Theory of Languages and Automata

Size: px
Start display at page:

Download "Theory of Languages and Automata"

Transcription

1 Theory of Languages and Automata Chapter 1- Regular Languages & Finite State Automaton Sharif University of Technology

2 Finite State Automaton O We begin with the simplest model of Computation, called finite state machine or finite automaton. O are good models for computers with an extremely limited amount of memory. Embedded Systems O Markov Chains are the probabilistic counterpart of Finite Automata Theory of Languages and Automata Prof. Movaghar 2

3 Simple Example O Automatic door Door Theory of Languages and Automata Prof. Movaghar 3

4 Simple Example (cont.) O State Diagram O State Transition Table Neither Front Rear Both Closed Closed Open Closed Closed Open Closed Open Open Open Theory of Languages and Automata Prof. Movaghar 4

5 Formal Definition O A finite automaton is a 5-tuple (Q,Σ,δ,q 0, F), where 1. Q is a finite set called states, 2. Σ is a finite set called the alphabet, 3. δ : Q Σ Q is the transition function, 4. q 0 Q is the start state, and 5. F Q is the set of accept states. Theory of Languages and Automata Prof. Movaghar 5

6 Example O M 1 = (Q, Σ, δ, q 0, F), where 1. Q = {q 1, q 2, q 3 }, 2. Σ = {0,1}, 3. δ is described as q 1 q 1 q 2 q 2 q 3 q 2 q 3 q 2 q 2 1. q 1 is the start state, and 2. F = {q 2 }. 0 1 Theory of Languages and Automata Prof. Movaghar 6

7 Language of a Finite machine O If A is the set of all strings that machine M accepts, we say that A is the language of machine M and write: L(M) = A. We say that M recognizes A or that M accepts A. Theory of Languages and Automata Prof. Movaghar 7

8 Example O L(M 1 ) = {w w contains at least one 1 and even number of 0s follow the last 1}. Theory of Languages and Automata Prof. Movaghar 8

9 Example O M 4 accepts all strings that start and end with a or with b. Theory of Languages and Automata Prof. Movaghar 9

10 Formal Definition O M = (Q, Σ, δ, q 0, F) O w = w 1 w 2 w n i, w i Σ O M accepts w r 0, 1 r,, n r 1. r 0 = q 0, i, r i Q 2. δ(r i, w i+1 ) = i+1 r, for i = 0,, n-1, 3. r n F. Theory of Languages and Automata Prof. Movaghar 10

11 Regular Language O A language is called a regular language if some finite automaton recognizes it. Theory of Languages and Automata Prof. Movaghar 11

12 Example O L (M 5 ) = {w the sum of the symbols in w is 0 modulo 3, except that <RESET> resets the count to 0}. As M 5 recognizes this language, it is a regular language. Theory of Languages and Automata Prof. Movaghar 12

13 Designing Finite Automata O Put yourself in the place of the machine and then see how you would go about performing the machine s task. O Design a finite automaton to recognize the regular language of all strings that contain the string 001 as a substring. Theory of Languages and Automata Prof. Movaghar 13

14 Designing Finite Automata (cont.) O There are four possibilities: You 1. haven t just seen any symbols of the pattern, 2. have just seen a 0, 3. have just seen 00, or 4. have seen the entire pattern 001. Theory of Languages and Automata Prof. Movaghar 14

15 The Regular Operations O Let A and B be languages. We define the regular operations union, concatenation, and star as follows. O Union: A B = {x x A or x B}. O Concatenation: A B = {xy x A and y B }. O Star: A * = {x 1 x 2 x k k 0 and each x i A }. Theory of Languages and Automata Prof. Movaghar 15

16 Closure Under Union O THEOREM The class of regular languages is closed under the union operation. Theory of Languages and Automata Prof. Movaghar 16

17 Proof O Let M 1 = (Q 1, Σ 1, δ 1, q 1, F 1 ) recognize A 1, and M 2 = (Q 2, Σ 2, δ 2, q 2, F 2 ) recognize A 2. O Construct M = (Q, Σ, δ, q 0, F) to recognize A 1 A Q = Q 1 Q 2 2. Σ = Σ 1 Σ 2 3. δ((r 1,r 2 ),a) = (δ 1 (r 1,a), δ 2 (r 2,a)). 4. q 0 is the pair (q 1, q 2 ). 5. F is the set of pair in which either members in an accept state of M 1 or M 2. F = (F 1 Q 2 ) (Q 1 F 2 ) F F 1 F 2 Theory of Languages and Automata Prof. Movaghar 17

18 Closure under Concatenation O THEOREM The class of regular languages is closed under the concatenation operation. O To prove this theorem we introduce a new technique called nondeterminism. Theory of Languages and Automata Prof. Movaghar 18

19 Nondeterminism O In a nondeterministic machine, several choices may exit for the next state at any point. O Nondeterminism is a generalization of determinism, so every deterministic finite automaton is automatically a nondeterministic finite automaton. Theory of Languages and Automata Prof. Movaghar 19

20 Differences between DFA & NFA O First, very state of a DFA always has exactly one exiting transition arrow for each symbol in the alphabet. In an NFA a state may have zero, one, or more exiting arrows for each alphabet symbol. O Second, in a DFA, labels on the transition arrows are symbols from the alphabet. An NFA may have arrows labeled with members of the alphabet or ε. Zero, one, or many arrows may exit from each state with the label ε. Theory of Languages and Automata Prof. Movaghar 20

21 Deterministic vs. Nondeterministic Theory of Languages and Automata Prof. Movaghar 21

22 Example O Consider the computation of N 1 on input Theory of Languages and Automata Prof. Movaghar 22

23 Example (cont.) Theory of Languages and Automata Prof. Movaghar 23

24 Formal Definition O A nondeterministic finite automaton is a 5-tuple (Q,Σ,δ,q 0, F), where 1. Q is a finite set of states, 2. Σ is a finite alphabet, 3. δ : Q Σ ε P(Q) is the transition function, 4. q 0 Q is the start state, and 5. F Q is the set of accept states. Theory of Languages and Automata Prof. Movaghar 24

