Logics with Verbs, Relative Clauses, and Comparative Adjectives

Size: px
Start display at page:

Download "Logics with Verbs, Relative Clauses, and Comparative Adjectives"

Transcription

1 1/48 Logics with Verbs, Relative Clauses, and Comparative Adjectives Larry Moss, Indiana University EASLLC 2014

2 2/48 Where we are FOL Turing FO 2 + trans RC (tr,opp) Aristotle FO 2 S S R R RC (tr) RC RC(tr) RC RC(tr, opp) adds full N-negation R + (transitive) comparative adjs R + relative clauses done relational syllogistic all these are today done

3 3/48 Preliminary work, done as a set of group exercises I am going to introduce this topic in a slow way by cutting things down to a very small language and working up. (This is my favorite thing to do for this subject!) Working with the small language enables us to do a lot of the proofs, and to get our hands dirty. After we do this, I will go more quickly and omit a huge number of details.

4 4/48 Does the conclusion follow? All boys are males All women see all males All who see all boys see all males The definition of follow here is in terms of models. For this fragment, a model M should be a set M, the universe subsets of M for the nouns: [[boys]], [[males]], and [[females]] a subset of M M for the verb: [[see]] In other words, it is a model like we had before, but with a set of pairs of individuals to interpret the verb.

5 5/48 Syntax We make set terms in the following way: see is a set term. If x is a set term, so is (see all x). We can read this in various ways to make it sound more like English. Sometimes it would be as see all who are x. So we get set terms like see all boys see all (see all women) see all (see all (see all women))

6 5/48 Syntax We make set terms in the following way: see is a set term. If x is a set term, so is (see all x). We can read this in various ways to make it sound more like English. Sometimes it would be as see all who are x. So we get set terms like see all boys see all (see all women) see all (see all (see all women)) Later on, we ll call set terms set terms.

7 6/48 Syntax and Semantics For our sentences, we take all expressions where x and y are set terms. All x y In the semantics, we need an inductive definition to interpret the set terms. [[see all x]] = {m M : }

8 6/48 For our sentences, we take all expressions where x and y are set terms. All x y Syntax and Semantics In the semantics, we need an inductive definition to interpret the set terms. [[see all x]] = {m M : } [[see all x ]] = {m M : for all n [[x]], (m,n) [[see]]}

9 7/48 Example of a model [[males]] = {a,d,f } [[females]] = {b,c,e} [[boys]] = {a,d} d c b a e f Technically, M = {a,...,f }, [[see]] = {(a,b),(a,c),(a,e),(c,d),(b,a),(b,b), (b,f ),(c,f ),(d,b),(d,d),(d,e),(d,f )}

10 Example of a model [[males]] = {a,d,f } [[females]] = {b,c,e} [[boys]] = {a,d} d c b a e f What is [[see all males]]? What is [[see all females]]? What is [[see all (see all females)]]? What is [[see all (see all (see all females))]]? 7/48

11 8/48 Logic For sure we need the logic of All from earlier: All x are x All x are y All y are z All x are z Barbara But note that we are using this with x, y, and z as set terms. But in fact we are missing at least one rule!

12 9/48 Logic All x are x axiom All y x All (see all x)(see all y) skunk All x are y All y are z All x are y Barbara

13 9/48 Logic All x are x axiom All y x All (see all x)(see all y) skunk All x are y All y are z All x are y Barbara Example: All x (see all y),all z y All x (see all z) All z y All x (see all y) All (see all y)(see all z) All x (see all z)

14 10/48 Does the conclusion follow? All boys are males All who see all women also see all who see all boys All who see all see all boys they are all women All women see all males All who see all boys see all males In our language, this is All boys males All (see all women) (see all (see all boys)) All (see all (see all boys)) women All women (see all males) All (see all boys) (see all males)

15 11/48 The canonical model Γ of a set of assertions in this logic We are given a set Γ in this language. We aim to build a model M of Γ with the property that if M = ϕ, then Γ ϕ. This would prove the completeness theorem for our logic, because if Γ ϕ, then M = ϕ. (In other words, every sentence which is true in all models of Γ (hence in M) would be provable from Γ.)

16 12/48 The canonical model Γ of a set of assertions in this logic Let M be the set of all set terms. Let [[females]] = {x : Γ All x females} [[males]] = {x : Γ All x males} [[boys]] = {x : Γ All x boys} The hardest point is to decide on the semantics of see

17 12/48 The canonical model Γ of a set of assertions in this logic Let M be the set of all set terms. Let [[females]] = {x : Γ All x females} [[males]] = {x : Γ All x males} [[boys]] = {x : Γ All x boys} The hardest point is to decide on the semantics of see [[see]] = {(x,y) : Γ All x see all y}

18 Does the conclusion follow? All p are q All who see all who see all p (see all q) The way we ll do this is to draw the canonical model of Γ, where Γ = {All p are q}. To make the notation simpler, let us write p 0 for p, and p n+1 for see all p n ; we adopt similar notation for q. Then the canonical model of Γ is shown below: p 0 p 1 p 2 p 3 p 4 q 0 q 1 q 2 q 3 q 4 Notice that the notation for subset arrows is different than the arrows for see. Notice also that we have omitted the reflexive subset arrows on all of the nodes, and also that the arrows which are shown alternate directions. 13/48

19 14/48 Theory Lemma: [[x]] = {y : Γ All y are x} The proof is by induction on x. Lemma: M = Γ This follows from the last lemma. Lemma: If M = ϕ, then Γ ϕ. This is basically what we saw yesterday for the language of All.

20 15/48 Solving the problem Does this follow? All boys males All (see all women) (see all (see all boys)) All (see all (see all boys)) women All women (see all males) All (see all boys) (see all males) We make the part of the canonical model just using the set terms above, and all the subterms: boys men women see all boys see all men see all women see all see all women

21 16/48 More boys see all boys subset see all see all boys subset subset see all males see all females males subset females

22 17/48 Here is part of the see relation The logic gives us part of the see relation in a trivial way boys see all boys see all see all boys see all males see all females males females

23 18/48 Many verbs What would we do if we had more than one verb? All women are football players All football fans love all football players All football fans like all women Does this work?

