CIT 425- AUTOMATA THEORY, COMPUTABILITY and FORMAL LANGUAGES LECTURE NOTE by DR. OYELAMI M. O. Introduction • This Course Cons
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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. -
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+ -
Regular Languages and Finite Automata for Part IA of the Computer Science Tripos
N Lecture Notes on Regular Languages and Finite Automata for Part IA of the Computer Science Tripos Prof. Andrew M. Pitts Cambridge University Computer Laboratory c 2012 A. M. Pitts Contents Learning Guide ii 1 Regular Expressions 1 1.1 Alphabets,strings,andlanguages . .......... 1 1.2 Patternmatching................................. .... 4 1.3 Somequestionsaboutlanguages . ....... 6 1.4 Exercises....................................... .. 8 2 Finite State Machines 11 2.1 Finiteautomata .................................. ... 11 2.2 Determinism, non-determinism, and ε-transitions. .. .. .. .. .. .. .. .. .. 14 2.3 Asubsetconstruction . .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..... 17 2.4 Summary ........................................ 20 2.5 Exercises....................................... .. 20 3 Regular Languages, I 23 3.1 Finiteautomatafromregularexpressions . ............ 23 3.2 Decidabilityofmatching . ...... 28 3.3 Exercises....................................... .. 30 4 Regular Languages, II 31 4.1 Regularexpressionsfromfiniteautomata . ........... 31 4.2 Anexample ....................................... 32 4.3 Complementand intersectionof regularlanguages . .............. 34 4.4 Exercises....................................... .. 36 5 The Pumping Lemma 39 5.1 ProvingthePumpingLemma . .... 40 5.2 UsingthePumpingLemma . ... 41 5.3 Decidabilityoflanguageequivalence . ........... 44 5.4 Exercises....................................... .. 45 6 Grammars 47 6.1 Context-freegrammars . ..... 47 6.2 Backus-NaurForm ................................ -
Practical Experiments with Regular Approximation of Context-Free Languages
Practical Experiments with Regular Approximation of Context-Free Languages Mark-Jan Nederhof German Research Center for Arti®cial Intelligence Several methods are discussed that construct a ®nite automaton given a context-free grammar, including both methods that lead to subsets and those that lead to supersets of the original context-free language. Some of these methods of regular approximation are new, and some others are presented here in a more re®ned form with respect to existing literature. Practical experiments with the different methods of regular approximation are performed for spoken-language input: hypotheses from a speech recognizer are ®ltered through a ®nite automaton. 1. Introduction Several methods of regular approximation of context-free languages have been pro- posed in the literature. For some, the regular language is a superset of the context-free language, and for others it is a subset. We have implemented a large number of meth- ods, and where necessary, re®ned them with an analysis of the grammar. We also propose a number of new methods. The analysis of the grammar is based on a suf®cient condition for context-free grammars to generate regular languages. For an arbitrary grammar, this analysis iden- ti®es sets of rules that need to be processed in a special way in order to obtain a regular language. The nature of this processing differs for the respective approximation meth- ods. For other parts of the grammar, no special treatment is needed and the grammar rules are translated to the states and transitions of a ®nite automaton without affecting the language. -
Neural Edit Operations for Biological Sequences
