Methods of Proof for Quantifiers Chapter 12 Language, Proof and Logic.

Slides:



Advertisements
Similar presentations
Lecture 3 – February 17, 2003.
Advertisements

Discrete Math Methods of proof 1.
Introduction to Proofs
PROOF BY CONTRADICTION
Chapter 1: The Foundations: Logic and Proofs 1.1 Propositional Logic 1.2 Propositional Equivalences 1.3 Predicates and Quantifiers 1.4 Nested Quantifiers.
3.2 Pumping Lemma for Regular Languages Given a language L, how do we know whether it is regular or not? If we can construct an FA to accept the language.
Discussion #17 1/15 Discussion #17 Derivations. Discussion #17 2/15 Topics Derivations  proofs in predicate calculus Inference Rules with Quantifiers.
Logic and Proof. Argument An argument is a sequence of statements. All statements but the first one are called assumptions or hypothesis. The final statement.
First Order Logic (chapter 2 of the book) Lecture 3: Sep 14.
Proofs, Recursion and Analysis of Algorithms Mathematical Structures for Computer Science Chapter 2.1 Copyright © 2006 W.H. Freeman & Co.MSCS SlidesProofs,
Lecture , 3.1 Methods of Proof. Last time in 1.5 To prove theorems we use rules of inference such as: p, p  q, therefore, q NOT q, p  q, therefore.
Mathematical Induction
LIAL HORNSBY SCHNEIDER
First Order Logic. This Lecture Last time we talked about propositional logic, a logic on simple statements. This time we will talk about first order.
Section 1.3: Predicates and Quantifiers
CS 2210 (22C:019) Discrete Structures Logic and Proof Spring 2015 Sukumar Ghosh.
Methods of Proof & Proof Strategies
INTRODUCTION TO LOGIC FALL 2009 Quiz Game. ConceptsTrue/FalseTranslations Informal Proofs Formal Proofs
Introduction to Proofs
Advanced Topics in FOL Chapter 18 Language, Proof and Logic.
Discrete Mathematics, Part II CSE 2353 Fall 2007 Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist University Some slides.
1 Methods of Proof CS/APMA 202 Epp, chapter 3 Aaron Bloomfield.
Review I Rosen , 3.1 Know your definitions!
1 Methods of Proof. 2 Consider (p  (p→q)) → q pqp→q p  (p→q)) (p  (p→q)) → q TTTTT TFFFT FTTFT FFTFT.
1 Sections 1.5 & 3.1 Methods of Proof / Proof Strategy.
Chapter 5 Existence and Proof by contradiction
Copyright © 2013, 2009, 2005 Pearson Education, Inc. 1 3 Polynomial and Rational Functions Copyright © 2013, 2009, 2005 Pearson Education, Inc.
The Logic of Quantifiers Chapter 10 Language, Proof and Logic.
2.3Logical Implication: Rules of Inference From the notion of a valid argument, we begin a formal study of what we shall mean by an argument and when such.
First Order Logic Lecture 2: Sep 9. This Lecture Last time we talked about propositional logic, a logic on simple statements. This time we will talk about.
1 Introduction to Abstract Mathematics Chapter 2: The Logic of Quantified Statements. Predicate Calculus Instructor: Hayk Melikya 2.3.
Section 3.3: Mathematical Induction Mathematical induction is a proof technique that can be used to prove theorems of the form:  n  Z +,P(n) We have.
1 Introduction to Abstract Mathematics Proofs in Predicate Logic , , ~, ,  Instructor: Hayk Melikya Purpose of Section: The theorems.
Chapter 2 Logic 2.1 Statements 2.2 The Negation of a Statement 2.3 The Disjunction and Conjunction of Statements 2.4 The Implication 2.5 More on Implications.
1 Introduction to Abstract Mathematics Predicate Logic Instructor: Hayk Melikya Purpose of Section: To introduce predicate logic (or.
First Order Logic Lecture 3: Sep 13 (chapter 2 of the book)
Zero of Polynomial Functions Factor Theorem Rational Zeros Theorem Number of Zeros Conjugate Zeros Theorem Finding Zeros of a Polynomial Function.
Proofs in Predicate Calculus
Method of proofs.  Consider the statements: “Humans have two eyes”  It implies the “universal quantification”  If a is a Human then a has two eyes.
22C:19 Discrete Structures Logic and Proof Fall 2014 Sukumar Ghosh.
Prime Numbers (3/17 ) We all know what a prime number is. Theorem (Euclid). There are infinitely many primes. Euclid’s original proof idea can be stated.
CSci 2011 Discrete Mathematics Lecture 4 CSci 2011.
CS104:Discrete Structures Chapter 2: Proof Techniques.
Week 4 - Friday.  What did we talk about last time?  Floor and ceiling  Proof by contradiction.
Introduction to Proofs
Section 1.5 and 1.6 Predicates and Quantifiers. Vocabulary Predicate Domain Universal Quantifier Existential Quantifier Counterexample Free variable Bound.
Lecture 041 Predicate Calculus Learning outcomes Students are able to: 1. Evaluate predicate 2. Translate predicate into human language and vice versa.
1 Introduction to Abstract Mathematics Chapter 3: The Logic of Quantified Statements. Predicate Calculus Instructor: Hayk Melikya 3.1.
The Logic of Conditionals Chapter 8 Language, Proof and Logic.
1 Section 7.3 Formal Proofs in Predicate Calculus All proof rules for propositional calculus extend to predicate calculus. Example. … k.  x p(x) P k+1.
1 Section 7.1 First-Order Predicate Calculus Predicate calculus studies the internal structure of sentences where subjects are applied to predicates existentially.
Chapter 1, Part III: Proofs With Question/Answer Animations Copyright © McGraw-Hill Education. All rights reserved. No reproduction or distribution without.
Foundations of Computing I CSE 311 Fall Announcements Homework #2 due today – Solutions available (paper format) in front – HW #3 will be posted.
Uniqueness Quantifier ROI for Quantified Statement.
Formal Proofs and Quantifiers
CS 2210:0001 Discrete Structures Logic and Proof
Lecture Notes 8 CS1502.
Proof methods We will discuss ten proof methods: Direct proofs
Methods of Proof CS 202 Epp, chapter 3.
Chapter 1: The Foundations: Logic and Proofs
CS201: Data Structures and Discrete Mathematics I
CS 1502 Formal Methods in Computer Science
The Foundations: Logic and Proofs
Quantified Propositions
Logical Inferences: A set of premises accompanied by a suggested conclusion regardless of whether or not the conclusion is a logical consequence of the.
First Order Logic Rosen Lecture 3: Sept 11, 12.
Information Technology Department SKN-SITS,Lonavala.
CS201: Data Structures and Discrete Mathematics I
Introduction to Proofs
Introduction to Proofs
Presentation transcript:

Methods of Proof for Quantifiers Chapter 12 Language, Proof and Logic

Valid quantifier steps 12.1 Universal elimination (instantiation): From  xP(x) infer P(c) Existential introduction (generalization): From P(c) infer  xP(x) 1.  x[Cube(x)  Large(x)] 2.  x[Large(x)  LeftOf(x,b)] 3. Cube(d) 4.  x[Large(x)  LeftOf(x,b)] 3 says that d is a cube. And 1 says that all cubes are large. Thus, d is large. But 2 says that every large object is to the left of b. So, d is to the left of b. To summarize, d is large and is to the left of b. Thus, there is a large object to the left of b. where c is the name of some object of the domain of discourse Let us think about whether there is any similarity with  -elim and  -intro.

The method of existential instantiation 12.2 Existential instantiation (elimination): Once you have proven  xP(x) (or have it as a premise), you can select a “neutral” (not used elsewhere) name d and use P(d) as a valid assumption. 1.  x[Cube(x)  Large(x)] 2.  x[Large(x)  LeftOf(x,b)] 3.  xCube(x) 4.  x[Large(x)  LeftOf(x,b)] 3 says that there is a cube. Let d be such a cube, i.e. assume Cube(d) (is true). 1 says that all cubes are large. Thus, d is large. But 2 says that every large object is to the left of b. So, d is to the left of b. To summarize, d is large and is to the left of b. Thus, there is a large object to the left of b. Important: If we had selected d=b, we would have been able to “prove”  xLeftOf(x,x)! Let us think about whether there is any similarity with  -elim.