24 19/48 Background knowledge In addition to our assumptions Γ, we can adopt a set of background assumptions which need not be expressible in our language. In this case, we might say = {loves likes} Then we require that our models respect the background assumptions: [[loves]] [[likes]].

25 20/48 Background knowledge: a new logical law All x love all y All x like all y What does completeness say? And why does it hold?

26 20/48 Background knowledge: a new logical law All x love all y All x like all y What does completeness say? Theorem: The following are equivalent Every model of Γ which respects the assumptions in satisfies ϕ. There is a proof of ϕ using the assumptions in Γ and the logic previously described, augmented by the inference rules corresponding to the sentences in And why does it hold?

27 Adding transitive verbs all work onrand related systems is joint with Ian Pratt-Hartmann The next language uses see or r as variables for transitive verbs. All p are q Some p are q All p see all q All p see some q Some p see all q Some p see some q All p aren t q No p are q Some p aren t q All p don t see all q No p sees any q All p don t see some q No p sees all q Some p don t see any q Some p don t see some q The interpretation is the natural one, using the subject wide scope readings in the ambiguous cases. This is R. (The first system of its kind was Nishihara, Morita, Iwata 1990.) The language R has complemented atoms p on top of R. 21/48

28 22/48 Example of a proof in the system for R What do you think? Sound or unsound? All X see all Y,All X see some Z,All Z see some Y = All X see some Y

29 22/48 Example of a proof in the system for R What do you think? Sound or unsound? All X see all Y,All X see some Z,All Z see some Y = All X see some Y The conclusion does indeed follow: take cases as to whether or not there are Z. We should have a formal proof.

30 23/48 Example of a proof in this system All X see all Y,All X see some Z,All Z see some Y All X see some Y Some X see no Y Some X are X All X see some Z Some X see some Z Some Z are Z All Z see some Y Some Z see some Y Some Y are Y All X see all Y All X see some Y Some X see no Y Some X aren t X

31 24/48 But now [Some X see no Y ] Some X are X All X see some Z Some X see some Z Some Z are Z All Z see some Y Some Z see some Y Some Y are Y All X see all Y All X see some Y [Some X see no Y ] Some X aren t X All X see some Y RAA This shows that All X see all Y,All X see some Z,All Z see some Y All X see some Y

32 25/48 Towards the syntax for R All p are q (p,q) Some p are q (p,q) All p r all q (p, (q,r)) All p r some q (p, (q,r)) Some p r all q (p, (q,r)) Some p r some q (p, (q,r)) No p are q (p,q) Some p aren t q (p,q) All p don t r all q No p r any q (p, (q,r)) All p don t r some q No p r all q (p, (q,r)) Some p don t r any q (p, (q, r)) Some p don t r some q (p, (q, r))

33 25/48 Towards the syntax for R All p are q (p,q) Some p are q (p,q) All p r all q (p, (q,r)) All p r some q (p, (q,r)) Some p r all q (p, (q,r)) Some p r some q (p, (q,r)) No p are q (p,q) Some p aren t q (p,q) No p r any q (p, (q,r)) No p r all q (p, (q,r)) Some p don t r any q (p, (q, r)) Some p don t r some q (p, (q, r)) set terms c positive p (p, r) (p, r) negative p (p, r) (p, r)

34 26/48 Reading the set terms (p,r) those wh r all p (p,r) those wh r some p (p,r) those wh fail-to-r all p = those wh r no p (p,r) those wh fail-to-r some p = those wh don t r some p

35 27/48 More on the set terms To understand how they work, let us exhibit a rendering of the simplest set terms into more idiomatic English: (boy,see) (girl,admire) (boy,see) (girl,recognize) those who see all boys those who admire some girl(s) those who fail-to-see all boys = those who see no boys = those who don t see any boys those who fail-to-recognize some girl = those who don t recognize some girl or other

36 28/48 Towards the syntax for R All p are q (p,q) Some p are q (p,q) All p r all q (p, (q,r)) All p r some q (p, (q,r)) Some p r all q (p, (q,r)) Some p r some q (p, (q,r)) No p are q (p,q) Some p aren t q (p,q) No p sees any q (p, (q, r)) No p sees all q (p, (q, r)) Some p don t r any q (p, (q, r)) Some p don t r some q (p, (q, r)) simplifies to (p, c) (p, c) set terms c positive p (p, r) (p, r) negative p (p, r) (p, r)

37 29/48 Syntax of R and related languages We start with one collection of unary atoms (for nouns) and another of binary atoms (for transitive verbs). expression variables syntax unary atom p, q binary atom r positive set term c + p (p,r) (p,r) set term c,d p (p,r) (p,r) p (p,r) (p,r) R sentence ϕ (p, c) (p, c) R sentence ϕ (p,c) (p,c) (p,c) (p,c) RC sentence ϕ (d +,c) (d +,c) RC sentence ϕ (d,c) (d,c)

38 30/48 Negations We need one last concept, syntactic negation: expression syntax negation positive set term c p p p p (p,r) (p,r) (p,r) (p,r) (p,r) (p,r) (p,r) (p,r) R sentence ϕ (p, c) (p, c) (p, c) (p, c) Note that p = p, c = c and ϕ = ϕ.

39 example Consider the model M with M = {w,x,y,z}, [[cat]] = {w,x,y}, [[dog]] = {z}, with [[see]] shown below on the left, and [[likes]] on the right: w x y z x,z y w Then [[ (dog,see)]] is the set of entities that see some dog, namely {x,y,z,w}. Similarly, [[ (dog,likes)]] = {w,y}. It follows that Since [[cat]] contains x, we have [[ ( (dog,likes),see)]] = {x}. M = ( ( (dog, likes), see), cat). That is, in our model it is true that everything which sees everything bigger than some dog is a cat. 31/48

40 32/48 Relational syllogistic logic p and q range over unary atoms, c over set terms, and t over binary atoms or their negations. (p, q) (q, c) (p, c) (p, q) (q, c) (p, c) (p, q) (p, c) (q, c) (p, p) (p, c) (p, p) (q, c) (p, c) (p, q) (p, p) (p, c) (p, (q, t)) (q, q) (p, (n, t)) (q, n) (p, (q, t)) (p, (q, t)) (q, n) (p, (n, t)) (p, (q, t)) (q, n) (p, (n, t)) [ϕ]. (p, p) RAA ϕ

41 33/48 Relational syllogistic logic Most are monotonicty principles (p,q ) (p,q ) (p, (q,t)) (p, (q,t)) (p, (q,t)) (p, (q,t)) Plus also (p, p) (p, c) (p, p) (p, p) (p, c) (p, (q, t)) (q, q) (q, c) (p, c) (p, q) ( ) (p, (n, t)) (q, n) (p, (q, t)) Of these, ( ) is the most interesting.