25 Example O N 1 = (Q, Σ, δ, q 0, F), where 1. Q = {q 1, q 2, q 3, q 4 }, 2. Σ = {0,1}, 3. δ is given as 0 1 ε q 1 {q 1 } {q 1,q 2 } q 2 {q 3 } {q 4 } q 3 {q 4 } q 4 {q 4 } {q 4 } 1. q 1 is the start state, and 2. F = {q 4 }. Theory of Languages and Automata Prof. Movaghar 25

26 Equivalence of NFAs & DFAs O THEOREM Every nondeterministic finite automaton has an equivalent deterministic finite automaton. O PROOF IDEA convert the NFA into an equivalent DFA that simulates the NFA. If k is the number of states of the NFA, so the DFA simulating the NFA will have 2 k states. Theory of Languages and Automata Prof. Movaghar 26

27 Proof O Let N = (Q,Σ,δ,q 0, F) be the NFA recognizing A. We construct a DFA M =(Q',Σ',δ',q 0 ', F ) recognizing A. O let's first consider the easier case wherein N has no ε arrows. 1. Q' = P(Q) q 0 = q F' = {R Q R contains an accept state of N}. Theory of Languages and Automata Prof. Movaghar 27

28 Proof (cont.) O Now we need to consider the ε arrows. O for R Q let O E(R) = {q q can be reached from R by traveling along 0 or more ε arrows}. 1. Q' = P(Q). 2. δ' (R,a) ={q Q q E(δ(r,a)) for some r R}. 3. q 0 = E({q 0 }). 4. F' = {R Q R contains an accept state of N}. Theory of Languages and Automata Prof. Movaghar 28

29 Corollary O A language is regular if and only if some nondeterministic finite automaton recognizes it. Theory of Languages and Automata Prof. Movaghar 29

30 Example O D s state set is {,{1},{2},{3},{1,2},{1,3},{2,3},{1,2,3}}. O The start state is E({1}) = {1,3}. O The accept states are {{1},{1,2},{1,3},{1,2,3}}. Theory of Languages and Automata Prof. Movaghar 30

31 Example (cont.) After removing unnecessary states Theory of Languages and Automata Prof. Movaghar 31

32 CLOSURE UNDER THE REGULAR OPERATIONS [Using NFA] Theory of Languages and Automata Prof. Movaghar 32

33 Closure Under Union O The class of regular languages is closed under the Union operation. Let NFA1 recognize A1 and NFA2 recognize A2. Construct NFA3 to recognize A1 U A2. Theory of Languages and Automata Prof. Movaghar 33

34 Proof (cont.) Theory of Languages and Automata Prof. Movaghar 34

35 Closure Under Concatenation Operation O The class of regular languages is closed under the concatenation operation. Theory of Languages and Automata Prof. Movaghar 35

36 Proof (cont.) Theory of Languages and Automata Prof. Movaghar 36

37 Closure Under Star operation O The class of regular languages is closed under the star operation. O We represent another NFA to recognize A*. Theory of Languages and Automata Prof. Movaghar 37

38 O Proof (cont.) Theory of Languages and Automata Prof. Movaghar 38

39 Regular Expression O Circular Definition? Theory of Languages and Automata Prof. Movaghar 39

40 Regular Expression Language O Theory of Languages and Automata Prof. Movaghar 40

41 Examples(cont.) O Theory of Languages and Automata Prof. Movaghar 41

42 Equivalence of DFA and Regular Expression O A language is regular if and only if some regular expression describes it. Lemma: O If a language is described by a regular expression, then it is regular. O If a language is regular, then it is described by a regular expression. Theory of Languages and Automata Prof. Movaghar 42

43 Building an NFA from the Regular Expression O We consider the six cases in the formal definition of regular expressions Theory of Languages and Automata Prof. Movaghar 43

44 Examples Theory of Languages and Automata Prof. Movaghar 44

45 Other direction of the proof O We need to show that, if a language A is regular, a regular expression describes it! O First we show how to convert DFAs into GNFAs, and then GNFAs into regular expressions. O We can easily convert a DFA into a GNFA in the special form. Theory of Languages and Automata Prof. Movaghar 45

46 Formal Definition O Theory of Languages and Automata Prof. Movaghar 46

47 Assumptions For convenience we require that GNFAs always have a special form that meets the following conditions: 1. The start state has transition arrows going to every other state but no arrows coming in from any other state. 2. There is only a single accept state, and it has arrows coming in from every other state but no arrows going to any other state. Furthermore, the accept state is not the same as the start state. 3. Except for the start and accept states, one arrow goes from every state to every other state and also from each state to itself. Theory of Languages and Automata Prof. Movaghar 47

48 Acceptance of Languages for GNFA O A GNFA accepts a string w in Σ* if w = w 1 w 2 w k, where each w i is in Σ* is in Σ* and a sequence of q 0, q 1,, q k exists such that 1. q 0 = q start is the start state, 2. q k = q accept is the accept state, and 3. For each i, we have w i L(R i ) where R i = δ(q i-1, q i ); in other words R i is the expression on the arrow from q i-1 to q i.

