1.5 Rules of Inference

Size: px
Start display at page:

Download "1.5 Rules of Inference"

Transcription

1 1.5 Rules of Inference (Inference: decision/conclusion by evidence/reasoning) Introduction Proofs are valid arguments that establish the truth of statements. An argument is a sequence of statements that end with a conclusion. Valid means that the conclusion must follow from the truth of the preceding statements, or premises, of the argument. An argument is valid if and only if it is impossible for all the premises to be true and the conclusion to be false. Rules of inference are the basic tools for establishing the truth of statements. Valid Arguments in Propositional Logic Consider the following argument involving propositions (a sequence of propositions): If you have a current password, then you can log onto the network. You have a current password. Therefore, You can log onto the network. Let p be You have a current password and q be You can log onto the network. Then the argument has the form p q p q where denotes therefore. Clearly if (p q) p=t, then q=t. We say this form of argument is valid because if all premises are true, the conclusion must also be true. This argument is valid because its form is valid. If not all premises are true, then it is not possible to conclude that the conclusion is true. We changed propositions to propositional variables and an argument to an argument form. We saw that the validity of an argument follows from the validity of the form of the argument. Definition 1 An argument in propositional logic is a sequence of proposition. All but the final proposition in the argument are called premises and the final proposition is called the conclusion. An argument is valid if the truth of all its premises 1

2 implies that the conclusion is true. An argument form in propositional logic is a sequence of compound propositions involving propositional variables. An argument form is valid if no matter which particular proposition is submitted for the propositional variables in its premises, the conclusion is true if the premises are all true. Rules of Inference for Propositional Logic We can always use truth table to show that an argument form is valid. We do this by showing that whenever the premises are true, the conclusion must also be true. However this can be a tedious approach. Instead we can first establish the validity of some relatively simple argument forms, called rules of inference. These can be used as building blocks to construct more complicated valid argument forms. The tautology ((p q) p) q is the basis of the rule of inference called modus ponens, or the law of detachment. This tautology leads to the following valid argument form. p q p q In particular, modus ponens tells us that if a conditional statement and the hypothesis of this conditional statement are both true, then the conclusion must be true. Example 1: Suppose that the conditional statement If it snows today, then we will go skiing and its hypothesis, It is snowing today are true. Then, by modus ponens, it follows that the conclusion of the conditional statement, We will go skiing, is true. A valid argument can lead to an incorrect conclusion if one or more of its premises is false. Example 2: Determine whether the argument given here is valid and determine whether its conclusion must be true because of the validity of the argument. If 2> 3/2, then 2> 9/4. We know that 2> 3/2. Consequently, 2> 9/4. 2

3 Sol: Let p be 2> 3/2 and q be 2> 9/4. The premises of the argument are p q and p, and q is its conclusion. The argument is valid because it is constructed by using modus ponens, a valid argument form. But 2> 3/2 is false, so we cannot conclude that the conclusion is true. Furthermore, note that the conclusion of this argument is false, because 2 <9/4. Table 1. Example 3 State which rule of inference is the basis of the following argument: It is below freezing now. Therefore, it is either below freezing or raining now. Sol: Let p be It is below freezing now and q be It is raining now. Then the argument is of the form p p q Thus it is addition. Example 4 State which rule of inference is the basis of the following argument: It is below freezing and raining now. Therefore, it is below freezing now. Sol: Let p be It is below freezing now and q be It is raining now. Then the argument is of the form p q p This argument uses the simplification rule. Example 5 State which rule of inference is used in the argument: If it rains today, then we will not have a barbecue today. If we do not have a barbecue today, then we will not have a barbecue tomorrow. Therefore, if it rains today, then we will not have a barbecue tomorrow. Sol: Let p be It is raining today, q be We will not have a barbecue today and r be We will not have a barbecue tomorrow. Then the argument is of the form p q 3

4 p r p r So this argument is a hypothetical syllogism. Using Rules of Inference to Build Arguments Remark: Conclusion of a sequence is valid/true if argument form is valid and all premises are true. When there are many premises, several rules of inference are needed to show that an argument is valid. Example 6: Show that the hypotheses It is not sunny this afternoon and it is colder than yesterday, We will go swimming only if it is sunny this afternoon, If we do not go swimming, then we will take a canoe trip, and If we take a canoe trip, then we will be at home by sunset lead to the conclusion We will be home by sunset. p: It is sunny this afternoon. q: It is colder than yesterday. r: We will go swimming. s: We will take a canoe trip. t: We will be at home by sunset. First we translate the statements into logical expressions and so we have the hypotheses: p q r p r s s t and conclusion t. We want to get that the conclusion t is true. Trick: tail to the head and back. t is true if s t is true and s is true modus ponens s is true if r s is true and r is true modus ponens/disjunctive syllogism 4

