Automata Theory 4Th
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Theory of Computer Science
Theory of Computer Science MODULE-1 Alphabet A finite set of symbols is denoted by ∑. Language A language is defined as a set of strings of symbols over an alphabet. Language of a Machine Set of all accepted strings of a machine is called language of a machine. Grammar Grammar is the set of rules that generates language. CHOMSKY HIERARCHY OF LANGUAGES Type 0-Language:-Unrestricted Language, Accepter:-Turing Machine, Generetor:-Unrestricted Grammar Type 1- Language:-Context Sensitive Language, Accepter:- Linear Bounded Automata, Generetor:-Context Sensitive Grammar Type 2- Language:-Context Free Language, Accepter:-Push Down Automata, Generetor:- Context Free Grammar Type 3- Language:-Regular Language, Accepter:- Finite Automata, Generetor:-Regular Grammar Finite Automata Finite Automata is generally of two types :(i)Deterministic Finite Automata (DFA)(ii)Non Deterministic Finite Automata(NFA) DFA DFA is represented formally by a 5-tuple (Q,Σ,δ,q0,F), where: Q is a finite set of states. Σ is a finite set of symbols, called the alphabet of the automaton. δ is the transition function, that is, δ: Q × Σ → Q. q0 is the start state, that is, the state of the automaton before any input has been processed, where q0 Q. F is a set of states of Q (i.e. F Q) called accept states or Final States 1. Construct a DFA that accepts set of all strings over ∑={0,1}, ending with 00 ? 1 0 A B S State/Input 0 1 1 A B A 0 B C A 1 * C C A C 0 2. Construct a DFA that accepts set of all strings over ∑={0,1}, not containing 101 as a substring ? 0 1 1 A B State/Input 0 1 S *A A B 0 *B C B 0 0,1 *C A R C R R R R 1 NFA NFA is represented formally by a 5-tuple (Q,Σ,δ,q0,F), where: Q is a finite set of states. -
Data Structure
EDUSAT LEARNING RESOURCE MATERIAL ON DATA STRUCTURE (For 3rd Semester CSE & IT) Contributors : 1. Er. Subhanga Kishore Das, Sr. Lect CSE 2. Mrs. Pranati Pattanaik, Lect CSE 3. Mrs. Swetalina Das, Lect CA 4. Mrs Manisha Rath, Lect CA 5. Er. Dillip Kumar Mishra, Lect 6. Ms. Supriti Mohapatra, Lect 7. Ms Soma Paikaray, Lect Copy Right DTE&T,Odisha Page 1 Data Structure (Syllabus) Semester & Branch: 3rd sem CSE/IT Teachers Assessment : 10 Marks Theory: 4 Periods per Week Class Test : 20 Marks Total Periods: 60 Periods per Semester End Semester Exam : 70 Marks Examination: 3 Hours TOTAL MARKS : 100 Marks Objective : The effectiveness of implementation of any application in computer mainly depends on the that how effectively its information can be stored in the computer. For this purpose various -structures are used. This paper will expose the students to various fundamentals structures arrays, stacks, queues, trees etc. It will also expose the students to some fundamental, I/0 manipulation techniques like sorting, searching etc 1.0 INTRODUCTION: 04 1.1 Explain Data, Information, data types 1.2 Define data structure & Explain different operations 1.3 Explain Abstract data types 1.4 Discuss Algorithm & its complexity 1.5 Explain Time, space tradeoff 2.0 STRING PROCESSING 03 2.1 Explain Basic Terminology, Storing Strings 2.2 State Character Data Type, 2.3 Discuss String Operations 3.0 ARRAYS 07 3.1 Give Introduction about array, 3.2 Discuss Linear arrays, representation of linear array In memory 3.3 Explain traversing linear arrays, inserting & deleting elements 3.4 Discuss multidimensional arrays, representation of two dimensional arrays in memory (row major order & column major order), and pointers 3.5 Explain sparse matrices. -
Grammars (Cfls & Cfgs) Reading: Chapter 5