42 34/48 Example of a proof in the system for R Every porter recognizes every porter No quarterback recognizes any quarterback No porter is a quarterback The derivation in the logical system for R is (q, (q,r)) (p, (p,r)) [ (p,q)] (p, (q,r)) [ (p,q)] (q, (q,r)) ( ) (q, q) (p,q) RAA

43 35/48 Results on R and R Theorem The system that we have is complete. There are no purely syllogistic logical systems complete for R, that is no systems without RAA.

44 35/48 Results on R and R Theorem The system that we have is complete. There are no purely syllogistic logical systems complete for R, that is no systems without RAA. Theorem There are no finite, complete syllogistic logical systems for R, even ones which allow RAA.

45 36/48 RCA Now let s add comparative adjectives, interpreted as transitive relations.

46 37/48 Example of what we want to formalize in RCA Example Every hyena is taller than some jackal Everything taller than some jackal is not heavier than any warthog Everything which is taller than some hyena is not heavier than any warthog

47 38/48 Syntax expression variables syntax unary atom p, q adjective atom a tv atom v binary atom r v a positive set term c +,d + p (p,r) (p,r) set term c,d c + p (p,r) (p,r) sentence ϕ (d +,c) (d +,c)

48 39/48 A few more examples (boy,see) (girl,taller) (boy,see) (girl,see) those who see all boys those who are taller than some girl(s) those who fail-to-see all boys = those who see no boys = those who don t see any boys those who fail-to-see some girl = those who don t see some girl

49 40/48 The set terms in this language are a recursive construct We may embed set terms. So we have set terms like ( (cat, sees), taller) which may be taken to denote the individuals who are taller than someone who sees no cat. We should note that the relative clauses which can be obtained in this way are all missing the subject, never missing the object. The language is too poor to express predicates like λx.all boys see x.

50 41/48 Semantics A model (for this language L) is a pair M = (M,[[ ]]), where M is a non-empty set, [[p]] M for all p P, [[r]] M 2 for all binary atoms r R. The only requirement is that for all adjectives a, [[a]] should be a transitive relation: if a(x,y) and a(y,z), then a(x,z).

51 42/48 Semantics Given a model M, we extend the interpretation function [[ ]] to the rest of the language by setting [[p]] = M \ [[p]] [[r]] = M 2 \ [[r]] [[ (l,t)]] = {x M : for some y such that [[l]](y), [[t]](x,y)} [[ (l,t)]] = {x M : for all y such that [[l]](y), [[t]](x,y)} We define the truth relation = between models and sentences by: M = (c,d) iff [[c]] [[d]] M = (c,d) iff [[c]] [[d]] If Γ is a set of formulas, we write M = Γ if for all ϕ Γ, M = ϕ.

52 Logic (c,c) (T) (c,d) (c,c) (I) (c,b) (b,c) (C) (b,c) (c,d) (B) (b, d) (b, c) (c, d) (b, d) (D1) (b, c) (b, d) (c, d) (D2) (p, q) ( (q,r), (p,r)) (J) (p, q) ( (p,r), (q,r)) (L) (p, p) (c, (p,r)) (Z) (p, (q, a)) ( (p,a), (q,a)) (tr1) (p, (q, a)) ( (p,a), (q,a)) (tr3) (p,q) ( (p,r), (q,r)) (K) (q, (p,r)) (p, p) (II) (p, p) ( (p,r), (p,r)) (W) (p, (q,a)) ( (p,a), (q,a)) (tr2) (p, (q,a)) ( (p,a), (q,a)) (tr4) [ϕ]. ϕ RAA The proof system for logic of RCA. In it, p and q range over unary atoms, b and c over set terms, d over positive set terms, r over binary atoms, and a over adjective atoms. 43/48

53 44/48 An invalid inference The following inference is invalid: Every giraffe sees every gnu Some gnu sees every lion Some lion sees some zebra Every giraffe sees some zebra To see this, consider the model shown below giraffe gnu lion zebra The interpretations of the unary atoms are obvious, and the interpretation of the verb is the relation indicated by the arrow.

54 45/48 Reading the rules (J) If all watches are gold items, then everyone who owns all gold items owns all watches. (K) If all watches are gold items, then everyone who owns some watch owns some gold item. (L) If some watches are gold items, then everyone who owns all watches owns some gold item. (II) If someone owns a watch, then there is a watch. (tr1) If all watches are bigger than some pencil, then everthing bigger than some watch is bigger than some pencil. (tr2) If all watches are bigger than all pencils, then everthing bigger than some watch is bigger than all pencils. (tr3) If some watch is bigger than all pencils, then everthing bigger than all watches is bigger than all pencils. (tr4) If some watch is bigger than some pencil, then everthing bigger than all watches is bigger than some pencil.

55 46/48 Return of the skunks Here is a derivation for an example from early on in this course: (skunk, mammal) ( (mammal,respect), (skunk,respect)) (J) ( ( (skunk,respect),fear), ( (mammal,respect),fear)) (J) Note that inference using (J) is antitone each time: skunk and mammal have switched positions.