5 r is true if r p is true and p is true modus tollens p is true if p q is true simplification Now back (No mystery!): 1. ( p q) p 2. [ p (r p)] r 3. ( r s) r=(r s) r, so [(r s) r] s 4. [(s t) s] t. Example 7: Show that the hypotheses If you send me an message, then I will finish writing the program, If you do not send me an message, then I will go to sleep early, and If I go to sleep early, then I will wake up feeling refreshed lead to the conclusion If I do not finish writing the program, then I will wake up feeling refreshed. p: You send me an message q: I will finish writing the program r: I will go to sleep early s: I will wake up feeling refreshed. Then the hypotheses are p q, p r, r s and the conclusion is q s. The treatment is similar to Example 6. Just looking at q s and p r, r s, we see that by Hypothetical syllogism we have p s from p r and r s. Now we have p q and p s. So from p q we have q p. Combining q p with p s again by the Hypothetical syllogism we have q s. Now going forward: 1. p q Hypothesis 2. q p Contrapositive of (1) 3. p r Hypothesis 4. q r Hypothetical Syllogism of (2)&(3) 5. r s Hypothesis 6. q s Hypothetical Syllogism of (4)&(5) 5

6 Resolution Computer programs have been developed to automate the task of reasoning and proving theorems. Many of these rules make use of a rule of inference known as resolution. It is based on the tautology ((p q) ( p r)) (q r). Example 8: Use resolution to show that the hypotheses Jasmine is skiing or it is not snowing and It is snowing or Bart playing hockey imply Jasmine is skiing or Bart is playing hockey. p: Jasmine is skiing, q: It is snowing, r: Bart playing hockey. So we have the hypotheses: p q and q r. Applying resolution p r follows. The other way: (p q) (q r) = (q r) (p q) =( r q) (q p). Applying Hypotheses syllogism, from ( r q) (q p) follows ( r p). Evidently ( r p) = (r p). Example 9: Show that the hypotheses (p q) r and r s imply the conclusion p s. Solution: Clearly [(p q) r] (r s)= [ (p q) r] (r s). Applying Hypotheses syllogism, from [ (p q) r] (r s) follows (p q) s. But (p q) s=(p q) s. Applying the distributive law (p q) s =(p s) (q s) and simplification from (p s) (q s) follows (p s). Fallacies Several common fallacies arise in incorrect arguments. These fallacies resemble rules of inference but are based on contingencies rather than tautologies. This type of reasoning is called fallacy of affirming the conclusion. Example 10: Show if the following argument is valid? If you do every problem in this book, then you will learn discrete mathematics. You learned discrete mathematics. Therefore, you did every problem in this book. p: You did every problem in this book, q: You learned discrete mathematics. 6

7 So the argument can be expressed as: [(p q) q] p. But [(p q) q] p is not a tautology, it is false if p=f and q=t. Therefore the reasoning is not correct. This type incorrect reasoning is called the fallacy of denying the hypotheses. Example 11: Let p and q be as in Example 10. If (p q) p =T, is it correct to conclude that q=t? Solution: We check if [(p q) p] q is a tautology. It is not (check for p=f, q=t). So it is not correct to conclude that q=t. This type incorrect reasoning is called the fallacy of denying the hypotheses. Rules of Inference for Quantified Statements Now we will describe some rules of inference for statements involving quantifiers. Universal instantiation is the rule of inference to conclude that P(c)=T, where c D, given the premise x P(x) true. Example: All women are wise. Lisa is a woman. Therefore Lisa is wise. Universal generalization is the rule of inference that states that x P(x) is true, given the premise P(c) is true for all c D. Existential instantiation is the rule of inference that is used to conclude that there is a c D for which P(c)=T, given the premise x P(x) true. Existential generalization is the rule of inference that is used to conclude that x P(x) true when a particular c with P(c) true is given. TABLE 2 Example 12: Show that the premises Everyone in this discrete mathematics class has taken a course in computer science and Marla is a student in this class imply the conclusion Marla has taken a course in computer science. D(x): x is in this discrete mathematics class, C(x): x has taken a course in computer science. 7