49 How to Eliminate a State? Theory of Languages and Automata Prof. Movaghar 49

50 Example Theory of Languages and Automata Prof. Movaghar 50

51 Example Theory of Languages and Automata Prof. Movaghar 51

52 Grammar O A grammar G is a 4-tuple G = (V, Σ, R, S) where: 1. V is a finite set of variables, 2. Σ is a finite, disjoint from V, of terminals, 3. R is a finite set of rules, 4. S is the start variable. Theory of Languages and Automata Prof. Movaghar 52

53 O Rule Theory of Languages and Automata Prof. Movaghar 53

54 Derivation O Theory of Languages and Automata Prof. Movaghar 54

55 Language of a Grammar O Theory of Languages and Automata Prof. Movaghar 55

56 OExample Theory of Languages and Automata Prof. Movaghar 56

57 A Notation for Grammars Consider the grammar G = ({S}, {a,b}, P, S} with P given by S asb S ε The above grammar is usually written as: G: S asb ε Theory of Languages and Automata Prof. Movaghar 57

58 Regular Grammar A grammar G = (V, Σ, R, S) is said to be right-linear if all rules are of the form A xb A x Where A, B V, and X Σ*. A grammar is said to be leftlinear if all rules are of the form A Bx A x A regular grammar is one that is either right-linear or left-linear. Theory of Languages and Automata Prof. Movaghar 58

59 Theorem Let G = (V, Σ, R, S) be a right-linear grammar. Then: L(G) is a regular language. Theory of Languages and Automata Prof. Movaghar 59

60 Example Construct a NFA that accepts the language generated by the grammar V 0 av 1 V 1 abv 0 b V 0 a V 1 b V f b a V 2 Theory of Languages and Automata Prof. Movaghar 60

61 Theorem Let L be a regular language on the alphabet Σ. Then: There exists a right-linear grammar G = (V, Σ, R, S) Such that L = L(G). Theory of Languages and Automata Prof. Movaghar 61

62 Theorem Theorem A language is regular if and only if there exists a left-linear grammar G such that L = L(G). Outline of the proof: Given any left-linear grammar with rules of the form A Bx A x We can construct a right-linear Ĝ by replacing every such rule of G with A x R B A x R We have L(G) = L(Ĝ) R. Theory of Languages and Automata Prof. Movaghar 62

63 Theorem O Theory of Languages and Automata Prof. Movaghar 63

Language properties and Grammar of Parallel and Series Parallel Languages

Language properties and Grammar of Parallel and Series Parallel Languages arxiv:1711.01799v1 [cs.fl] 6 Nov 2017 Language properties and Grammar of Parallel and Series Parallel Languages Mohana.N 1, Kalyani Desikan 2 and V.Rajkumar Dare 3 1 Division of Mathematics, School of

More information

A General Class of Noncontext Free Grammars Generating Context Free Languages

A General Class of Noncontext Free Grammars Generating Context Free Languages INFORMATION AND CONTROL 43, 187-194 (1979) A General Class of Noncontext Free Grammars Generating Context Free Languages SARWAN K. AGGARWAL Boeing Wichita Company, Wichita, Kansas 67210 AND JAMES A. HEINEN

More information

Informatics 2A: Language Complexity and the. Inf2A: Chomsky Hierarchy

Informatics 2A: Language Complexity and the. Inf2A: Chomsky Hierarchy Informatics 2A: Language Complexity and the Chomsky Hierarchy September 28, 2010 Starter 1 Is there a finite state machine that recognises all those strings s from the alphabet {a, b} where the difference

More information

Lecture 10: Reinforcement Learning

Lecture 10: Reinforcement Learning Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation

More information

COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR

COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR ROLAND HAUSSER Institut für Deutsche Philologie Ludwig-Maximilians Universität München München, West Germany 1. CHOICE OF A PRIMITIVE OPERATION The

More information

Erkki Mäkinen State change languages as homomorphic images of Szilard languages

Erkki Mäkinen State change languages as homomorphic images of Szilard languages Erkki Mäkinen State change languages as homomorphic images of Szilard languages UNIVERSITY OF TAMPERE SCHOOL OF INFORMATION SCIENCES REPORTS IN INFORMATION SCIENCES 48 TAMPERE 2016 UNIVERSITY OF TAMPERE

More information

A R "! I,,, !~ii ii! A ow ' r.-ii ' i ' JA' V5, 9. MiN, ;

A R ! I,,, !~ii ii! A ow ' r.-ii ' i ' JA' V5, 9. MiN, ; A R "! I,,, r.-ii ' i '!~ii ii! A ow ' I % i o,... V. 4..... JA' i,.. Al V5, 9 MiN, ; Logic and Language Models for Computer Science Logic and Language Models for Computer Science HENRY HAMBURGER George

More information

On the Polynomial Degree of Minterm-Cyclic Functions

On the Polynomial Degree of Minterm-Cyclic Functions On the Polynomial Degree of Minterm-Cyclic Functions Edward L. Talmage Advisor: Amit Chakrabarti May 31, 2012 ABSTRACT When evaluating Boolean functions, each bit of input that must be checked is costly,

More information

Proof Theory for Syntacticians

Proof Theory for Syntacticians Department of Linguistics Ohio State University Syntax 2 (Linguistics 602.02) January 5, 2012 Logics for Linguistics Many different kinds of logic are directly applicable to formalizing theories in syntax

More information

Grammars & Parsing, Part 1:

Grammars & Parsing, Part 1: Grammars & Parsing, Part 1: Rules, representations, and transformations- oh my! Sentence VP The teacher Verb gave the lecture 2015-02-12 CS 562/662: Natural Language Processing Game plan for today: Review

More information

Are You Ready? Simplify Fractions

Are You Ready? Simplify Fractions SKILL 10 Simplify Fractions Teaching Skill 10 Objective Write a fraction in simplest form. Review the definition of simplest form with students. Ask: Is 3 written in simplest form? Why 7 or why not? (Yes,

More information

CS 598 Natural Language Processing

CS 598 Natural Language Processing CS 598 Natural Language Processing Natural language is everywhere Natural language is everywhere Natural language is everywhere Natural language is everywhere!"#$%&'&()*+,-./012 34*5665756638/9:;< =>?@ABCDEFGHIJ5KL@