Context-Free Languages & Grammars (CFLs & CFGs) Reading: Chapter 5 1 Not all languages are regular So what happens to the languages which are not regular? Can we still come up with a language recognizer? i.e., something that will accept (or reject) strings that belong (or do not belong) to the language? 2 Context-Free Languages A language class larger than the class of regular languages Supports natural, recursive notation called “context- free grammar” Applications: Context- Parse trees, compilers Regular free XML (FA/RE) (PDA/CFG) 3 An Example A palindrome is a word that reads identical from both ends E.g., madam, redivider, malayalam, 010010010 Let L = { w | w is a binary palindrome} Is L regular? No. Proof: N N (assuming N to be the p/l constant) Let w=0 10 k By Pumping lemma, w can be rewritten as xyz, such that xy z is also L (for any k≥0) But |xy|≤N and y≠ + ==> y=0 k ==> xy z will NOT be in L for k=0 ==> Contradiction 4 But the language of palindromes… is a CFL, because it supports recursive substitution (in the form of a CFG) This is because we can construct a “grammar” like this: Same as: 1. A ==> A => 0A0 | 1A1 | 0 | 1 | Terminal 2. A ==> 0 3. A ==> 1 Variable or non-terminal Productions 4. A ==> 0A0 5. A ==> 1A1 How does this grammar work? 5 How does the CFG for palindromes work? An input string belongs to the language (i.e., accepted) iff it can be generated by the CFG G: Example: w=01110 A => 0A0 | 1A1 | 0 | 1 | G can generate w as follows: Generating a string from a grammar: 1. -
Cs 61A/Cs 98-52
CS 61A/CS 98-52 Mehrdad Niknami University of California, Berkeley Mehrdad Niknami (UC Berkeley) CS 61A/CS 98-52 1 / 23 Something like this? (Is this good?) def find(string, pattern): n= len(string) m= len(pattern) for i in range(n-m+ 1): is_match= True for j in range(m): if pattern[j] != string[i+ j] is_match= False break if is_match: return i What if you were looking for a pattern? Like an email address? Motivation How would you find a substring inside a string? Mehrdad Niknami (UC Berkeley) CS 61A/CS 98-52 2 / 23 def find(string, pattern): n= len(string) m= len(pattern) for i in range(n-m+ 1): is_match= True for j in range(m): if pattern[j] != string[i+ j] is_match= False break if is_match: return i What if you were looking for a pattern? Like an email address? Motivation How would you find a substring inside a string? Something like this? (Is this good?) Mehrdad Niknami (UC Berkeley) CS 61A/CS 98-52 2 / 23 What if you were looking for a pattern? Like an email address? Motivation How would you find a substring inside a string? Something like this? (Is this good?) def find(string, pattern): n= len(string) m= len(pattern) for i in range(n-m+ 1): is_match= True for j in range(m): if pattern[j] != string[i+ j] is_match= False break if is_match: return i Mehrdad Niknami (UC Berkeley) CS 61A/CS 98-52 2 / 23 Motivation How would you find a substring inside a string? Something like this? (Is this good?) def find(string, pattern): n= len(string) m= len(pattern) for i in range(n-m+ 1): is_match= True for j in range(m): if pattern[j] != string[i+ -
Precise Analysis of String Expressions
Precise Analysis of String Expressions Aske Simon Christensen, Anders Møller?, and Michael I. Schwartzbach BRICS??, Department of Computer Science University of Aarhus, Denmark aske,amoeller,mis @brics.dk f g Abstract. We perform static analysis of Java programs to answer a simple question: which values may occur as results of string expressions? The answers are summarized for each expression by a regular language that is guaranteed to contain all possible values. We present several ap- plications of this analysis, including statically checking the syntax of dynamically generated expressions, such as SQL queries. Our analysis constructs flow graphs from class files and generates a context-free gram- mar with a nonterminal for each string expression. The language of this grammar is then widened into a regular language through a variant of an algorithm previously used for speech recognition. The collection of resulting regular languages is compactly represented as a special kind of multi-level automaton from which individual answers may be extracted. If a program error is detected, examples of invalid strings are automat- ically produced. We present extensive benchmarks demonstrating that the analysis is efficient and produces results of useful precision. 1 Introduction To detect errors and perform optimizations in Java programs, it is useful to know which values that may occur as results of string expressions. The exact answer is of course undecidable, so we must settle for a conservative approxima- tion. The answers we provide are summarized for each expression by a regular language that is guaranteed to contain all its possible values. Thus we use an upper approximation, which is what most client analyses will find useful. -