56 47/48 Return of the jackals and hyenas Every hyena is taller than some jackal Everything taller than some jackal is not heavier than any warthog Everything which is taller than some hyena is not heavier than any warthog (hyena, (jackal, taller)) ( (hyena,taller), (jkl,taller)) (tr1) ( (jkl,taller), (warthog,heavier)) ( (hyena, taller), (warthog, heavier)))

57 48/48 Zero Here is a proof of the (Zero) rule of S : (p,p) (p,q): [ (p,q)] 1 (p, p) (I) (p, p) (p, p) (p,q) (RAA)1 (D1)

CHAPTER 7 GENERAL PROOF SYSTEMS

CHAPTER 7 GENERAL PROOF SYSTEMS CHAPTER 7 GENERAL PROOF SYSTEMS 1 Introduction Proof systems are built to prove statements. They can be thought as an inference machine with special statements, called provable statements, or sometimes

More information

A Propositional Dynamic Logic for CCS Programs

A Propositional Dynamic Logic for CCS Programs A Propositional Dynamic Logic for CCS Programs Mario R. F. Benevides and L. Menasché Schechter {mario,luis}@cos.ufrj.br Abstract This work presents a Propositional Dynamic Logic in which the programs are

More information

INCIDENCE-BETWEENNESS GEOMETRY

INCIDENCE-BETWEENNESS GEOMETRY INCIDENCE-BETWEENNESS GEOMETRY MATH 410, CSUSM. SPRING 2008. PROFESSOR AITKEN This document covers the geometry that can be developed with just the axioms related to incidence and betweenness. The full

More information

! " # The Logic of Descriptions. Logics for Data and Knowledge Representation. Terminology. Overview. Three Basic Features. Some History on DLs

!  # The Logic of Descriptions. Logics for Data and Knowledge Representation. Terminology. Overview. Three Basic Features. Some History on DLs ,!0((,.+#$),%$(-&.& *,2(-$)%&2.'3&%!&, Logics for Data and Knowledge Representation Alessandro Agostini agostini@dit.unitn.it University of Trento Fausto Giunchiglia fausto@dit.unitn.it The Logic of Descriptions!$%&'()*$#)

More information

University of Ostrava. Reasoning in Description Logic with Semantic Tableau Binary Trees

University of Ostrava. Reasoning in Description Logic with Semantic Tableau Binary Trees University of Ostrava Institute for Research and Applications of Fuzzy Modeling Reasoning in Description Logic with Semantic Tableau Binary Trees Alena Lukasová Research report No. 63 2005 Submitted/to

More information

3. Mathematical Induction

3. Mathematical Induction 3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)

More information

CHAPTER 3. Methods of Proofs. 1. Logical Arguments and Formal Proofs

CHAPTER 3. Methods of Proofs. 1. Logical Arguments and Formal Proofs CHAPTER 3 Methods of Proofs 1. Logical Arguments and Formal Proofs 1.1. Basic Terminology. An axiom is a statement that is given to be true. A rule of inference is a logical rule that is used to deduce

More information

The Syntax of Predicate Logic

The Syntax of Predicate Logic The Syntax of Predicate Logic LX 502 Semantics I October 11, 2008 1. Below the Sentence-Level In Propositional Logic, atomic propositions correspond to simple sentences in the object language. Since atomic

More information

So let us begin our quest to find the holy grail of real analysis.

So let us begin our quest to find the holy grail of real analysis. 1 Section 5.2 The Complete Ordered Field: Purpose of Section We present an axiomatic description of the real numbers as a complete ordered field. The axioms which describe the arithmetic of the real numbers

More information

Automata and Formal Languages

Automata and Formal Languages Automata and Formal Languages Winter 2009-2010 Yacov Hel-Or 1 What this course is all about This course is about mathematical models of computation We ll study different machine models (finite automata,

More information

CONTENTS 1. Peter Kahn. Spring 2007

CONTENTS 1. Peter Kahn. Spring 2007 CONTENTS 1 MATH 304: CONSTRUCTING THE REAL NUMBERS Peter Kahn Spring 2007 Contents 2 The Integers 1 2.1 The basic construction.......................... 1 2.2 Adding integers..............................

More information

This asserts two sets are equal iff they have the same elements, that is, a set is determined by its elements.

This asserts two sets are equal iff they have the same elements, that is, a set is determined by its elements. 3. Axioms of Set theory Before presenting the axioms of set theory, we first make a few basic comments about the relevant first order logic. We will give a somewhat more detailed discussion later, but

More information

Lecture 16 : Relations and Functions DRAFT

Lecture 16 : Relations and Functions DRAFT CS/Math 240: Introduction to Discrete Mathematics 3/29/2011 Lecture 16 : Relations and Functions Instructor: Dieter van Melkebeek Scribe: Dalibor Zelený DRAFT In Lecture 3, we described a correspondence

More information

6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010. Class 4 Nancy Lynch

6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010. Class 4 Nancy Lynch 6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010 Class 4 Nancy Lynch Today Two more models of computation: Nondeterministic Finite Automata (NFAs)

More information

facultad de informática universidad politécnica de madrid

facultad de informática universidad politécnica de madrid facultad de informática universidad politécnica de madrid On the Confluence of CHR Analytical Semantics Rémy Haemmerlé Universidad olitécnica de Madrid & IMDEA Software Institute, Spain TR Number CLI2/2014.0

More information

CS510 Software Engineering

CS510 Software Engineering CS510 Software Engineering Propositional Logic Asst. Prof. Mathias Payer Department of Computer Science Purdue University TA: Scott A. Carr Slides inspired by Xiangyu Zhang http://nebelwelt.net/teaching/15-cs510-se

More information

Boolean Algebra Part 1

Boolean Algebra Part 1 Boolean Algebra Part 1 Page 1 Boolean Algebra Objectives Understand Basic Boolean Algebra Relate Boolean Algebra to Logic Networks Prove Laws using Truth Tables Understand and Use First Basic Theorems

More information

Full and Complete Binary Trees

Full and Complete Binary Trees Full and Complete Binary Trees Binary Tree Theorems 1 Here are two important types of binary trees. Note that the definitions, while similar, are logically independent. Definition: a binary tree T is full

More information

Cartesian Products and Relations

Cartesian Products and Relations Cartesian Products and Relations Definition (Cartesian product) If A and B are sets, the Cartesian product of A and B is the set A B = {(a, b) :(a A) and (b B)}. The following points are worth special

More information

Regular Languages and Finite Automata

Regular Languages and Finite Automata Regular Languages and Finite Automata 1 Introduction Hing Leung Department of Computer Science New Mexico State University Sep 16, 2010 In 1943, McCulloch and Pitts [4] published a pioneering work on a

More information

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2 CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2 Proofs Intuitively, the concept of proof should already be familiar We all like to assert things, and few of us