More information

Classroom Connections Examining the Intersection of the Standards for Mathematical Content and the Standards for Mathematical Practice

Classroom Connections Examining the Intersection of the Standards for Mathematical Content and the Standards for Mathematical Practice Classroom Connections Examining the Intersection of the Standards for Mathematical Content and the Standards for Mathematical Practice Title: Considering Coordinate Geometry Common Core State Standards

More information

Language Evolution, Metasyntactically. First International Workshop on Bidirectional Transformations (BX 2012)

Language Evolution, Metasyntactically. First International Workshop on Bidirectional Transformations (BX 2012) Language Evolution, Metasyntactically First International Workshop on Bidirectional Transformations (BX 2012) Vadim Zaytsev, SWAT, CWI 2012 Introduction Every language document employs its own We focus

More information

Enumeration of Context-Free Languages and Related Structures

Enumeration of Context-Free Languages and Related Structures Enumeration of Context-Free Languages and Related Structures Michael Domaratzki Jodrey School of Computer Science, Acadia University Wolfville, NS B4P 2R6 Canada Alexander Okhotin Department of Mathematics,

More information

A Version Space Approach to Learning Context-free Grammars

A Version Space Approach to Learning Context-free Grammars Machine Learning 2: 39~74, 1987 1987 Kluwer Academic Publishers, Boston - Manufactured in The Netherlands A Version Space Approach to Learning Context-free Grammars KURT VANLEHN (VANLEHN@A.PSY.CMU.EDU)

More information

AP Calculus AB. Nevada Academic Standards that are assessable at the local level only.

AP Calculus AB. Nevada Academic Standards that are assessable at the local level only. Calculus AB Priority Keys Aligned with Nevada Standards MA I MI L S MA represents a Major content area. Any concept labeled MA is something of central importance to the entire class/curriculum; it is a

More information

Discriminative Learning of Beam-Search Heuristics for Planning

Discriminative Learning of Beam-Search Heuristics for Planning Discriminative Learning of Beam-Search Heuristics for Planning Yuehua Xu School of EECS Oregon State University Corvallis,OR 97331 xuyu@eecs.oregonstate.edu Alan Fern School of EECS Oregon State University

More information

AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS

AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS 1 CALIFORNIA CONTENT STANDARDS: Chapter 1 ALGEBRA AND WHOLE NUMBERS Algebra and Functions 1.4 Students use algebraic

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Grade 5 + DIGITAL. EL Strategies. DOK 1-4 RTI Tiers 1-3. Flexible Supplemental K-8 ELA & Math Online & Print

Grade 5 + DIGITAL. EL Strategies. DOK 1-4 RTI Tiers 1-3. Flexible Supplemental K-8 ELA & Math Online & Print Standards PLUS Flexible Supplemental K-8 ELA & Math Online & Print Grade 5 SAMPLER Mathematics EL Strategies DOK 1-4 RTI Tiers 1-3 15-20 Minute Lessons Assessments Consistent with CA Testing Technology

More information

Morphotactics as Tier-Based Strictly Local Dependencies

Morphotactics as Tier-Based Strictly Local Dependencies Morphotactics as Tier-Based Strictly Local Dependencies Alëna Aksënova, Thomas Graf, and Sedigheh Moradi Stony Brook University SIGMORPHON 14 Berlin, Germany 11. August 2016 Our goal Received view Recent

More information

Probability and Game Theory Course Syllabus

Probability and Game Theory Course Syllabus Probability and Game Theory Course Syllabus DATE ACTIVITY CONCEPT Sunday Learn names; introduction to course, introduce the Battle of the Bismarck Sea as a 2-person zero-sum game. Monday Day 1 Pre-test

More information

RANKING AND UNRANKING LEFT SZILARD LANGUAGES. Erkki Mäkinen DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF TAMPERE REPORT A ER E P S I M S

RANKING AND UNRANKING LEFT SZILARD LANGUAGES. Erkki Mäkinen DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF TAMPERE REPORT A ER E P S I M S N S ER E P S I M TA S UN A I S I T VER RANKING AND UNRANKING LEFT SZILARD LANGUAGES Erkki Mäkinen DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF TAMPERE REPORT A-1997-2 UNIVERSITY OF TAMPERE DEPARTMENT OF

More information

Pre-Algebra A. Syllabus. Course Overview. Course Goals. General Skills. Credit Value

Pre-Algebra A. Syllabus. Course Overview. Course Goals. General Skills. Credit Value Syllabus Pre-Algebra A Course Overview Pre-Algebra is a course designed to prepare you for future work in algebra. In Pre-Algebra, you will strengthen your knowledge of numbers as you look to transition

More information

Mathematics Success Level E

Mathematics Success Level E T403 [OBJECTIVE] The student will generate two patterns given two rules and identify the relationship between corresponding terms, generate ordered pairs, and graph the ordered pairs on a coordinate plane.

More information

arxiv: v1 [math.at] 10 Jan 2016

arxiv: v1 [math.at] 10 Jan 2016 THE ALGEBRAIC ATIYAH-HIRZEBRUCH SPECTRAL SEQUENCE OF REAL PROJECTIVE SPECTRA arxiv:1601.02185v1 [math.at] 10 Jan 2016 GUOZHEN WANG AND ZHOULI XU Abstract. In this note, we use Curtis s algorithm and the

More information

have to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,

have to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words, A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994

More information

IT Students Workshop within Strategic Partnership of Leibniz University and Peter the Great St. Petersburg Polytechnic University

IT Students Workshop within Strategic Partnership of Leibniz University and Peter the Great St. Petersburg Polytechnic University IT Students Workshop within Strategic Partnership of Leibniz University and Peter the Great St. Petersburg Polytechnic University 06.11.16 13.11.16 Hannover Our group from Peter the Great St. Petersburg