Language and Automata Theory and Applications
LANGUAGE AND AUTOMATA THEORY AND APPLICATIONS Carlos Martín-Vide Characterization • It deals with the description of properties of sequences of symbols • Such an abstract characterization explains the interdisciplinary flavour of the field • The theory grew with the need of formalizing and describing the processes linked with the use of computers and communication devices, but its origins are within mathematical logic and linguistics A bit of history • Early roots in the work of logicians at the beginning of the XXth century: Emil Post, Alonzo Church, Alan Turing Developments motivated by the search for the foundations of the notion of proof in mathematics (Hilbert) • After the II World War: Claude Shannon, Stephen Kleene, John von Neumann Development of computers and telecommunications Interest in exploring the functions of the human brain • Late 50s XXth century: Noam Chomsky Formal methods to describe natural languages • Last decades Molecular biology considers the sequences of molecules formed by genomes as sequences of symbols on the alphabet of basic elements Interest in describing properties like repetitions of occurrences or similarity between sequences Chomsky hierarchy of languages • Finite-state or regular • Context-free • Context-sensitive • Recursively enumerable REG ⊂ CF ⊂ CS ⊂ RE Finite automata: origins • Warren McCulloch & Walter Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115-133, 1943 • Stephen C. Kleene. Representation of events in nerve nets and -
Indexed Induction-Recursion
Indexed Induction-Recursion Peter Dybjer a;? aDepartment of Computer Science and Engineering, Chalmers University of Technology, 412 96 G¨oteborg, Sweden Email: [email protected], http://www.cs.chalmers.se/∼peterd/, Tel: +46 31 772 1035, Fax: +46 31 772 3663. Anton Setzer b;1 bDepartment of Computer Science, University of Wales Swansea, Singleton Park, Swansea SA2 8PP, UK, Email: [email protected], http://www.cs.swan.ac.uk/∼csetzer/, Tel: +44 1792 513368, Fax: +44 1792 295651. Abstract An indexed inductive definition (IID) is a simultaneous inductive definition of an indexed family of sets. An inductive-recursive definition (IRD) is a simultaneous inductive definition of a set and a recursive definition of a function from that set into another type. An indexed inductive-recursive definition (IIRD) is a combination of both. We present a closed theory which allows us to introduce all IIRDs in a natural way without much encoding. By specialising it we also get a closed theory of IID. Our theory of IIRDs includes essentially all definitions of sets which occur in Martin-L¨of type theory. We show in particular that Martin-L¨of's computability predicates for dependent types and Palmgren's higher order universes are special kinds of IIRD and thereby clarify why they are constructively acceptable notions. We give two axiomatisations. The first and more restricted one formalises a prin- ciple for introducing meaningful IIRD by using the data-construct in the original version of the proof assistant Agda for Martin-L¨of type theory. The second one admits a more general form of introduction rule, including the introduction rule for the intensional identity relation, which is not covered by the restricted one. -
2020 SIGACT REPORT SIGACT EC – Eric Allender, Shuchi Chawla, Nicole Immorlica, Samir Khuller (Chair), Bobby Kleinberg September 14Th, 2020
