PPT-Probabilistic Automaton

Ashish Srivastava Harshil Pathak Introduction to Probabilistic Automaton Deterministic Probabilistic Finite Automata Probabilistic Finite Automaton Probably Approximately Correct PAC learnability. A recursive automaton is like a program which can use recursion calling procedures recursively but again over a 64257nite amount of memory in its variable space Note that the recursion which is typically handled by using a stack gives a limited form A deterministic finite automaton (DFA) is a five-tuple A = (Q, . , , q. 0,. F) where. Q is a finite set of . states.  is a finite set of . input symbols.  is a function : Q   Q called . and. Happy and fruitful New Year. שנה טובה. (Happy New Year). Staff. Instructor:. Amos Israeli room 2-106 .. Office Hours: . Fri 10-12 or by arrangement . Tel#: . 53. 4-886. e-mail:. aisraeli “at” cs.ucsd.edu. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. Executions, Reach set, Invariance. Lecture 03. Sayan. . Mitra. Announcements. Project proposals due in a week. 2 pages with goals, description & milestones. Allerton. . Conference special session on Verification of CPS. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Lexers. Example in . javacc. TOKEN. : {. <IDENTIFIER: <LETTER> (<LETTER> | <DIGIT> | "_")* >. | . <INTLITERAL: . <DIGIT> (<DIGIT>)* . >. | <LETTER: ["a"-"z"] | ["A"-"Z"]>. Automata, Logic and infinite games”, edited by . Gradel. , Thomas and Wilke. Games, logic and Automata Seminar. Assaf. Ben Shimon. ω. -. Automata. Intro. The main topic covered in this chapter is the question how to define acceptance of infinite words by finite automata.. Indranil Gupta. Associate Professor. Dept. of Computer Science, University of Illinois at Urbana-Champaign. Joint work with . Muntasir. . Raihan. . Rahman. , Lewis Tseng, Son Nguyen, . Nitin. . Vaidya. Systems. Timed to Hybrid Automata . Sayan. . Mitra. . (edited by Yu Wang). Lecture 10. Announcements. HW2 released . start soon. Readings. [Henz95]. Thomas . Henzinger. , Peter . Kopke. , . Anuj. . Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Regular Languages Refresher. Roman . Manevich. Ben-Gurion University of the Negev. Regular languages refresher. 2. Regular languages refresher. Formal languages. Alphabet = finite set of letters. Word = sequence of letter. Nathan Clement. Computational Sciences Laboratory. Brigham Young University. Provo, Utah, USA. Next-Generation Sequencing. Problem Statement . Map next-generation sequence reads with variable nucleotide confidence to .