The method of general conditional proof 12.3.a Universal generalization (introduction): Once you have proven P(d) for some “neutral” (not used elsewhere) name d (denoting a “totally arbitrary” object), you can conclude  xP(x). Consider any object d. By 1, d is large. But, by 2, every large object is in the same row as b. So, d is in the same row as b. As d was arbitrary, we conclude that every object is in the same row as b. Important: The “arbitrary” object 1. Cube(b) d indeed has to be arbitrary. Things 2.  x[Cube(x)  Large(x)] will go wrong if you select d=b here 3.  xLarge(x) 1.  xLarge(x) 2.  x[Large(x)  SameRow(x,b)] 3.  xSameRow(x,b) Let us think about whether there is any similarity with  -intro.

The method of general conditional proof 12.3.b General conditional proof: Once you have proven Q(d) from the assumption P(d) for some “neutral” (not used elsewhere) name d (denoting a “totally arbitrary” object), you can conclude  x[P(x)  Q(x)]. Let us think about why universal generalization in fact makes this rule redundant. 1.  x[Cube(x)  SameRow(x,b)] 2.  x[SameRow(x,b)  Small(x)] 3.  x[Cube(x)  Small(x)] Consider any object d, and assume d is a cube. 1 says that every cube is in the same row as b. So, d is in the same row as b. But, by 2, everything in the same row as b is small. So, d is small. As d was arbitrary, we conclude that every cube is small.

Proofs involving mixed quantifiers 12.4.a 1.  y [ Girl(y)   x ( Boy(x)  Likes(x,y) )] 2.  x [ Boy(x)   y ( Girl(y)  Likes(x,y) )] Consider an arbitrary boy d. By 1, there is a girl who is liked by every boy. Let c be such a girl. So, d likes c. That is, d likes some girl. As d was arbitrary, we conclude that every boy likes some girl. 1.  x [ Boy(x)   y ( Girl(y)  Likes(x,y) )] 2.  y [ Girl(y)   x ( Boy(x)  Likes(x,y) )] Pseudo-proof: Consider an arbitrary boy d. By 1, d likes some girl. Let c be such a girl. Thus, d likes c. Since d was arbitrary, we conclude that every boy likes c. So, there is a girl (specifically, c) who is liked by every boy.

Proofs involving mixed quantifiers 12.4.b REMEMBER Let P(x), Q(x) be wffs. 1.Existential Instantiation: If you have proven  xP(x) then you may choose a new constant symbol c to stand for any object satisfying P(x) and so you may assume P(c). 2. General Conditional Proof: If you want to prove  x[P(x)  Q(x)] then you may choose a new constant symbol c, assume P(c), and prove Q(c), making sure that Q does not contain any names introduced by existential instantiation after the assumption of P(c). 3. Universal Generalization: If you want to prove  xQ(x) then you may choose a new constant symbol c and prove Q(c), making sure that Q does not contain any names introduced by existential instantiation after the introduction of c.

Proofs involving mixed quantifiers 12.4.c Euclid’s Theorem:  x  y[y  x  Prime(y)] Proof. Consider an arbitrary natural number n. Our goal is to show that  y[y  n  Prime(y)], from which Euclid’s theorem follows by universal generalization. Let k be the product of all the prime numbers less than n. Thus each prime with <n divides k without remainder. Now let m=k+1. Each prime less than n divides m with remainder 1. But we know that m can be factored into primes. Let p be one of those primes. Clearly, by the earlier observation, p  n. Hence, by existential generalization, there is a prime (specifically, p) greater or equal to n. As n was arbitrary, we conclude that  x  y[y  x  Prime(y)].

Proofs involving mixed quantifiers 12.4.d The Barber Paradox:  x  y [Shave(x,y)   Shave(y,y)] The domain of discourse is the set of all men in a small village. Proof. Assume, for a contradiction, that 1.  x  y [Shave(x,y)   Shave(y,y)] Let b be a man (barber) such that 2.  y [Shave(b,y)   Shave(y,y)] is true. By universal instantiation from 2, 3. Shave(b,b)   Shave(b,b). But this is (indeed) a contradiction.