2020 SIGACT REPORT SIGACT EC – Eric Allender, Shuchi Chawla, Nicole Immorlica, Samir Khuller (chair), Bobby Kleinberg September 14th, 2020 SIGACT Mission Statement: The primary mission of ACM SIGACT (Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory) is to foster and promote the discovery and dissemination of high quality research in the domain of theoretical computer science. The field of theoretical computer science is the rigorous study of all computational phenomena - natural, artificial or man-made. This includes the diverse areas of algorithms, data structures, complexity theory, distributed computation, parallel computation, VLSI, machine learning, computational biology, computational geometry, information theory, cryptography, quantum computation, computational number theory and algebra, program semantics and verification, automata theory, and the study of randomness. Work in this field is often distinguished by its emphasis on mathematical technique and rigor. 1. Awards ▪ 2020 Gödel Prize: This was awarded to Robin A. Moser and Gábor Tardos for their paper “A constructive proof of the general Lovász Local Lemma”, Journal of the ACM, Vol 57 (2), 2010. The Lovász Local Lemma (LLL) is a fundamental tool of the probabilistic method. It enables one to show the existence of certain objects even though they occur with exponentially small probability. The original proof was not algorithmic, and subsequent algorithmic versions had significant losses in parameters. This paper provides a simple, powerful algorithmic paradigm that converts almost all known applications of the LLL into randomized algorithms matching the bounds of the existence proof. The paper further gives a derandomized algorithm, a parallel algorithm, and an extension to the “lopsided” LLL. -
Formal Language and Automata Theory (CS21004)
Context Free Grammar Normal Forms Derivations and Ambiguities Pumping lemma for CFLs PDA Parsing CFL Properties Formal Language and Automata Theory (CS21004) Soumyajit Dey CSE, IIT Kharagpur Context Free Formal Language and Automata Theory Grammar (CS21004) Normal Forms Derivations and Ambiguities Pumping lemma Soumyajit Dey for CFLs CSE, IIT Kharagpur PDA Parsing CFL Properties DPDA, DCFL Membership CSL Soumyajit Dey CSE, IIT Kharagpur Formal Language and Automata Theory (CS21004) Context Free Grammar Normal Forms Derivations and Ambiguities Pumping lemma for CFLs PDA Parsing CFL Properties Formal Language and Automata Announcements Theory (CS21004) Soumyajit Dey CSE, IIT Kharagpur Context Free Grammar Normal Forms Derivations and The slide is just a short summary Ambiguities Follow the discussion and the boardwork Pumping lemma for CFLs Solve problems (apart from those we dish out in class) PDA Parsing CFL Properties DPDA, DCFL Membership CSL Soumyajit Dey CSE, IIT Kharagpur Formal Language and Automata Theory (CS21004) Context Free Grammar Normal Forms Derivations and Ambiguities Pumping lemma for CFLs PDA Parsing CFL Properties Formal Language and Automata Table of Contents Theory (CS21004) Soumyajit Dey 1 Context Free Grammar CSE, IIT Kharagpur 2 Normal Forms Context Free Grammar 3 Derivations and Ambiguities Normal Forms 4 Derivations and Pumping lemma for CFLs Ambiguities 5 Pumping lemma PDA for CFLs 6 Parsing PDA Parsing 7 CFL Properties CFL Properties DPDA, DCFL 8 DPDA, DCFL Membership 9 Membership CSL 10 CSL Soumyajit Dey CSE, -
Formal Systems: Combinatory Logic and -Calculus
INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS:COMBINATORY LOGIC AND λ-CALCULUS Andrew R. Plummer Department of Linguistics The Ohio State University 30 Sept., 2009 INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION OUTLINE 1 INTRODUCTION 2 APPLICATIVE SYSTEMS 3 USEFUL INFORMATION INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION COMBINATORY LOGIC We present the foundations of Combinatory Logic and the λ-calculus. We mean to precisely demonstrate their similarities and differences. CURRY AND FEYS (KOREAN FACE) The material discussed is drawn from: Combinatory Logic Vol. 1, (1958) Curry and Feys. Lambda-Calculus and Combinators, (2008) Hindley and Seldin. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS We begin with some definitions. FORMAL SYSTEMS A formal system is composed of: A set of terms; A set of statements about terms; A set of statements, which are true, called theorems. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS TERMS We are given a set of atomic terms, which are unanalyzed primitives. We are also given a set of operations, each of which is a mode for combining a finite sequence of terms to form a new term. Finally, we are given a set of term formation rules detailing how to use the operations to form terms. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS STATEMENTS We are given a set of predicates, each of which is a mode for forming a statement from a finite sequence of terms. We are given a set of statement formation rules detailing how to use the predicates to form statements. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS THEOREMS We are given a set of axioms, each of which is a statement that is unconditionally true (and thus a theorem). -