More information

Cal s Dinner Card Deals

Cal s Dinner Card Deals Cal s Dinner Card Deals Overview: In this lesson students compare three linear functions in the context of Dinner Card Deals. Students are required to interpret a graph for each Dinner Card Deal to help

More information

Grade 6: Correlated to AGS Basic Math Skills

Grade 6: Correlated to AGS Basic Math Skills Grade 6: Correlated to AGS Basic Math Skills Grade 6: Standard 1 Number Sense Students compare and order positive and negative integers, decimals, fractions, and mixed numbers. They find multiples and

More information

Backwards Numbers: A Study of Place Value. Catherine Perez

Backwards Numbers: A Study of Place Value. Catherine Perez Backwards Numbers: A Study of Place Value Catherine Perez Introduction I was reaching for my daily math sheet that my school has elected to use and in big bold letters in a box it said: TO ADD NUMBERS

More information

Evolution of Collective Commitment during Teamwork

Evolution of Collective Commitment during Teamwork Fundamenta Informaticae 56 (2003) 329 371 329 IOS Press Evolution of Collective Commitment during Teamwork Barbara Dunin-Kȩplicz Institute of Informatics, Warsaw University Banacha 2, 02-097 Warsaw, Poland

More information

Liquid Narrative Group Technical Report Number

Liquid Narrative Group Technical Report Number http://liquidnarrative.csc.ncsu.edu/pubs/tr04-004.pdf NC STATE UNIVERSITY_ Liquid Narrative Group Technical Report Number 04-004 Equivalence between Narrative Mediation and Branching Story Graphs Mark

More information

Natural Language Processing. George Konidaris

Natural Language Processing. George Konidaris Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans

More information

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus CS 1103 Computer Science I Honors Fall 2016 Instructor Muller Syllabus Welcome to CS1103. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts

More information

Shockwheat. Statistics 1, Activity 1

Shockwheat. Statistics 1, Activity 1 Statistics 1, Activity 1 Shockwheat Students require real experiences with situations involving data and with situations involving chance. They will best learn about these concepts on an intuitive or informal

More information

Introduction to Causal Inference. Problem Set 1. Required Problems

Introduction to Causal Inference. Problem Set 1. Required Problems Introduction to Causal Inference Problem Set 1 Professor: Teppei Yamamoto Due Friday, July 15 (at beginning of class) Only the required problems are due on the above date. The optional problems will not

More information

The New York City Department of Education. Grade 5 Mathematics Benchmark Assessment. Teacher Guide Spring 2013

The New York City Department of Education. Grade 5 Mathematics Benchmark Assessment. Teacher Guide Spring 2013 The New York City Department of Education Grade 5 Mathematics Benchmark Assessment Teacher Guide Spring 2013 February 11 March 19, 2013 2704324 Table of Contents Test Design and Instructional Purpose...

More information

systems have been developed that are well-suited to phenomena in but is properly contained in the indexed languages. We give a

systems have been developed that are well-suited to phenomena in but is properly contained in the indexed languages. We give a J. LOGIC PROGRAMMING 1993:12:1{199 1 STRING VARIABLE GRAMMAR: A LOGIC GRAMMAR FORMALISM FOR THE BIOLOGICAL LANGUAGE OF DNA DAVID B. SEARLS > Building upon Denite Clause Grammar (DCG), a number of logic

More information

Statewide Framework Document for:

Statewide Framework Document for: Statewide Framework Document for: 270301 Standards may be added to this document prior to submission, but may not be removed from the framework to meet state credit equivalency requirements. Performance

More information

Outreach Connect User Manual

Outreach Connect User Manual Outreach Connect A Product of CAA Software, Inc. Outreach Connect User Manual Church Growth Strategies Through Sunday School, Care Groups, & Outreach Involving Members, Guests, & Prospects PREPARED FOR:

More information

NCU IISR English-Korean and English-Chinese Named Entity Transliteration Using Different Grapheme Segmentation Approaches

NCU IISR English-Korean and English-Chinese Named Entity Transliteration Using Different Grapheme Segmentation Approaches NCU IISR English-Korean and English-Chinese Named Entity Transliteration Using Different Grapheme Segmentation Approaches Yu-Chun Wang Chun-Kai Wu Richard Tzong-Han Tsai Department of Computer Science

More information

The Evolution of Random Phenomena

The Evolution of Random Phenomena The Evolution of Random Phenomena A Look at Markov Chains Glen Wang glenw@uchicago.edu Splash! Chicago: Winter Cascade 2012 Lecture 1: What is Randomness? What is randomness? Can you think of some examples

More information

TOPICS LEARNING OUTCOMES ACTIVITES ASSESSMENT Numbers and the number system

TOPICS LEARNING OUTCOMES ACTIVITES ASSESSMENT Numbers and the number system Curriculum Overview Mathematics 1 st term 5º grade - 2010 TOPICS LEARNING OUTCOMES ACTIVITES ASSESSMENT Numbers and the number system Multiplies and divides decimals by 10 or 100. Multiplies and divide

More information

Refining the Design of a Contracting Finite-State Dependency Parser

Refining the Design of a Contracting Finite-State Dependency Parser Refining the Design of a Contracting Finite-State Dependency Parser Anssi Yli-Jyrä and Jussi Piitulainen and Atro Voutilainen The Department of Modern Languages PO Box 3 00014 University of Helsinki {anssi.yli-jyra,jussi.piitulainen,atro.voutilainen}@helsinki.fi

More information

The Indices Investigations Teacher s Notes

The Indices Investigations Teacher s Notes The Indices Investigations Teacher s Notes These activities are for students to use independently of the teacher to practise and develop number and algebra properties.. Number Framework domain and stage:

More information

Objectives. Chapter 2: The Representation of Knowledge. Expert Systems: Principles and Programming, Fourth Edition

