PPT-Chapter 2 FINITE AUTOMATA Learning Objectives At the conclusion of the chapter, the student

Chapter 2 FINITE AUTOMATA Learning Objectives At the conclusion of the chapter the student will be able to Describe the components of a deterministic finite accepter dfa State whether an input string is accepted by a dfa. Great Theoretical Ideas In Computer Science. Anupam. Gupta. Danny Sleator. CS 15-251 . Fall . 2010. Lecture 20. Oct 28, 2010. Carnegie Mellon University. A machine so simple that you can understand it in less than one minute. THEORY. II B. Questions Answers(DFA-NFA) . Dept. of Computer Science & IT, FUUAST Automata Theory . 2. Automata Theory II B. For .  = {a, b} construct DFA that accepts all strings with exactly one ‘a’ .. Introduce finite state automata. Able to capture state, events and dynamic behavior of “man-made systems”. Present logical properties. Textbook :. C. . Cassandras. and S. . Lafortune. , Introduction to Discrete Event Systems, Springer, 2007. THEORY. II. 1. . Defs. .. . a) Finite Automaton:. A Finite Automaton ( FA ) has finite set of ‘states’ ( Q={q. 0,. q. 1,. q. 2,. ….. ) and its ‘control’ moves from state to state in response to external ‘inputs’(. Introduce finite state automata. Able to capture state, events and dynamic behavior of “man-made systems”. Present logical properties. Textbook :. C. . Cassandras. and S. . Lafortune. , Introduction to Discrete Event Systems, Springer, 2007. THEORY. VII. Dept. of Computer Science & IT, FUUAST Automata Theory . 2. Automata Theory VII. Properties of Context-Free Languages. Dept. of Computer Science & IT, FUUAST Automata Theory . Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. THEORY. V. Dept. of Computer Science & IT, FUUAST Automata Theory . 2. Automata Theory V. Context-Free Grammars. and . Languages. A . Pierre Bourhis . CNRS . CRIStAL. , Equipe LINKS. Joint . works. . with. . M.Benedikt. , . M.Krötzsch. , S. Rudolph, P. Senellart, M. Van Den Boom. 1. Querying a database . Database : a labelled graph or hypergraph. Ruby Regular Expressions. Why Learn Regular Expressions?. RegEx. are part of many programmer’s tools. vi, . grep. , PHP, Perl. They provide powerful search (via pattern matching) capabilities. Simple regex are easy, but more advanced patterns can be created as needed (use with care, may not be efficient). Systems. Model Checking Timed Automata . Sayan. . Mitra. Lecture 09. What we have seen so far . A very general modeling framework (Lynch et al.’s Hybrid Automata[1]) . Complex discrete dynamics. Possibly nonlinear continuous dynamics. 2. Finite Automaton (FA). Informally, a state diagram that comprehensively captures all possible states and transitions that a machine can take while responding to a stream or sequence of input symbols. For Inferring Discrete Event Automata. Mohammad Mahdi Karimi. PhD. . Candidate, . ECE. Supervisor: . Dr. Ali . Karimoddini. Summer 2015. 1. Content. Discrete Event Systems. Definitions . Applications. Chapter 2 FINITE AUTOMATA Learning Objectives At the conclusion of the chapter, the student will be able to: Describe the components of a deterministic finite accepter ( dfa ) State whether an input string is accepted by a dfa