More information

Mathematical Induction

Mathematical Induction Mathematical Induction (Handout March 8, 01) The Principle of Mathematical Induction provides a means to prove infinitely many statements all at once The principle is logical rather than strictly mathematical,

More information

MATH10040 Chapter 2: Prime and relatively prime numbers

MATH10040 Chapter 2: Prime and relatively prime numbers MATH10040 Chapter 2: Prime and relatively prime numbers Recall the basic definition: 1. Prime numbers Definition 1.1. Recall that a positive integer is said to be prime if it has precisely two positive

More information

CS 103X: Discrete Structures Homework Assignment 3 Solutions

CS 103X: Discrete Structures Homework Assignment 3 Solutions CS 103X: Discrete Structures Homework Assignment 3 s Exercise 1 (20 points). On well-ordering and induction: (a) Prove the induction principle from the well-ordering principle. (b) Prove the well-ordering

More information

4. CLASSES OF RINGS 4.1. Classes of Rings class operator A-closed Example 1: product Example 2:

4. CLASSES OF RINGS 4.1. Classes of Rings class operator A-closed Example 1: product Example 2: 4. CLASSES OF RINGS 4.1. Classes of Rings Normally we associate, with any property, a set of objects that satisfy that property. But problems can arise when we allow sets to be elements of larger sets

More information

6.3 Conditional Probability and Independence

6.3 Conditional Probability and Independence 222 CHAPTER 6. PROBABILITY 6.3 Conditional Probability and Independence Conditional Probability Two cubical dice each have a triangle painted on one side, a circle painted on two sides and a square painted

More information

WRITING PROOFS. Christopher Heil Georgia Institute of Technology

WRITING PROOFS. Christopher Heil Georgia Institute of Technology WRITING PROOFS Christopher Heil Georgia Institute of Technology A theorem is just a statement of fact A proof of the theorem is a logical explanation of why the theorem is true Many theorems have this

More information

Lecture 1. Basic Concepts of Set Theory, Functions and Relations

Lecture 1. Basic Concepts of Set Theory, Functions and Relations September 7, 2005 p. 1 Lecture 1. Basic Concepts of Set Theory, Functions and Relations 0. Preliminaries...1 1. Basic Concepts of Set Theory...1 1.1. Sets and elements...1 1.2. Specification of sets...2

More information

Introduction to Automata Theory. Reading: Chapter 1

Introduction to Automata Theory. Reading: Chapter 1 Introduction to Automata Theory Reading: Chapter 1 1 What is Automata Theory? Study of abstract computing devices, or machines Automaton = an abstract computing device Note: A device need not even be a

More information

How To Write Intuitionistic Logic

How To Write Intuitionistic Logic AN INTUITIONISTIC EPISTEMIC LOGIC FOR ASYNCHRONOUS COMMUNICATION by Yoichi Hirai A Master Thesis Submitted to the Graduate School of the University of Tokyo on February 10, 2010 in Partial Fulfillment

More information

Predicate logic Proofs Artificial intelligence. Predicate logic. SET07106 Mathematics for Software Engineering

Predicate logic Proofs Artificial intelligence. Predicate logic. SET07106 Mathematics for Software Engineering Predicate logic SET07106 Mathematics for Software Engineering School of Computing Edinburgh Napier University Module Leader: Uta Priss 2010 Copyright Edinburgh Napier University Predicate logic Slide 1/24

More information

Student Outcomes. Lesson Notes. Classwork. Discussion (10 minutes)

Student Outcomes. Lesson Notes. Classwork. Discussion (10 minutes) NYS COMMON CORE MATHEMATICS CURRICULUM Lesson 5 8 Student Outcomes Students know the definition of a number raised to a negative exponent. Students simplify and write equivalent expressions that contain

More information

Correspondence analysis for strong three-valued logic

Correspondence analysis for strong three-valued logic Correspondence analysis for strong three-valued logic A. Tamminga abstract. I apply Kooi and Tamminga s (2012) idea of correspondence analysis for many-valued logics to strong three-valued logic (K 3 ).

More information

Mathematical Induction. Lecture 10-11

Mathematical Induction. Lecture 10-11 Mathematical Induction Lecture 10-11 Menu Mathematical Induction Strong Induction Recursive Definitions Structural Induction Climbing an Infinite Ladder Suppose we have an infinite ladder: 1. We can reach

More information

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a

More information

Basic Set Theory. 1. Motivation. Fido Sue. Fred Aristotle Bob. LX 502 - Semantics I September 11, 2008

Basic Set Theory. 1. Motivation. Fido Sue. Fred Aristotle Bob. LX 502 - Semantics I September 11, 2008 Basic Set Theory LX 502 - Semantics I September 11, 2008 1. Motivation When you start reading these notes, the first thing you should be asking yourselves is What is Set Theory and why is it relevant?

More information

Synthetic Projective Treatment of Cevian Nests and Graves Triangles

Synthetic Projective Treatment of Cevian Nests and Graves Triangles Synthetic Projective Treatment of Cevian Nests and Graves Triangles Igor Minevich 1 Introduction Several proofs of the cevian nest theorem (given below) are known, including one using ratios along sides

More information

Predicate Logic. For example, consider the following argument:

Predicate Logic. For example, consider the following argument: Predicate Logic The analysis of compound statements covers key aspects of human reasoning but does not capture many important, and common, instances of reasoning that are also logically valid. For example,

More information

Programming Languages Featherweight Java David Walker

Programming Languages Featherweight Java David Walker Programming Languages Featherweight Java David Walker Overview Featherweight Java (FJ), a minimal Javalike language. Models inheritance and subtyping. Immutable objects: no mutation of fields. Trivialized

More information

Projective Geometry - Part 2

Projective Geometry - Part 2 Projective Geometry - Part 2 Alexander Remorov alexanderrem@gmail.com Review Four collinear points A, B, C, D form a harmonic bundle (A, C; B, D) when CA : DA CB DB = 1. A pencil P (A, B, C, D) is the

More information

On strong fairness in UNITY

On strong fairness in UNITY On strong fairness in UNITY H.P.Gumm, D.Zhukov Fachbereich Mathematik und Informatik Philipps Universität Marburg {gumm,shukov}@mathematik.uni-marburg.de Abstract. In [6] Tsay and Bagrodia present a correct

More information

Homework until Test #2

Homework until Test #2 MATH31: Number Theory Homework until Test # Philipp BRAUN Section 3.1 page 43, 1. It has been conjectured that there are infinitely many primes of the form n. Exhibit five such primes. Solution. Five such

More information

Lemma 5.2. Let S be a set. (1) Let f and g be two permutations of S. Then the composition of f and g is a permutation of S.