Objectives. Chapter 2: The Representation of Knowledge. Expert Systems: Principles and Programming, Fourth Edition Chapter 2: The Representation of Knowledge Expert Systems: Principles and Programming, Fourth Edition Objectives Introduce the study of logic Learn the difference between formal logic and informal logic

More information

ARNE - A tool for Namend Entity Recognition from Arabic Text

ARNE - A tool for Namend Entity Recognition from Arabic Text 24 ARNE - A tool for Namend Entity Recognition from Arabic Text Carolin Shihadeh DFKI Stuhlsatzenhausweg 3 66123 Saarbrücken, Germany carolin.shihadeh@dfki.de Günter Neumann DFKI Stuhlsatzenhausweg 3 66123

More information

College Pricing and Income Inequality

College Pricing and Income Inequality College Pricing and Income Inequality Zhifeng Cai U of Minnesota and FRB Minneapolis Jonathan Heathcote FRB Minneapolis OSU, November 15 2016 The views expressed herein are those of the authors and not

More information

Teaching a Laboratory Section

Teaching a Laboratory Section Chapter 3 Teaching a Laboratory Section Page I. Cooperative Problem Solving Labs in Operation 57 II. Grading the Labs 75 III. Overview of Teaching a Lab Session 79 IV. Outline for Teaching a Lab Session

More information

Presentation skills. Bojan Jovanoski, project assistant. University Skopje Business Start-up Centre

Presentation skills. Bojan Jovanoski, project assistant. University Skopje Business Start-up Centre Presentation skills Bojan Jovanoski, project assistant University Skopje Business Start-up Centre Let me present myself Bojan Jovanoski Project assistant / Demonstrator Working in the Business Start-up

More information

Corrective Feedback and Persistent Learning for Information Extraction

Corrective Feedback and Persistent Learning for Information Extraction Corrective Feedback and Persistent Learning for Information Extraction Aron Culotta a, Trausti Kristjansson b, Andrew McCallum a, Paul Viola c a Dept. of Computer Science, University of Massachusetts,

More information

Syntax Parsing 1. Grammars and parsing 2. Top-down and bottom-up parsing 3. Chart parsers 4. Bottom-up chart parsing 5. The Earley Algorithm

Syntax Parsing 1. Grammars and parsing 2. Top-down and bottom-up parsing 3. Chart parsers 4. Bottom-up chart parsing 5. The Earley Algorithm Syntax Parsing 1. Grammars and parsing 2. Top-down and bottom-up parsing 3. Chart parsers 4. Bottom-up chart parsing 5. The Earley Algorithm syntax: from the Greek syntaxis, meaning setting out together

More information

Page 1 of 11. Curriculum Map: Grade 4 Math Course: Math 4 Sub-topic: General. Grade(s): None specified

Page 1 of 11. Curriculum Map: Grade 4 Math Course: Math 4 Sub-topic: General. Grade(s): None specified Curriculum Map: Grade 4 Math Course: Math 4 Sub-topic: General Grade(s): None specified Unit: Creating a Community of Mathematical Thinkers Timeline: Week 1 The purpose of the Establishing a Community

More information

Edexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE

Edexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE Edexcel GCSE Statistics 1389 Paper 1H June 2007 Mark Scheme Edexcel GCSE Statistics 1389 NOTES ON MARKING PRINCIPLES 1 Types of mark M marks: method marks A marks: accuracy marks B marks: unconditional

More information

Math 96: Intermediate Algebra in Context

Math 96: Intermediate Algebra in Context : Intermediate Algebra in Context Syllabus Spring Quarter 2016 Daily, 9:20 10:30am Instructor: Lauri Lindberg Office Hours@ tutoring: Tutoring Center (CAS-504) 8 9am & 1 2pm daily STEM (Math) Center (RAI-338)

More information

Mathematics subject curriculum

Mathematics subject curriculum Mathematics subject curriculum Dette er ei omsetjing av den fastsette læreplanteksten. Læreplanen er fastsett på Nynorsk Established as a Regulation by the Ministry of Education and Research on 24 June

More information

Math 098 Intermediate Algebra Spring 2018

Math 098 Intermediate Algebra Spring 2018 Math 098 Intermediate Algebra Spring 2018 Dept. of Mathematics Instructor's Name: Office Location: Office Hours: Office Phone: E-mail: MyMathLab Course ID: Course Description This course expands on the

More information

"f TOPIC =T COMP COMP... OBJ

f TOPIC =T COMP COMP... OBJ TREATMENT OF LONG DISTANCE DEPENDENCIES IN LFG AND TAG: FUNCTIONAL UNCERTAINTY IN LFG IS A COROLLARY IN TAG" Aravind K. Joshi Dept. of Computer & Information Science University of Pennsylvania Philadelphia,

More information

School Competition and Efficiency with Publicly Funded Catholic Schools David Card, Martin D. Dooley, and A. Abigail Payne

School Competition and Efficiency with Publicly Funded Catholic Schools David Card, Martin D. Dooley, and A. Abigail Payne School Competition and Efficiency with Publicly Funded Catholic Schools David Card, Martin D. Dooley, and A. Abigail Payne Web Appendix See paper for references to Appendix Appendix 1: Multiple Schools

More information

TabletClass Math Geometry Course Guidebook

TabletClass Math Geometry Course Guidebook TabletClass Math Geometry Course Guidebook Includes Final Exam/Key, Course Grade Calculation Worksheet and Course Certificate Student Name Parent Name School Name Date Started Course Date Completed Course

More information

Visual CP Representation of Knowledge

Visual CP Representation of Knowledge Visual CP Representation of Knowledge Heather D. Pfeiffer and Roger T. Hartley Department of Computer Science New Mexico State University Las Cruces, NM 88003-8001, USA email: hdp@cs.nmsu.edu and rth@cs.nmsu.edu

