) are regular languages.
4. No other languages over Σ are regular.
19. Example
All finite languages are regular; in
particular the empty string language {ε}
= Ø* is regular. Other typical examples
include the language consisting of all
strings over the alphabet {a, b} which
contain an even number of a’s, or the
language consisting of all strings of the
form: several a’s followed by several b’s.
A simple example of a language that is
not regular is the set of strings { anbn | n ≥
0 }. Intuitively, it cannot be recognized
with a finite automaton, since a finite
automaton has finite memory and it
cannot remember the exact number of
a's.
20. Closure
Properties
The regular languages
are closed under the various
operations, that is, if the
languages K and L are regular, so is the
result of the following operations:
• the set theoretic Boolean
operations: union K ∪ L, intersection
K ∩ L, and complement L, hence
also relative complement K-L.
• the regular
operations: K ∪ L, concatenation K ∘
L, and Kleene star L*.
• the reverse (or mirror image) LR .