Neural Edit Operations for Biological Sequences Satoshi Koide Keisuke Kawano Toyota Central R&D Labs. Toyota Central R&D Labs. [email protected] [email protected] Takuro Kutsuna Toyota Central R&D Labs. [email protected] Abstract The evolution of biological sequences, such as proteins or DNAs, is driven by the three basic edit operations: substitution, insertion, and deletion. Motivated by the recent progress of neural network models for biological tasks, we implement two neural network architectures that can treat such edit operations. The first proposal is the edit invariant neural networks, based on differentiable Needleman-Wunsch algorithms. The second is the use of deep CNNs with concatenations. Our analysis shows that CNNs can recognize regular expressions without Kleene star, and that deeper CNNs can recognize more complex regular expressions including the insertion/deletion of characters. The experimental results for the protein secondary structure prediction task suggest the importance of insertion/deletion. The test accuracy on the widely-used CB513 dataset is 71.5%, which is 1.2-points better than the current best result on non-ensemble models. 1 Introduction Neural networks are now used in many applications, not limited to classical fields such as image processing, speech recognition, and natural language processing. Bioinformatics is becoming an important application field of neural networks. These biological applications are often implemented as a supervised learning model that takes a biological string (such as DNA or protein) as an input, and outputs the corresponding label(s), such as a protein secondary structure [13, 14, 15, 18, 19, 23, 24, 26], protein contact maps [4, 8], and genome accessibility [12]. -
Context-Free Grammars
Chapter 3 Context-Free Grammars By Dr Zalmiyah Zakaria •Context-Free Grammars and Languages •Regular Grammars Formal Definition of Context-Free Grammars (CFG) A CFG can be formally defined by a quadruple of (V, , P, S) where: – V is a finite set of variables (non-terminal) – (the alphabet) is a finite set of terminal symbols , where V = – P is a finite set of rules (production rules) written as: A for A V, (v )*. – S is the start symbol, S V 2 Formal Definition of CFG • We can give a formal description to a particular CFG by specifying each of its four components, for example, G = ({S, A}, {0, 1}, P, S) where P consists of three rules: S → 0S1 S → A A → Sept2011 Theory of Computer Science 3 Context-Free Grammars • Terminal symbols – elements of the alphabet • Variables or non-terminals – additional symbols used in production rules • Variable S (start symbol) initiates the process of generating acceptable strings. 4 Terminal or Variable ? • S → (S) | S + S | S × S | A • A → 1 | 2 | 3 • The terminal symbols are { (, ), +, ×, 1, 2, 3} • The variable symbols are S and A Sept2011 Theory of Computer Science 5 Context-Free Grammars • A rule is an element of the set V (V )*. • An A rule: [A, w] or A w • A null rule or lambda rule: A 6 Context-Free Grammars • Grammars are used to generate strings of a language. • An A rule can be applied to the variable A whenever and wherever it occurs. • No limitation on applicability of a rule – it is context free 8 Context-Free Grammars • CFG have no restrictions on the right-hand side of production rules. -
Theory of Computation
Theory of Computation Alexandre Duret-Lutz [email protected] September 10, 2010 ADL Theory of Computation 1 / 121 References Introduction to the Theory of Computation (Michael Sipser, 2005). Lecture notes from Pierre Wolper's course at http://www.montefiore.ulg.ac.be/~pw/cours/calc.html (The page is in French, but the lecture notes labelled chapitre 1 to chapitre 8 are in English). Elements of Automata Theory (Jacques Sakarovitch, 2009). Compilers: Principles, Techniques, and Tools (A. Aho, R. Sethi, J. Ullman, 2006). ADL Theory of Computation 2 / 121 Introduction What would be your reaction if someone came at you to explain he has invented a perpetual motion machine (i.e. a device that can sustain continuous motion without losing energy or matter)? You would probably laugh. Without looking at the machine, you know outright that such the device cannot sustain perpetual motion. Indeed the laws of thermodynamics demonstrate that perpetual motion devices cannot be created. We know beforehand, from scientic knowledge, that building such a machine is impossible. The ultimate goal of this course is to develop similar knowledge for computer programs. ADL Theory of Computation 3 / 121 Theory of Computation Theory of computation studies whether and how eciently problems can be solved using a program on a model of computation (abstractions of a computer). Computability theory deals with the whether, i.e., is a problem solvable for a given model. For instance a strong result we will learn is that the halting problem is not solvable by a Turing machine. Complexity theory deals with the how eciently. -