More information

Improving Fairness in Memory Scheduling

Improving Fairness in Memory Scheduling Improving Fairness in Memory Scheduling Using a Team of Learning Automata Aditya Kajwe and Madhu Mutyam Department of Computer Science & Engineering, Indian Institute of Tehcnology - Madras June 14, 2014

More information

CS 101 Computer Science I Fall Instructor Muller. Syllabus

CS 101 Computer Science I Fall Instructor Muller. Syllabus CS 101 Computer Science I Fall 2013 Instructor Muller Syllabus Welcome to CS101. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts of

More information

Introduction to Simulation

Introduction to Simulation Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /

More information

Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany

Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany Jana Kitzmann and Dirk Schiereck, Endowed Chair for Banking and Finance, EUROPEAN BUSINESS SCHOOL, International

More information

THE UNIVERSITY OF SYDNEY Semester 2, Information Sheet for MATH2068/2988 Number Theory and Cryptography

THE UNIVERSITY OF SYDNEY Semester 2, Information Sheet for MATH2068/2988 Number Theory and Cryptography THE UNIVERSITY OF SYDNEY Semester 2, 2017 Information Sheet for MATH2068/2988 Number Theory and Cryptography Websites: It is important that you check the following webpages regularly. Intermediate Mathematics

More information

PHYSICS 40S - COURSE OUTLINE AND REQUIREMENTS Welcome to Physics 40S for !! Mr. Bryan Doiron

PHYSICS 40S - COURSE OUTLINE AND REQUIREMENTS Welcome to Physics 40S for !! Mr. Bryan Doiron PHYSICS 40S - COURSE OUTLINE AND REQUIREMENTS Welcome to Physics 40S for 2016-2017!! Mr. Bryan Doiron The course covers the following topics (time permitting): Unit 1 Kinematics: Special Equations, Relative

More information

Action Models and their Induction

Action Models and their Induction Action Models and their Induction Michal Čertický, Comenius University, Bratislava certicky@fmph.uniba.sk March 5, 2013 Abstract By action model, we understand any logic-based representation of effects

More information

2/15/13. POS Tagging Problem. Part-of-Speech Tagging. Example English Part-of-Speech Tagsets. More Details of the Problem. Typical Problem Cases

2/15/13. POS Tagging Problem. Part-of-Speech Tagging. Example English Part-of-Speech Tagsets. More Details of the Problem. Typical Problem Cases POS Tagging Problem Part-of-Speech Tagging L545 Spring 203 Given a sentence W Wn and a tagset of lexical categories, find the most likely tag T..Tn for each word in the sentence Example Secretariat/P is/vbz

More information

Disambiguation of Thai Personal Name from Online News Articles

Disambiguation of Thai Personal Name from Online News Articles Disambiguation of Thai Personal Name from Online News Articles Phaisarn Sutheebanjard Graduate School of Information Technology Siam University Bangkok, Thailand mr.phaisarn@gmail.com Abstract Since online

More information

Foothill College Fall 2014 Math My Way Math 230/235 MTWThF 10:00-11:50 (click on Math My Way tab) Math My Way Instructors:

Foothill College Fall 2014 Math My Way Math 230/235 MTWThF 10:00-11:50  (click on Math My Way tab) Math My Way Instructors: This is a team taught directed study course. Foothill College Fall 2014 Math My Way Math 230/235 MTWThF 10:00-11:50 www.psme.foothill.edu (click on Math My Way tab) Math My Way Instructors: Instructor:

More information

Functional Skills Mathematics Level 2 assessment

Functional Skills Mathematics Level 2 assessment Functional Skills Mathematics Level 2 assessment www.cityandguilds.com September 2015 Version 1.0 Marking scheme ONLINE V2 Level 2 Sample Paper 4 Mark Represent Analyse Interpret Open Fixed S1Q1 3 3 0

More information

University of Cincinnati College of Medicine. DECISION ANALYSIS AND COST-EFFECTIVENESS BE-7068C: Spring 2016

University of Cincinnati College of Medicine. DECISION ANALYSIS AND COST-EFFECTIVENESS BE-7068C: Spring 2016 1 DECISION ANALYSIS AND COST-EFFECTIVENESS BE-7068C: Spring 2016 Instructor Name: Mark H. Eckman, MD, MS Office:, Division of General Internal Medicine (MSB 7564) (ML#0535) Cincinnati, Ohio 45267-0535

More information

Factoring - Grouping

Factoring - Grouping 6.2 Factoring - Grouping Objective: Factor polynomials with four terms using grouping. The first thing we will always do when factoring is try to factor out a GCF. This GCF is often a monomial like in

More information

MTH 141 Calculus 1 Syllabus Spring 2017

MTH 141 Calculus 1 Syllabus Spring 2017 Instructor: Section/Meets Office Hrs: Textbook: Calculus: Single Variable, by Hughes-Hallet et al, 6th ed., Wiley. Also needed: access code to WileyPlus (included in new books) Calculator: Not required,

More information

Detecting English-French Cognates Using Orthographic Edit Distance

Detecting English-French Cognates Using Orthographic Edit Distance Detecting English-French Cognates Using Orthographic Edit Distance Qiongkai Xu 1,2, Albert Chen 1, Chang i 1 1 The Australian National University, College of Engineering and Computer Science 2 National

More information

AMULTIAGENT system [1] can be defined as a group of

AMULTIAGENT system [1] can be defined as a group of 156 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 2, MARCH 2008 A Comprehensive Survey of Multiagent Reinforcement Learning Lucian Buşoniu, Robert Babuška,

More information

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za

More information

PRODUCT PLATFORM DESIGN: A GRAPH GRAMMAR APPROACH

PRODUCT PLATFORM DESIGN: A GRAPH GRAMMAR APPROACH Proceedings of DETC 99: 1999 ASME Design Engineering Technical Conferences September 12-16, 1999, Las Vegas, Nevada DETC99/DTM-8762 PRODUCT PLATFORM DESIGN: A GRAPH GRAMMAR APPROACH Zahed Siddique Graduate