Lemma 5.2. Let S be a set. (1) Let f and g be two permutations of S. Then the composition of f and g is a permutation of S. Definition 51 Let S be a set bijection f : S S 5 Permutation groups A permutation of S is simply a Lemma 52 Let S be a set (1) Let f and g be two permutations of S Then the composition of f and g is a

More information

INTRODUCTORY SET THEORY

INTRODUCTORY SET THEORY M.Sc. program in mathematics INTRODUCTORY SET THEORY Katalin Károlyi Department of Applied Analysis, Eötvös Loránd University H-1088 Budapest, Múzeum krt. 6-8. CONTENTS 1. SETS Set, equal sets, subset,

More information

Sentence Structure/Sentence Types HANDOUT

Sentence Structure/Sentence Types HANDOUT Sentence Structure/Sentence Types HANDOUT This handout is designed to give you a very brief (and, of necessity, incomplete) overview of the different types of sentence structure and how the elements of

More information

Probability. a number between 0 and 1 that indicates how likely it is that a specific event or set of events will occur.

Probability. a number between 0 and 1 that indicates how likely it is that a specific event or set of events will occur. Probability Probability Simple experiment Sample space Sample point, or elementary event Event, or event class Mutually exclusive outcomes Independent events a number between 0 and 1 that indicates how

More information

8 Divisibility and prime numbers

8 Divisibility and prime numbers 8 Divisibility and prime numbers 8.1 Divisibility In this short section we extend the concept of a multiple from the natural numbers to the integers. We also summarize several other terms that express

More information

(IALC, Chapters 8 and 9) Introduction to Turing s life, Turing machines, universal machines, unsolvable problems.

(IALC, Chapters 8 and 9) Introduction to Turing s life, Turing machines, universal machines, unsolvable problems. 3130CIT: Theory of Computation Turing machines and undecidability (IALC, Chapters 8 and 9) Introduction to Turing s life, Turing machines, universal machines, unsolvable problems. An undecidable problem

More information

Copyright 2012 MECS I.J.Information Technology and Computer Science, 2012, 1, 50-63

Copyright 2012 MECS I.J.Information Technology and Computer Science, 2012, 1, 50-63 I.J. Information Technology and Computer Science, 2012, 1, 50-63 Published Online February 2012 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2012.01.07 Using Logic Programming to Represent

More information

Chapter 3. Cartesian Products and Relations. 3.1 Cartesian Products

Chapter 3. Cartesian Products and Relations. 3.1 Cartesian Products Chapter 3 Cartesian Products and Relations The material in this chapter is the first real encounter with abstraction. Relations are very general thing they are a special type of subset. After introducing

More information

C H A P T E R Regular Expressions regular expression

C H A P T E R Regular Expressions regular expression 7 CHAPTER Regular Expressions Most programmers and other power-users of computer systems have used tools that match text patterns. You may have used a Web search engine with a pattern like travel cancun

More information

Lecture 1: Course overview, circuits, and formulas

Lecture 1: Course overview, circuits, and formulas Lecture 1: Course overview, circuits, and formulas Topics in Complexity Theory and Pseudorandomness (Spring 2013) Rutgers University Swastik Kopparty Scribes: John Kim, Ben Lund 1 Course Information Swastik

More information

A first step towards modeling semistructured data in hybrid multimodal logic

A first step towards modeling semistructured data in hybrid multimodal logic A first step towards modeling semistructured data in hybrid multimodal logic Nicole Bidoit * Serenella Cerrito ** Virginie Thion * * LRI UMR CNRS 8623, Université Paris 11, Centre d Orsay. ** LaMI UMR

More information

k, then n = p2α 1 1 pα k

k, then n = p2α 1 1 pα k Powers of Integers An integer n is a perfect square if n = m for some integer m. Taking into account the prime factorization, if m = p α 1 1 pα k k, then n = pα 1 1 p α k k. That is, n is a perfect square

More information

Linear Algebra I. Ronald van Luijk, 2012

Linear Algebra I. Ronald van Luijk, 2012 Linear Algebra I Ronald van Luijk, 2012 With many parts from Linear Algebra I by Michael Stoll, 2007 Contents 1. Vector spaces 3 1.1. Examples 3 1.2. Fields 4 1.3. The field of complex numbers. 6 1.4.

More information

LESSON THIRTEEN STRUCTURAL AMBIGUITY. Structural ambiguity is also referred to as syntactic ambiguity or grammatical ambiguity.

LESSON THIRTEEN STRUCTURAL AMBIGUITY. Structural ambiguity is also referred to as syntactic ambiguity or grammatical ambiguity. LESSON THIRTEEN STRUCTURAL AMBIGUITY Structural ambiguity is also referred to as syntactic ambiguity or grammatical ambiguity. Structural or syntactic ambiguity, occurs when a phrase, clause or sentence

More information

Paraphrasing controlled English texts

Paraphrasing controlled English texts Paraphrasing controlled English texts Kaarel Kaljurand Institute of Computational Linguistics, University of Zurich kaljurand@gmail.com Abstract. We discuss paraphrasing controlled English texts, by defining

More information

A. The answer as per this document is No, it cannot exist keeping all distances rational.

A. The answer as per this document is No, it cannot exist keeping all distances rational. Rational Distance Conor.williams@gmail.com www.unsolvedproblems.org: Q. Given a unit square, can you find any point in the same plane, either inside or outside the square, that is a rational distance from

More information

Vieta s Formulas and the Identity Theorem

Vieta s Formulas and the Identity Theorem Vieta s Formulas and the Identity Theorem This worksheet will work through the material from our class on 3/21/2013 with some examples that should help you with the homework The topic of our discussion