A Representation-Based Approach to Connect Regular Grammar and Deep Learning
The Pennsylvania State University The Graduate School A REPRESENTATION-BASED APPROACH TO CONNECT REGULAR GRAMMAR AND DEEP LEARNING A Dissertation in Information Sciences and Technology by Kaixuan Zhang ©2021 Kaixuan Zhang Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2021 The dissertation of Kaixuan Zhang was reviewed and approved by the following: C. Lee Giles Professor of College of Information Sciences and Technology Dissertation Adviser Chair of Committee Kenneth Huang Assistant Professor of College of Information Sciences and Technology Shomir Wilson Assistant Professor of College of Information Sciences and Technology Daniel Kifer Associate Professor of Department of Computer Science and Engineering Mary Beth Rosson Professor of Information Sciences and Technology Director of Graduate Programs, College of Information Sciences and Technology ii Abstract Formal language theory has brought amazing breakthroughs in many traditional areas, in- cluding control systems, compiler design, and model verification, and continues promoting these research directions. As recent years have witnessed that deep learning research brings the long-buried power of neural networks to the surface and has brought amazing break- throughs, it is crucial to revisit formal language theory from a new perspective. Specifically, investigation of the theoretical foundation, rather than a practical application of the con- necting point obviously warrants attention. On the other hand, as the spread of deep neural networks (DNN) continues to reach multifarious branches of research, it has been found that the mystery of these powerful models is equally impressive as their capability in learning tasks. Recent work has demonstrated the vulnerability of DNN classifiers constructed for many different learning tasks, which opens the discussion of adversarial machine learning and explainable artificial intelligence. -
6.035 Lecture 2, Specifying Languages with Regular Expressions and Context-Free Grammars
MIT 6.035 Specifying Languages with Regular Expressions and Context-Free Grammars Martin Rinard Laboratory for Computer Science Massachusetts Institute of Technology Language Definition Problem • How to precisely define language • Layered struc ture of langua ge defifinitiition • Start with a set of letters in language • Lexical structurestructure - identifies “words ” in language (each word is a sequence of letters) • Syntactic sstructuretructure - identifies “sentences ” in language (each sentence is a sequence of words) • Semantics - meaning of program (specifies what result should be for each input) • Today’s topic: lexical and syntactic structures Specifying Formal Languages • Huge Triumph of Computer Science • Beautiful Theoretical Results • Practical Techniques and Applications • Two Dual Notions • Generative approach ((grammar or regular expression) • Recognition approach (automaton) • Lots of theorems about convertingconverting oonene approach automatically to another Specifying Lexical Structure Using Regular ExpressionsExpressions • Have some alphabet ∑ = set of letters • R egular expressions are bu ilt from: • ε -empty string • AAnyn yl lettere tte rf fromr oma alphabetlp ha be t ∑ •r1r2 – regular expression r1 followed by r2 (sequence) •r1| r2 – either regular expression r1 or r2 (choice) • r* - iterated sequence and choice ε | r | rr | … • Parentheses to indicate grouping/precedence Concept of Regular Expression Generating a StringString Rewrite regular expression until have only a sequence of letters (string) left Genera -
Constraints for Membership in Formal Languages Under Systematic Search and Stochastic Local Search