More information

Outline for Session III

Outline for Session III Outline for Session III Before you begin be sure to have the following materials Extra JM cards Extra blank break-down sheets Extra proposal sheets Proposal reports Attendance record Be at the meeting

More information

A Grammar for Battle Management Language

A Grammar for Battle Management Language Bastian Haarmann 1 Dr. Ulrich Schade 1 Dr. Michael R. Hieb 2 1 Fraunhofer Institute for Communication, Information Processing and Ergonomics 2 George Mason University bastian.haarmann@fkie.fraunhofer.de

More information

Paper 2. Mathematics test. Calculator allowed. First name. Last name. School KEY STAGE TIER

Paper 2. Mathematics test. Calculator allowed. First name. Last name. School KEY STAGE TIER 259574_P2 5-7_KS3_Ma.qxd 1/4/04 4:14 PM Page 1 Ma KEY STAGE 3 TIER 5 7 2004 Mathematics test Paper 2 Calculator allowed Please read this page, but do not open your booklet until your teacher tells you

More information

Should a business have the right to ban teenagers?

Should a business have the right to ban teenagers? practice the task Image Credits: Photodisc/Getty Images Should a business have the right to ban teenagers? You will read: You will write: a newspaper ad An Argumentative Essay Munchy s Promise a business

More information

What the National Curriculum requires in reading at Y5 and Y6

What the National Curriculum requires in reading at Y5 and Y6 What the National Curriculum requires in reading at Y5 and Y6 Word reading apply their growing knowledge of root words, prefixes and suffixes (morphology and etymology), as listed in Appendix 1 of the

More information

Transfer Learning Action Models by Measuring the Similarity of Different Domains

Transfer Learning Action Models by Measuring the Similarity of Different Domains Transfer Learning Action Models by Measuring the Similarity of Different Domains Hankui Zhuo 1, Qiang Yang 2, and Lei Li 1 1 Software Research Institute, Sun Yat-sen University, Guangzhou, China. zhuohank@gmail.com,lnslilei@mail.sysu.edu.cn

More information

A process by any other name

A process by any other name January 05, 2016 Roger Tregear A process by any other name thoughts on the conflicted use of process language What s in a name? That which we call a rose By any other name would smell as sweet. William

More information

An Introduction to Simio for Beginners

An Introduction to Simio for Beginners An Introduction to Simio for Beginners C. Dennis Pegden, Ph.D. This white paper is intended to introduce Simio to a user new to simulation. It is intended for the manufacturing engineer, hospital quality

More information

Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines

Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines Amit Juneja and Carol Espy-Wilson Department of Electrical and Computer Engineering University of Maryland,

More information

Stacks Teacher notes. Activity description. Suitability. Time. AMP resources. Equipment. Key mathematical language. Key processes

Stacks Teacher notes. Activity description. Suitability. Time. AMP resources. Equipment. Key mathematical language. Key processes Stacks Teacher notes Activity description (Interactive not shown on this sheet.) Pupils start by exploring the patterns generated by moving counters between two stacks according to a fixed rule, doubling

More information

MOODLE 2.0 GLOSSARY TUTORIALS

MOODLE 2.0 GLOSSARY TUTORIALS BEGINNING TUTORIALS SECTION 1 TUTORIAL OVERVIEW MOODLE 2.0 GLOSSARY TUTORIALS The glossary activity module enables participants to create and maintain a list of definitions, like a dictionary, or to collect

More information

Reinforcement Learning by Comparing Immediate Reward

Reinforcement Learning by Comparing Immediate Reward Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate

More information

Extending Place Value with Whole Numbers to 1,000,000

Extending Place Value with Whole Numbers to 1,000,000 Grade 4 Mathematics, Quarter 1, Unit 1.1 Extending Place Value with Whole Numbers to 1,000,000 Overview Number of Instructional Days: 10 (1 day = 45 minutes) Content to Be Learned Recognize that a digit

More information

Guide to the Uniform mark scale (UMS) Uniform marks in A-level and GCSE exams

Guide to the Uniform mark scale (UMS) Uniform marks in A-level and GCSE exams Guide to the Uniform mark scale (UMS) Uniform marks in A-level and GCSE exams This booklet explains why the Uniform mark scale (UMS) is necessary and how it works. It is intended for exams officers and

More information

Planning with External Events

Planning with External Events 94 Planning with External Events Jim Blythe School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 blythe@cs.cmu.edu Abstract I describe a planning methodology for domains with uncertainty

More information

Radius STEM Readiness TM

Radius STEM Readiness TM Curriculum Guide Radius STEM Readiness TM While today s teens are surrounded by technology, we face a stark and imminent shortage of graduates pursuing careers in Science, Technology, Engineering, and

More information

GACE Computer Science Assessment Test at a Glance

GACE Computer Science Assessment Test at a Glance GACE Computer Science Assessment Test at a Glance Updated May 2017 See the GACE Computer Science Assessment Study Companion for practice questions and preparation resources. Assessment Name Computer Science

More information

CALCULUS III MATH

CALCULUS III MATH CALCULUS III MATH 01230-1 1. Instructor: Dr. Evelyn Weinstock Mathematics Department, Robinson, Second Floor, 228E 856-256-4500, ext. 3862, email: weinstock@rowan.edu Days/Times: Monday & Thursday 2:00-3:15,

More information

Pre-AP Geometry Course Syllabus Page 1

Pre-AP Geometry Course Syllabus Page 1 Pre-AP Geometry Course Syllabus 2015-2016 Welcome to my Pre-AP Geometry class. I hope you find this course to be a positive experience and I am certain that you will learn a great deal during the next

More information