PPT-Finite Automata

Great Theoretical Ideas In Computer Science Anupam Gupta Danny Sleator CS 15251 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’(. THEORY. I. Introduction. What is Automata. Automaton and types of automata. History, Turing Machine. Why study Automata Theory. e) Applications & Uses. Dept. of Computer Science & IT, FUUAST Automata Theory . 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. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Zhe. Dang. Dmitry . Dementyev. Thomas R. Fischer. William J. Hutton, III. Washington State University – Pullman, WA USA. Overview. Introduction. Mathematical foundation for computer security. The “CIA” triad. Reading: Chapter 2. 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. 2015.03.16. Front End. The purpose of the front end is to deal with the input language. Perform a membership test: code . . source language?. Is the program well-formed (semantically) ?. Build an . . Characterization. of. Context-Free. . Trace. . Languages. Benedek Nagy. Friedrich Otto. Department. of Computer Science . Fachbereich. . Elektrotechnik. /. Informatik. Faculty. of . 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. 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.