8 Then the premises are x(d(x) C(x)) and D(Marla). The conclusion is C(Marla). 1. x(d(x) C(x)) Premise 2. D(Marla) C(Marla) Universal instantiation from (1) 3. D(Marla) Premise 4. C(Marla) Modus ponens from (2) and (3) Example 13: Show that the premises A student in this class has not read the book, and Everyone in this class passed the first exam imply the conclusion Someone who passed the first exam has not read the book. C(x): x is in this class B(x): x has read the book P(x): x passed the first exam. The premises are: x (C(x) B(x)) and x(c(x) P(x)). The conclusion is x (P(x) B(x)). The steps can be used to get the conclusion from the premises: 1. x (C(x) B(x)) Premise 2. (C(a) B(a)) Existential instantiation from (1) 3. C(a) Simplification from(2) 4. x(c(x) P(x)) Premise 5. (C(a) P(a)) Universal instantiation from (4) 6. P(a) Modus ponens from (3) and (5) 7. B(a) Simplification from (2) 8. P(a) B(a) Conjunction from (6) and (7) 9. x (P(x) B(x)) Existential generalization from (8) Rules of Inference for Quantified Statements We often need to use combination of rules. Example 14: Assume that For all positive integer n, if n is greater than 4, then n^2 is less than 2^n is true. Show that 100^2<2^100. 8

9 P(n): n>4 Q(n): n^2<2^n. So the statement can be represented as n(p(n) Q(n)), where D=N. We assume that n(p(n) Q(n)) is true. 1. P(100) Premise 2. n(p(n) Q(n)) Premise 3. P(100) Q(100)) Universal instantiation from(2) 4. Q(100) Modus ponens from (1) and (3). 9

Rules of Inference Friday, January 18, 2013 Chittu Tripathy Lecture 05

Rules of Inference Friday, January 18, 2013 Chittu Tripathy Lecture 05 Rules of Inference Today s Menu Rules of Inference Quantifiers: Universal and Existential Nesting of Quantifiers Applications Old Example Re-Revisited Our Old Example: Suppose we have: All human beings

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

def: An axiom is a statement that is assumed to be true, or in the case of a mathematical system, is used to specify the system.

def: An axiom is a statement that is assumed to be true, or in the case of a mathematical system, is used to specify the system. Section 1.5 Methods of Proof 1.5.1 1.5 METHODS OF PROOF Some forms of argument ( valid ) never lead from correct statements to an incorrect. Some other forms of argument ( fallacies ) can lead from true

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

DISCRETE MATH: LECTURE 3

DISCRETE MATH: LECTURE 3 DISCRETE MATH: LECTURE 3 DR. DANIEL FREEMAN 1. Chapter 2.2 Conditional Statements If p and q are statement variables, the conditional of q by p is If p then q or p implies q and is denoted p q. It is false

More information

Predicate Logic. Example: All men are mortal. Socrates is a man. Socrates is mortal.

Predicate Logic. Example: All men are mortal. Socrates is a man. Socrates is mortal. Predicate Logic Example: All men are mortal. Socrates is a man. Socrates is mortal. Note: We need logic laws that work for statements involving quantities like some and all. In English, the predicate is

More information

Likewise, we have contradictions: formulas that can only be false, e.g. (p p).

Likewise, we have contradictions: formulas that can only be false, e.g. (p p). CHAPTER 4. STATEMENT LOGIC 59 The rightmost column of this truth table contains instances of T and instances of F. Notice that there are no degrees of contingency. If both values are possible, the formula

More information

Chapter 1. Use the following to answer questions 1-5: In the questions below determine whether the proposition is TRUE or FALSE

Chapter 1. Use the following to answer questions 1-5: In the questions below determine whether the proposition is TRUE or FALSE Use the following to answer questions 1-5: Chapter 1 In the questions below determine whether the proposition is TRUE or FALSE 1. 1 + 1 = 3 if and only if 2 + 2 = 3. 2. If it is raining, then it is raining.

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

Propositional Logic. A proposition is a declarative sentence (a sentence that declares a fact) that is either true or false, but not both.