Regular Expressions with a Brief Intro to FSM
Regular Expressions with a brief intro to FSM 15-123 Systems Skills in C and Unix Case for regular expressions • Many web applications require pattern matching – look for <a href> tag for links – Token search • A regular expression – A pattern that defines a class of strings – Special syntax used to represent the class • Eg; *.c - any pattern that ends with .c Formal Languages • Formal language consists of – An alphabet – Formal grammar • Formal grammar defines – Strings that belong to language • Formal languages with formal semantics generates rules for semantic specifications of programming languages Automaton • An automaton ( or automata in plural) is a machine that can recognize valid strings generated by a formal language . • A finite automata is a mathematical model of a finite state machine (FSM), an abstract model under which all modern computers are built. Automaton • A FSM is a machine that consists of a set of finite states and a transition table. • The FSM can be in any one of the states and can transit from one state to another based on a series of rules given by a transition function. Example What does this machine represents? Describe the kind of strings it will accept. Exercise • Draw a FSM that accepts any string with even number of A’s. Assume the alphabet is {A,B} Build a FSM • Stream: “I love cats and more cats and big cats ” • Pattern: “cat” Regular Expressions Regex versus FSM • A regular expressions and FSM’s are equivalent concepts. • Regular expression is a pattern that can be recognized by a FSM. • Regex is an example of how good theory leads to good programs Regular Expression • regex defines a class of patterns – Patterns that ends with a “*” • Regex utilities in unix – grep , awk , sed • Applications – Pattern matching (DNA) – Web searches Regex Engine • A software that can process a string to find regex matches. -
Wittgenstein, Turing and Gödel
Wittgenstein, Turing and Gödel Juliet Floyd Boston University Lichtenberg-Kolleg, Georg August Universität Göttingen Japan Philosophy of Science Association Meeting, Tokyo, Japan 12 June 2010 Wittgenstein on Turing (1946) RPP I 1096. Turing's 'Machines'. These machines are humans who calculate. And one might express what he says also in the form of games. And the interesting games would be such as brought one via certain rules to nonsensical instructions. I am thinking of games like the “racing game”. One has received the order "Go on in the same way" when this makes no sense, say because one has got into a circle. For that order makes sense only in certain positions. (Watson.) Talk Outline: Wittgenstein’s remarks on mathematics and logic Turing and Wittgenstein Gödel on Turing compared Wittgenstein on Mathematics and Logic . The most dismissed part of his writings {although not by Felix Mülhölzer – BGM III} . Accounting for Wittgenstein’s obsession with the intuitive (e.g. pictures, models, aspect perception) . No principled finitism in Wittgenstein . Detail the development of Wittgenstein’s remarks against background of the mathematics of his day Machine metaphors in Wittgenstein Proof in logic is a “mechanical” expedient Logical symbolisms/mathematical theories are “calculi” with “proof machinery” Proofs in mathematics (e.g. by induction) exhibit or show algorithms PR, PG, BB: “Can a machine think?” Language (thought) as a mechanism Pianola Reading Machines, the Machine as Symbolizing its own actions, “Is the human body a thinking machine?” is not an empirical question Turing Machines Turing resolved Hilbert’s Entscheidungsproblem (posed in 1928): Find a definite method by which every statement of mathematics expressed formally in an axiomatic system can be determined to be true or false based on the axioms.