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1027 Constraints for Membership in Formal Languages under Systematic Search and Stochastic Local Search JUN HE ACTA UNIVERSITATIS UPSALIENSIS ISSN 1651-6214 ISBN 978-91-554-8617-4 UPPSALA urn:nbn:se:uu:diva-196347 2013 Dissertation presented at Uppsala University to be publicly examined in Room 2446, Polacksbacken, Lägerhyddsvägen 2D, Uppsala, Friday, April 26, 2013 at 13:00 for the degree of Doctor of Philosophy. The examination will be conducted in English. Abstract He, J. 2013. Constraints for Membership in Formal Languages under Systematic Search and Stochastic Local Search. Acta Universitatis Upsaliensis. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1027. 74 pp. Uppsala. ISBN 978-91-554-8617-4. This thesis focuses on constraints for membership in formal languages under both the systematic search and stochastic local search approaches to constraint programming (CP). Such constraints are very useful in CP for the following three reasons: They provide a powerful tool for user- level extensibility of CP languages. They are very useful for modelling complex work shift regulation constraints, which exist in many shift scheduling problems. In the analysis, testing, and verification of string-manipulating programs, string constraints often arise. We show in this thesis that CP solvers with constraints for membership in formal languages are much more suitable than existing solvers used in tools that have to solve string constraints. In the stochastic local search approach to CP, we make the following two contributions: We introduce a stochastic method of maintaining violations for the regular constraint and extend our method to the automaton constraint with counters. -
Regular Languages and Finite Automata for Part IA of the Computer Science Tripos
N Lecture Notes on Regular Languages and Finite Automata for Part IA of the Computer Science Tripos Prof. Andrew M. Pitts Cambridge University Computer Laboratory c 2010 A. M. Pitts First Edition 1998. Revised 1999, 2000, 2001, 2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010. Contents Learning Guide ii 1 Regular Expressions 1 1.1 Alphabets, strings, and languages . ...... 1 1.2 Patternmatching ................................ 4 1.3 Some questions about languages . ... 6 1.4 Exercises .................................... 8 2 Finite State Machines 11 2.1 Finiteautomata ................................. 11 2.2 Determinism, non-determinism, and ε-transitions . 14 2.3 Asubsetconstruction . .... .... .... ..... .... .... ... 17 2.4 Summary .................................... 20 2.5 Exercises .................................... 20 3 Regular Languages, I 23 3.1 Finite automata from regular expressions . ........ 23 3.2 Decidabilityofmatching . 28 3.3 Exercises .................................... 30 4 Regular Languages, II 31 4.1 Regular expressions from finite automata . ..... 31 4.2 Anexample ................................... 32 4.3 Complement and intersection of regular languages . ......... 34 4.4 Exercises .................................... 36 5 The Pumping Lemma 39 5.1 ProvingthePumpingLemma. 40 5.2 UsingthePumpingLemma.. .... .... ..... .... .... .... 41 5.3 Decidability of language equivalence . ....... 44 5.4 Exercises .................................... 45 6 Grammars 47 6.1 Context-freegrammars . 47 6.2 Backus-NaurForm .............................. -
Theory of Computation
Theory of Computation Todd Gaugler December 14, 2011 2 Contents 1 Mathematical Background 5 1.1 Overview . .5 1.2 Number System . .5 1.3 Functions . .6 1.4 Relations . .6 1.5 Recursive Definitions . .8 1.6 Mathematical Induction . .9 2 Languages and Context-Free Grammars 11 2.1 Languages . 11 2.2 Counting the Rational Numbers . 13 2.3 Grammars . 14 2.4 Regular Grammar . 15 3 Normal Forms and Finite Automata 17 3.1 Review of Grammars . 17 3.2 Normal Forms . 18 3.3 Machines . 20 3.3.1 An NFA λ ..................................... 22 4 Regular Languages 23 4.1 Computation . 24 4.2 The Extended Transition Function . 24 4.3 Algorithms . 26 4.3.1 Removing Non-Determinism . 26 4.3.2 State Minimization . 26 4.3.3 Expression Graph . 26 4.4 The Relationship between a Regular Grammar and the Finite Automaton . 26 4.4.1 Building an NFA corresponding to a Regular Grammar . 27 4.4.2 Closure . 27 4.5 Review for the First Exam . 28 4.6 The Pumping Lemma . 28 5 Pushdown Automata and Context-Free Languages 31 5.1 Pushdown Automata . 31 5.2 Variations on the PDA Theme . 34 5.3 Acceptance of Context-Free Languages . 36 3 CONTENTS CONTENTS 5.4 The Pumping Lemma for Context-Free Languages . 36 5.5 Closure Properties of Context- Free Languages . 37 6 Turing Machines 39 6.1 The Standard Turing Machine . 39 6.2 Turing Machines as Language Acceptors . 40 6.3 Alternative Acceptance Criteria . 41 6.4 Multitrack Machines . 42 6.5 Two-Way Tape Machines .