Propositional Logic. A proposition is a declarative sentence (a sentence that declares a fact) that is either true or false, but not both. irst Order Logic Propositional Logic A proposition is a declarative sentence (a sentence that declares a fact) that is either true or false, but not both. Are the following sentences propositions? oronto

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

Math 319 Problem Set #3 Solution 21 February 2002

Math 319 Problem Set #3 Solution 21 February 2002 Math 319 Problem Set #3 Solution 21 February 2002 1. ( 2.1, problem 15) Find integers a 1, a 2, a 3, a 4, a 5 such that every integer x satisfies at least one of the congruences x a 1 (mod 2), x a 2 (mod

More information

Hypothetical Syllogisms 1

Hypothetical Syllogisms 1 Phil 2302 Intro to Logic Dr. Naugle Hypothetical Syllogisms 1 Compound syllogisms are composed of different kinds of sentences in their premises and conclusions (not just categorical propositions, statements

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

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

Beyond Propositional Logic Lukasiewicz s System

Beyond Propositional Logic Lukasiewicz s System Beyond Propositional Logic Lukasiewicz s System Consider the following set of truth tables: 1 0 0 1 # # 1 0 # 1 1 0 # 0 0 0 0 # # 0 # 1 0 # 1 1 1 1 0 1 0 # # 1 # # 1 0 # 1 1 0 # 0 1 1 1 # 1 # 1 Brandon

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

Lecture Notes in Discrete Mathematics. Marcel B. Finan Arkansas Tech University c All Rights Reserved

Lecture Notes in Discrete Mathematics. Marcel B. Finan Arkansas Tech University c All Rights Reserved Lecture Notes in Discrete Mathematics Marcel B. Finan Arkansas Tech University c All Rights Reserved 2 Preface This book is designed for a one semester course in discrete mathematics for sophomore or junior

More information

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson Mathematics for Computer Science/Software Engineering Notes for the course MSM1F3 Dr. R. A. Wilson October 1996 Chapter 1 Logic Lecture no. 1. We introduce the concept of a proposition, which is a statement

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

WOLLONGONG COLLEGE AUSTRALIA. Diploma in Information Technology

WOLLONGONG COLLEGE AUSTRALIA. Diploma in Information Technology First Name: Family Name: Student Number: Class/Tutorial: WOLLONGONG COLLEGE AUSTRALIA A College of the University of Wollongong Diploma in Information Technology Mid-Session Test Summer Session 008-00

More information

1.2 Forms and Validity

1.2 Forms and Validity 1.2 Forms and Validity Deductive Logic is the study of methods for determining whether or not an argument is valid. In this section we identify some famous valid argument forms. Argument Forms Consider

More information

Logic Appendix. Section 1 Truth Tables CONJUNCTION EXAMPLE 1

Logic Appendix. Section 1 Truth Tables CONJUNCTION EXAMPLE 1 Logic Appendix T F F T Section 1 Truth Tables Recall that a statement is a group of words or symbols that can be classified collectively as true or false. The claim 5 7 12 is a true statement, whereas

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

The last three chapters introduced three major proof techniques: direct,

The last three chapters introduced three major proof techniques: direct, CHAPTER 7 Proving Non-Conditional Statements The last three chapters introduced three major proof techniques: direct, contrapositive and contradiction. These three techniques are used to prove statements

More information

The Mathematics of GIS. Wolfgang Kainz

The Mathematics of GIS. Wolfgang Kainz The Mathematics of GIS Wolfgang Kainz Wolfgang Kainz Department of Geography and Regional Research University of Vienna Universitätsstraße 7, A-00 Vienna, Austria E-Mail: wolfgang.kainz@univie.ac.at Version.

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

DEDUCTIVE & INDUCTIVE REASONING

DEDUCTIVE & INDUCTIVE REASONING DEDUCTIVE & INDUCTIVE REASONING Expectations 1. Take notes on inductive and deductive reasoning. 2. This is an information based presentation -- I simply want you to be able to apply this information to

More information

Basic Proof Techniques

Basic Proof Techniques Basic Proof Techniques David Ferry dsf43@truman.edu September 13, 010 1 Four Fundamental Proof Techniques When one wishes to prove the statement P Q there are four fundamental approaches. This document

More information

Solutions for Practice problems on proofs

Solutions for Practice problems on proofs Solutions for Practice problems on proofs Definition: (even) An integer n Z is even if and only if n = 2m for some number m Z. Definition: (odd) An integer n Z is odd if and only if n = 2m + 1 for some

More information

p: I am elected q: I will lower the taxes

p: I am elected q: I will lower the taxes Implication Conditional Statement p q (p implies q) (if p then q) is the proposition that is false when p is true and q is false and true otherwise. Equivalent to not p or q Ex. If I am elected then I

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

6.080/6.089 GITCS Feb 12, 2008. Lecture 3

6.080/6.089 GITCS Feb 12, 2008. Lecture 3 6.8/6.89 GITCS Feb 2, 28 Lecturer: Scott Aaronson Lecture 3 Scribe: Adam Rogal Administrivia. Scribe notes The purpose of scribe notes is to transcribe our lectures. Although I have formal notes of my

More information

CHAPTER 2. Logic. 1. Logic Definitions. Notation: Variables are used to represent propositions. The most common variables used are p, q, and r.