More information

Lecture 17 : Equivalence and Order Relations DRAFT

Lecture 17 : Equivalence and Order Relations DRAFT CS/Math 240: Introduction to Discrete Mathematics 3/31/2011 Lecture 17 : Equivalence and Order Relations Instructor: Dieter van Melkebeek Scribe: Dalibor Zelený DRAFT Last lecture we introduced the notion

More information

God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886)

God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886) Chapter 2 Numbers God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886) God created the integers and the rest is the work

More information

The Book of Grammar Lesson Six. Mr. McBride AP Language and Composition

The Book of Grammar Lesson Six. Mr. McBride AP Language and Composition The Book of Grammar Lesson Six Mr. McBride AP Language and Composition Table of Contents Lesson One: Prepositions and Prepositional Phrases Lesson Two: The Function of Nouns in a Sentence Lesson Three:

More information

Am I Decisive? Handout for Government 317, Cornell University, Fall 2003. Walter Mebane

Am I Decisive? Handout for Government 317, Cornell University, Fall 2003. Walter Mebane Am I Decisive? Handout for Government 317, Cornell University, Fall 2003 Walter Mebane I compute the probability that one s vote is decisive in a maority-rule election between two candidates. Here, a decisive

More information

Regression Verification: Status Report

Regression Verification: Status Report Regression Verification: Status Report Presentation by Dennis Felsing within the Projektgruppe Formale Methoden der Softwareentwicklung 2013-12-11 1/22 Introduction How to prevent regressions in software

More information

Get Ready for IELTS Writing. About Get Ready for IELTS Writing. Part 1: Language development. Part 2: Skills development. Part 3: Exam practice

Get Ready for IELTS Writing. About Get Ready for IELTS Writing. Part 1: Language development. Part 2: Skills development. Part 3: Exam practice About Collins Get Ready for IELTS series has been designed to help learners at a pre-intermediate level (equivalent to band 3 or 4) to acquire the skills they need to achieve a higher score. It is easy

More information

6.1 Standard Form, Mood, and Figure

6.1 Standard Form, Mood, and Figure 6.1 Standard Form, Mood, and Figure Definition: A syllogism is an argument with two premises and a conclusion. Definition: A categorical syllogism is a syllogism whose premises and conclusion are all categorical

More information

Simplifying Algebraic Fractions

Simplifying Algebraic Fractions 5. Simplifying Algebraic Fractions 5. OBJECTIVES. Find the GCF for two monomials and simplify a fraction 2. Find the GCF for two polynomials and simplify a fraction Much of our work with algebraic fractions

More information

each college c i C has a capacity q i - the maximum number of students it will admit

each college c i C has a capacity q i - the maximum number of students it will admit n colleges in a set C, m applicants in a set A, where m is much larger than n. each college c i C has a capacity q i - the maximum number of students it will admit each college c i has a strict order i

More information

Math 223 Abstract Algebra Lecture Notes

Math 223 Abstract Algebra Lecture Notes Math 223 Abstract Algebra Lecture Notes Steven Tschantz Spring 2001 (Apr. 23 version) Preamble These notes are intended to supplement the lectures and make up for the lack of a textbook for the course

More information

Handout #1: Mathematical Reasoning

Handout #1: Mathematical Reasoning Math 101 Rumbos Spring 2010 1 Handout #1: Mathematical Reasoning 1 Propositional Logic A proposition is a mathematical statement that it is either true or false; that is, a statement whose certainty or

More information

Turing Degrees and Definability of the Jump. Theodore A. Slaman. University of California, Berkeley. CJuly, 2005

Turing Degrees and Definability of the Jump. Theodore A. Slaman. University of California, Berkeley. CJuly, 2005 Turing Degrees and Definability of the Jump Theodore A. Slaman University of California, Berkeley CJuly, 2005 Outline Lecture 1 Forcing in arithmetic Coding and decoding theorems Automorphisms of countable

More information

Ling 201 Syntax 1. Jirka Hana April 10, 2006

Ling 201 Syntax 1. Jirka Hana April 10, 2006 Overview of topics What is Syntax? Word Classes What to remember and understand: Ling 201 Syntax 1 Jirka Hana April 10, 2006 Syntax, difference between syntax and semantics, open/closed class words, all

More information

Continued Fractions and the Euclidean Algorithm

Continued Fractions and the Euclidean Algorithm Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction

More information

E3: PROBABILITY AND STATISTICS lecture notes

E3: PROBABILITY AND STATISTICS lecture notes E3: PROBABILITY AND STATISTICS lecture notes 2 Contents 1 PROBABILITY THEORY 7 1.1 Experiments and random events............................ 7 1.2 Certain event. Impossible event............................

More information

Handout NUMBER THEORY

Handout NUMBER THEORY Handout of NUMBER THEORY by Kus Prihantoso Krisnawan MATHEMATICS DEPARTMENT FACULTY OF MATHEMATICS AND NATURAL SCIENCES YOGYAKARTA STATE UNIVERSITY 2012 Contents Contents i 1 Some Preliminary Considerations

More information

Predicate Logic Review

Predicate Logic Review Predicate Logic Review UC Berkeley, Philosophy 142, Spring 2016 John MacFarlane 1 Grammar A term is an individual constant or a variable. An individual constant is a lowercase letter from the beginning

More information

Formal Languages and Automata Theory - Regular Expressions and Finite Automata -

Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Samarjit Chakraborty Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology (ETH) Zürich March

More information

Mathematical Induction

Mathematical Induction Mathematical Induction In logic, we often want to prove that every member of an infinite set has some feature. E.g., we would like to show: N 1 : is a number 1 : has the feature Φ ( x)(n 1 x! 1 x) How

More information

4.2 Euclid s Classification of Pythagorean Triples

4.2 Euclid s Classification of Pythagorean Triples 178 4. Number Theory: Fermat s Last Theorem Exercise 4.7: A primitive Pythagorean triple is one in which any two of the three numbers are relatively prime. Show that every multiple of a Pythagorean triple

More information

CHAPTER 5. Number Theory. 1. Integers and Division. Discussion

CHAPTER 5. Number Theory. 1. Integers and Division. Discussion CHAPTER 5 Number Theory 1. Integers and Division 1.1. Divisibility. Definition 1.1.1. Given two integers a and b we say a divides b if there is an integer c such that b = ac. If a divides b, we write a