CHAPTER 2. Logic. 1. Logic Definitions. Notation: Variables are used to represent propositions. The most common variables used are p, q, and r. CHAPTER 2 Logic 1. Logic Definitions 1.1. Propositions. Definition 1.1.1. A proposition is a declarative sentence that is either true (denoted either T or 1) or false (denoted either F or 0). Notation:

More information

Section 6.4: Counting Subsets of a Set: Combinations

Section 6.4: Counting Subsets of a Set: Combinations Section 6.4: Counting Subsets of a Set: Combinations In section 6.2, we learnt how to count the number of r-permutations from an n-element set (recall that an r-permutation is an ordered selection of r

More information

Logic and Reasoning Practice Final Exam Spring 2015. Section Number

Logic and Reasoning Practice Final Exam Spring 2015. Section Number Logic and Reasoning Practice Final Exam Spring 2015 Name Section Number The final examination is worth 100 points. 1. (5 points) What is an argument? Explain what is meant when one says that logic is the

More information

Math 3000 Section 003 Intro to Abstract Math Homework 2

Math 3000 Section 003 Intro to Abstract Math Homework 2 Math 3000 Section 003 Intro to Abstract Math Homework 2 Department of Mathematical and Statistical Sciences University of Colorado Denver, Spring 2012 Solutions (February 13, 2012) Please note that these

More information

Solutions to Homework 6 Mathematics 503 Foundations of Mathematics Spring 2014

Solutions to Homework 6 Mathematics 503 Foundations of Mathematics Spring 2014 Solutions to Homework 6 Mathematics 503 Foundations of Mathematics Spring 2014 3.4: 1. If m is any integer, then m(m + 1) = m 2 + m is the product of m and its successor. That it to say, m 2 + m is the

More information

Invalidity in Predicate Logic

Invalidity in Predicate Logic Invalidity in Predicate Logic So far we ve got a method for establishing that a predicate logic argument is valid: do a derivation. But we ve got no method for establishing invalidity. In propositional

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

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

Reasoning and Proof Review Questions

Reasoning and Proof Review Questions www.ck12.org 1 Reasoning and Proof Review Questions Inductive Reasoning from Patterns 1. What is the next term in the pattern: 1, 4, 9, 16, 25, 36, 49...? (a) 81 (b) 64 (c) 121 (d) 56 2. What is the next

More information

CSL105: Discrete Mathematical Structures. Ragesh Jaiswal, CSE, IIT Delhi

CSL105: Discrete Mathematical Structures. Ragesh Jaiswal, CSE, IIT Delhi Propositional Logic: logical operators Negation ( ) Conjunction ( ) Disjunction ( ). Exclusive or ( ) Conditional statement ( ) Bi-conditional statement ( ): Let p and q be propositions. The biconditional

More information

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

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

Math 55: Discrete Mathematics

Math 55: Discrete Mathematics Math 55: Discrete Mathematics UC Berkeley, Fall 2011 Homework # 5, due Wednesday, February 22 5.1.4 Let P (n) be the statement that 1 3 + 2 3 + + n 3 = (n(n + 1)/2) 2 for the positive integer n. a) What

More information

WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT?

WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT? WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT? introduction Many students seem to have trouble with the notion of a mathematical proof. People that come to a course like Math 216, who certainly

More information

WOLLONGONG COLLEGE AUSTRALIA. Diploma in Information Technology

WOLLONGONG COLLEGE AUSTRALIA. Diploma in Information Technology First Name: Family Name: Student Number: Class/Tutorial: WOLLONGONG COLLEGE AUSTRALIA A College of the University of Wollongong Diploma in Information Technology Final Examination Spring Session 2008 WUCT121

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

A Few Basics of Probability

A Few Basics of Probability A Few Basics of Probability Philosophy 57 Spring, 2004 1 Introduction This handout distinguishes between inductive and deductive logic, and then introduces probability, a concept essential to the study

More information

Examination paper for MA0301 Elementær diskret matematikk

Examination paper for MA0301 Elementær diskret matematikk Department of Mathematical Sciences Examination paper for MA0301 Elementær diskret matematikk Academic contact during examination: Iris Marjan Smit a, Sverre Olaf Smalø b Phone: a 9285 0781, b 7359 1750

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

Cosmological Arguments for the Existence of God S. Clarke

Cosmological Arguments for the Existence of God S. Clarke Cosmological Arguments for the Existence of God S. Clarke [Modified Fall 2009] 1. Large class of arguments. Sometimes they get very complex, as in Clarke s argument, but the basic idea is simple. Lets

More information

Quine on truth by convention

Quine on truth by convention Quine on truth by convention March 8, 2005 1 Linguistic explanations of necessity and the a priori.............. 1 2 Relative and absolute truth by definition.................... 2 3 Is logic true by convention?...........................

More information

4.5 Linear Dependence and Linear Independence

4.5 Linear Dependence and Linear Independence 4.5 Linear Dependence and Linear Independence 267 32. {v 1, v 2 }, where v 1, v 2 are collinear vectors in R 3. 33. Prove that if S and S are subsets of a vector space V such that S is a subset of S, then

More information

Elementary Number Theory and Methods of Proof. CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook.

Elementary Number Theory and Methods of Proof. CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook. Elementary Number Theory and Methods of Proof CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook.edu/~cse215 1 Number theory Properties: 2 Properties of integers (whole

More information

Logic is a systematic way of thinking that allows us to deduce new information

Logic is a systematic way of thinking that allows us to deduce new information CHAPTER 2 Logic Logic is a systematic way of thinking that allows us to deduce new information from old information and to parse the meanings of sentences. You use logic informally in everyday life and

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

Jaakko Hintikka Boston University. and. Ilpo Halonen University of Helsinki INTERPOLATION AS EXPLANATION

Jaakko Hintikka Boston University. and. Ilpo Halonen University of Helsinki INTERPOLATION AS EXPLANATION Jaakko Hintikka Boston University and Ilpo Halonen University of Helsinki INTERPOLATION AS EXPLANATION INTERPOLATION AS EXPLANATION In the study of explanation, one can distinguish two main trends. On

More information

SECTION 10-2 Mathematical Induction

SECTION 10-2 Mathematical Induction 73 0 Sequences and Series 6. Approximate e 0. using the first five terms of the series. Compare this approximation with your calculator evaluation of e 0.. 6. Approximate e 0.5 using the first five terms

More information

Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan

Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan 3 Binary Operations We are used to addition and multiplication of real numbers. These operations combine two real numbers

More information

Cardinality. The set of all finite strings over the alphabet of lowercase letters is countable. The set of real numbers R is an uncountable set.

Cardinality. The set of all finite strings over the alphabet of lowercase letters is countable. The set of real numbers R is an uncountable set. Section 2.5 Cardinality (another) Definition: The cardinality of a set A is equal to the cardinality of a set B, denoted A = B, if and only if there is a bijection from A to B. If there is an injection

More information

Notes on Continuous Random Variables

Notes on Continuous Random Variables Notes on Continuous Random Variables Continuous random variables are random quantities that are measured on a continuous scale. They can usually take on any value over some interval, which distinguishes

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

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

10.4 Traditional Subject Predicate Propositions

10.4 Traditional Subject Predicate Propositions M10_COPI1396_13_SE_C10.QXD 10/22/07 8:42 AM Page 445 10.4 Traditional Subject Predicate Propositions 445 Continuing to assume the existence of at least one individual, we can say, referring to this square,

More information

PHILOSOPHY 101: CRITICAL THINKING

PHILOSOPHY 101: CRITICAL THINKING PHILOSOPHY 101: CRITICAL THINKING [days and times] [classroom] [semester] 20YY, [campus] [instructor s name] [office hours: days and times] [instructor s e-mail] COURSE OBJECTIVES AND OUTCOMES 1. Identify

More information

Reading 13 : Finite State Automata and Regular Expressions

Reading 13 : Finite State Automata and Regular Expressions CS/Math 24: Introduction to Discrete Mathematics Fall 25 Reading 3 : Finite State Automata and Regular Expressions Instructors: Beck Hasti, Gautam Prakriya In this reading we study a mathematical model

More information

1. Prove that the empty set is a subset of every set.

1. Prove that the empty set is a subset of every set. 1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: b89902089@ntu.edu.tw Proof: For any element x of the empty set, x is also an element of every set since