More information

Comparatives, Superlatives, Diminutives

Comparatives, Superlatives, Diminutives Comparatives, Superlatives, Diminutives Finally in this lesson we are going to look at Comparatives, i.e. how you compare one thing with an other; Superlatives, how you say something is the most; and Diminutives

More information

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES I GROUPS: BASIC DEFINITIONS AND EXAMPLES Definition 1: An operation on a set G is a function : G G G Definition 2: A group is a set G which is equipped with an operation and a special element e G, called

More information

CMPSCI 250: Introduction to Computation. Lecture #19: Regular Expressions and Their Languages David Mix Barrington 11 April 2013

CMPSCI 250: Introduction to Computation. Lecture #19: Regular Expressions and Their Languages David Mix Barrington 11 April 2013 CMPSCI 250: Introduction to Computation Lecture #19: Regular Expressions and Their Languages David Mix Barrington 11 April 2013 Regular Expressions and Their Languages Alphabets, Strings and Languages

More information

Follow links for Class Use and other Permissions. For more information send email to: permissions@pupress.princeton.edu

Follow links for Class Use and other Permissions. For more information send email to: permissions@pupress.princeton.edu COPYRIGHT NOTICE: Ariel Rubinstein: Lecture Notes in Microeconomic Theory is published by Princeton University Press and copyrighted, c 2006, by Princeton University Press. All rights reserved. No part

More information

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +

More information

Set Theory Basic Concepts and Definitions

Set Theory Basic Concepts and Definitions Set Theory Basic Concepts and Definitions The Importance of Set Theory One striking feature of humans is their inherent need and ability to group objects according to specific criteria. Our prehistoric

More information

x < y iff x < y, or x and y are incomparable and x χ(x,y) < y χ(x,y).

x < y iff x < y, or x and y are incomparable and x χ(x,y) < y χ(x,y). 12. Large cardinals The study, or use, of large cardinals is one of the most active areas of research in set theory currently. There are many provably different kinds of large cardinals whose descriptions

More information

COUNTING SUBSETS OF A SET: COMBINATIONS

COUNTING SUBSETS OF A SET: COMBINATIONS COUNTING SUBSETS OF A SET: COMBINATIONS DEFINITION 1: Let n, r be nonnegative integers with r n. An r-combination of a set of n elements is a subset of r of the n elements. EXAMPLE 1: Let S {a, b, c, d}.

More information

Continued Fractions. Darren C. Collins

Continued Fractions. Darren C. Collins Continued Fractions Darren C Collins Abstract In this paper, we discuss continued fractions First, we discuss the definition and notation Second, we discuss the development of the subject throughout history

More information

Parametric Domain-theoretic models of Linear Abadi & Plotkin Logic

Parametric Domain-theoretic models of Linear Abadi & Plotkin Logic Parametric Domain-theoretic models of Linear Abadi & Plotkin Logic Lars Birkedal Rasmus Ejlers Møgelberg Rasmus Lerchedahl Petersen IT University Technical Report Series TR-00-7 ISSN 600 600 February 00

More information

Morphology. Morphology is the study of word formation, of the structure of words. 1. some words can be divided into parts which still have meaning

Morphology. Morphology is the study of word formation, of the structure of words. 1. some words can be divided into parts which still have meaning Morphology Morphology is the study of word formation, of the structure of words. Some observations about words and their structure: 1. some words can be divided into parts which still have meaning 2. many

More information

Basic Probability Concepts

Basic Probability Concepts page 1 Chapter 1 Basic Probability Concepts 1.1 Sample and Event Spaces 1.1.1 Sample Space A probabilistic (or statistical) experiment has the following characteristics: (a) the set of all possible outcomes

More information

Lecture 8: Resolution theorem-proving

Lecture 8: Resolution theorem-proving Comp24412 Symbolic AI Lecture 8: Resolution theorem-proving Ian Pratt-Hartmann Room KB2.38: email: ipratt@cs.man.ac.uk 2014 15 In the previous Lecture, we met SATCHMO, a first-order theorem-prover implemented

More information

ON FUNCTIONAL SYMBOL-FREE LOGIC PROGRAMS

ON FUNCTIONAL SYMBOL-FREE LOGIC PROGRAMS PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical and Mathematical Sciences 2012 1 p. 43 48 ON FUNCTIONAL SYMBOL-FREE LOGIC PROGRAMS I nf or m at i cs L. A. HAYKAZYAN * Chair of Programming and Information

More information

4. FIRST STEPS IN THE THEORY 4.1. A

4. FIRST STEPS IN THE THEORY 4.1. A 4. FIRST STEPS IN THE THEORY 4.1. A Catalogue of All Groups: The Impossible Dream The fundamental problem of group theory is to systematically explore the landscape and to chart what lies out there. We

More information

DEFINABLE TYPES IN PRESBURGER ARITHMETIC

DEFINABLE TYPES IN PRESBURGER ARITHMETIC DEFINABLE TYPES IN PRESBURGER ARITHMETIC GABRIEL CONANT Abstract. We consider the first order theory of (Z, +,

More information

v w is orthogonal to both v and w. the three vectors v, w and v w form a right-handed set of vectors.

v w is orthogonal to both v and w. the three vectors v, w and v w form a right-handed set of vectors. 3. Cross product Definition 3.1. Let v and w be two vectors in R 3. The cross product of v and w, denoted v w, is the vector defined as follows: the length of v w is the area of the parallelogram with

More information

Determination of the normalization level of database schemas through equivalence classes of attributes

Determination of the normalization level of database schemas through equivalence classes of attributes Computer Science Journal of Moldova, vol.17, no.2(50), 2009 Determination of the normalization level of database schemas through equivalence classes of attributes Cotelea Vitalie Abstract In this paper,

More information

2. The Language of First-order Logic

2. The Language of First-order Logic 2. The Language of First-order Logic KR & R Brachman & Levesque 2005 17 Declarative language Before building system before there can be learning, reasoning, planning, explanation... need to be able to

More information

Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm.

Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm. Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm. We begin by defining the ring of polynomials with coefficients in a ring R. After some preliminary results, we specialize

More information