More information

Dedekind s forgotten axiom and why we should teach it (and why we shouldn t teach mathematical induction in our calculus classes)

Dedekind s forgotten axiom and why we should teach it (and why we shouldn t teach mathematical induction in our calculus classes) Dedekind s forgotten axiom and why we should teach it (and why we shouldn t teach mathematical induction in our calculus classes) by Jim Propp (UMass Lowell) March 14, 2010 1 / 29 Completeness Three common

More information

Mathematical Induction. Mary Barnes Sue Gordon

Mathematical Induction. Mary Barnes Sue Gordon Mathematics Learning Centre Mathematical Induction Mary Barnes Sue Gordon c 1987 University of Sydney Contents 1 Mathematical Induction 1 1.1 Why do we need proof by induction?.... 1 1. What is proof by

More information

Lecture 2: Moral Reasoning & Evaluating Ethical Theories

Lecture 2: Moral Reasoning & Evaluating Ethical Theories Lecture 2: Moral Reasoning & Evaluating Ethical Theories I. Introduction In this ethics course, we are going to avoid divine command theory and various appeals to authority and put our trust in critical

More information

People have thought about, and defined, probability in different ways. important to note the consequences of the definition:

People have thought about, and defined, probability in different ways. important to note the consequences of the definition: PROBABILITY AND LIKELIHOOD, A BRIEF INTRODUCTION IN SUPPORT OF A COURSE ON MOLECULAR EVOLUTION (BIOL 3046) Probability The subject of PROBABILITY is a branch of mathematics dedicated to building models

More information

Midterm Practice Problems

Midterm Practice Problems 6.042/8.062J Mathematics for Computer Science October 2, 200 Tom Leighton, Marten van Dijk, and Brooke Cowan Midterm Practice Problems Problem. [0 points] In problem set you showed that the nand operator

More information

Read this syllabus very carefully. If there are any reasons why you cannot comply with what I am requiring, then talk with me about this at once.

Read this syllabus very carefully. If there are any reasons why you cannot comply with what I am requiring, then talk with me about this at once. LOGIC AND CRITICAL THINKING PHIL 2020 Maymester Term, 2010 Daily, 9:30-12:15 Peabody Hall, room 105 Text: LOGIC AND RATIONAL THOUGHT by Frank R. Harrison, III Professor: Frank R. Harrison, III Office:

More information

Bayesian Updating with Discrete Priors Class 11, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom

Bayesian Updating with Discrete Priors Class 11, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom 1 Learning Goals Bayesian Updating with Discrete Priors Class 11, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom 1. Be able to apply Bayes theorem to compute probabilities. 2. Be able to identify

More information

MATH 289 PROBLEM SET 4: NUMBER THEORY

MATH 289 PROBLEM SET 4: NUMBER THEORY MATH 289 PROBLEM SET 4: NUMBER THEORY 1. The greatest common divisor If d and n are integers, then we say that d divides n if and only if there exists an integer q such that n = qd. Notice that if d divides

More information

The Fundamental Theorem of Arithmetic

The Fundamental Theorem of Arithmetic The Fundamental Theorem of Arithmetic 1 Introduction: Why this theorem? Why this proof? One of the purposes of this course 1 is to train you in the methods mathematicians use to prove mathematical statements,

More information

6.2 Permutations continued

6.2 Permutations continued 6.2 Permutations continued Theorem A permutation on a finite set A is either a cycle or can be expressed as a product (composition of disjoint cycles. Proof is by (strong induction on the number, r, of

More information

About the inverse football pool problem for 9 games 1

About the inverse football pool problem for 9 games 1 Seventh International Workshop on Optimal Codes and Related Topics September 6-1, 013, Albena, Bulgaria pp. 15-133 About the inverse football pool problem for 9 games 1 Emil Kolev Tsonka Baicheva Institute

More information

Vocabulary. Term Page Definition Clarifying Example. biconditional statement. conclusion. conditional statement. conjecture.

Vocabulary. Term Page Definition Clarifying Example. biconditional statement. conclusion. conditional statement. conjecture. CHAPTER Vocabulary The table contains important vocabulary terms from Chapter. As you work through the chapter, fill in the page number, definition, and a clarifying example. biconditional statement conclusion

More information

One natural response would be to cite evidence of past mornings, and give something like the following argument:

One natural response would be to cite evidence of past mornings, and give something like the following argument: Hume on induction Suppose you were asked to give your reasons for believing that the sun will come up tomorrow, in the form of an argument for the claim that the sun will come up tomorrow. One natural

More information

Today s Topics. Primes & Greatest Common Divisors

Today s Topics. Primes & Greatest Common Divisors Today s Topics Primes & Greatest Common Divisors Prime representations Important theorems about primality Greatest Common Divisors Least Common Multiples Euclid s algorithm Once and for all, what are prime

More information

CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e.

CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. This chapter contains the beginnings of the most important, and probably the most subtle, notion in mathematical analysis, i.e.,

More information

Random variables P(X = 3) = P(X = 3) = 1 8, P(X = 1) = P(X = 1) = 3 8.

Random variables P(X = 3) = P(X = 3) = 1 8, P(X = 1) = P(X = 1) = 3 8. Random variables Remark on Notations 1. When X is a number chosen uniformly from a data set, What I call P(X = k) is called Freq[k, X] in the courseware. 2. When X is a random variable, what I call F ()

More information

APPENDIX 1 PROOFS IN MATHEMATICS. A1.1 Introduction 286 MATHEMATICS

APPENDIX 1 PROOFS IN MATHEMATICS. A1.1 Introduction 286 MATHEMATICS 286 MATHEMATICS APPENDIX 1 PROOFS IN MATHEMATICS A1.1 Introduction Suppose your family owns a plot of land and there is no fencing around it. Your neighbour decides one day to fence off his land. After

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

P1. All of the students will understand validity P2. You are one of the students -------------------- C. You will understand validity

P1. All of the students will understand validity P2. You are one of the students -------------------- C. You will understand validity Validity Philosophy 130 O Rourke I. The Data A. Here are examples of arguments that are valid: P1. If I am in my office, my lights are on P2. I am in my office C. My lights are on P1. He is either in class

More information

Stupid Divisibility Tricks

Stupid Divisibility Tricks Stupid Divisibility Tricks 101 Ways to Stupefy Your Friends Appeared in Math Horizons November, 2006 Marc Renault Shippensburg University Mathematics Department 1871 Old Main Road Shippensburg, PA 17013

More information

Every Positive Integer is the Sum of Four Squares! (and other exciting problems)

Every Positive Integer is the Sum of Four Squares! (and other exciting problems) Every Positive Integer is the Sum of Four Squares! (and other exciting problems) Sophex University of Texas at Austin October 18th, 00 Matilde N. Lalín 1. Lagrange s Theorem Theorem 1 Every positive integer

More information

Principle of Data Reduction

Principle of Data Reduction Chapter 6 Principle of Data Reduction 6.1 Introduction An experimenter uses the information in a sample X 1,..., X n to make inferences about an unknown parameter θ. If the sample size n is large, then

More information

Sample Induction Proofs

Sample Induction Proofs Math 3 Worksheet: Induction Proofs III, Sample Proofs A.J. Hildebrand Sample Induction Proofs Below are model solutions to some of the practice problems on the induction worksheets. The solutions given

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete

More information

Gödel s Ontological Proof of the Existence of God

Gödel s Ontological Proof of the Existence of God Prof. Dr. Elke Brendel Institut für Philosophie Lehrstuhl für Logik und Grundlagenforschung g Rheinische Friedrich-Wilhelms-Universität Bonn ebrendel@uni-bonn.de Gödel s Ontological Proof of the Existence

More information

Logic in general. Inference rules and theorem proving

Logic in general. Inference rules and theorem proving Logical Agents Knowledge-based agents Logic in general Propositional logic Inference rules and theorem proving First order logic Knowledge-based agents Inference engine Knowledge base Domain-independent

More information

Quotient Rings and Field Extensions

Quotient Rings and Field Extensions Chapter 5 Quotient Rings and Field Extensions In this chapter we describe a method for producing field extension of a given field. If F is a field, then a field extension is a field K that contains F.

More information

Row Echelon Form and Reduced Row Echelon Form

Row Echelon Form and Reduced Row Echelon Form These notes closely follow the presentation of the material given in David C Lay s textbook Linear Algebra and its Applications (3rd edition) These notes are intended primarily for in-class presentation

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

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

The sum of digits of polynomial values in arithmetic progressions

The sum of digits of polynomial values in arithmetic progressions The sum of digits of polynomial values in arithmetic progressions Thomas Stoll Institut de Mathématiques de Luminy, Université de la Méditerranée, 13288 Marseille Cedex 9, France E-mail: stoll@iml.univ-mrs.fr

More information