SlideShare a Scribd company logo
1 of 7
Download to read offline
QUANTIFICATION THEORY
Definition: (Open Proposition or Propositional Function)
An open proposition is a declarative sentence which
1. contains one or more variables
2. is not a proposition, and
3. produces a proposition when each of its variables is replaced by a specific object from a
designated set.
The set of objects which the variables in an open proposition can represent is the UNIVERSE OF
DISCOURSE of the open proposition. To be precise it is necessary to establish the universe explicitly but
frequently the universe is left implicit.
examples of open propositions
1. the number x + 1 is an even integer.
2. 2x + y = 5 ∧ x - 3y = 8
3. x1
2
+ x2
2
+ x3
2
= 14
4. x is a rational number.
5. y > 5 .
6. x + y = 5
7. x climbed Mount Everest.
8. He is a lawyer and she is a scientist.
Something to think about:
Implicitly, the universe of discourse for open propositions 1, 2, 3, 4, 5, and 6 is the set of integers. What
about propositions 7 and 8?
FUNCTIONAL NOTATIONS
( The above propositional functions can be denoted using capital letters and literal variables )
Example:
1. P( x ) can denote “x is a rational number”
2. Q( x, y ) can denote x+y=5
3. R( X1, X2, X3 ) can denote “x1
2
+ x2
2
+ x3
2
= 14”
BINDING A VARIABLE
To change an open proposition (propositional function) into a proposition, each individual variable
must be bound. This can be done in two ways:
a)By assigning values:
ex. 1) P ( 1 ) is true
When x = 1, the number x + 1 is an even integer.
2) Q ( 2, 1 ) is false
When x=2 and y=1, ( 2x + y = 5 ∧ x - 3y = 8 )
3) R ( 1, 2, 3 ) is true
When X1 = 1, X2 = 2 and X3 = 3, X1
2
+ X2
2
+ X3
2
= 14
B)By Quantification
1. Universal Quantification
Notation: ∀x P( x ) read as “For all x, P( x )”
For an open sentence P(x) with variable x, the sentence ∀x P( x ) is read “for all x, P(x)”
and is true precisely when the truth set for P(x) is the entire universe. The symbol ∀ is called the
universal quantifier.
similar forms:
For all x, All x such that
For every x, Every x such that
For each x, Each x such that
ex:
1) Let P( x ) denote X2
≥ 4
consider U = R
set of real nos.
universe of discourse
∀x P( x ) is false
For all real numbers x, It is false that x2
≥ 4
2) P( x ) : x2
> 0
U = Z+
set of positive integers
∀x P( x ) is true
For all positive integers x , x2
> 0 is true
2. Existential Quantification
Notation : ∃x P( x ) read as “there exist an x such that P(x)”
The sentence ∃x P( x ) is read “there exist an x such that P( x )” and is true precisely
when the truth set for P(x) is nonempty. The symbol ∃ is called the existential quantifier
similar forms:
There exist an x such that…
There is an x such that…
For some x …
There is at least one x such that…
Some x is such that …
ex: Let P( x ) denote x + 75 = 80
U = Z+
set of positive integers
∃x P( x ) is true
“There exist a positive integer x such that x+75=80” is true
DETERMINING THE TRUTH VALUE OF QUANTIFIED PROPOSITIONS
STATEMENT WHEN IS IT TRUE? WHEN IS IT FALSE?
∃x P( x ) if there is at least one c ∈ U For all c ∈ U
such that P( x ) is satisfied P(c) is false
∀x P( x ) If all elements of U If there is at least one c ∈ U
satisfies P( x ) such that P( x ) is false
Quantified Propositions with 2 variables
In general, if P(x,y) is any predicate involving the two variables x and y, then the following
possibilities exist:
(∀x)(∀y)P(x,y) (∀x)(∃y)P(x,y)
(∃x)(∀y)P(x,y) (∃x)(∃y)P(x,y)
(∀y)(∀x)P(x,y) (∃y)(∀x)P(x,y)
(∀y)(∃x)P(x,y) (∃y)(∃x)P(x,y)
If a sentence involves both the universal and the existential quantifiers, one must be careful about
the order in which they are written. One always works from left to right.
For instance, consider the two sentences concerning real numbers:
1. (∀x)(∃y)[x+y=5]
2. (∃y)(∀x)[x+y=5]
Statement 1 is true. Why?
Statement 2 is false. Why?
Question:
Consider (∀x)(∀y)[x∈R ∧ y∈R ⇒ xy ∈ R]
a. What does it mean?(R=set of real numbers)
b. What is its truth value?
Exercises:
A. Determine the truth values:
let U = { 1,2,3... } = Z+
(set of positive integers)
1) ∀x ( x is a prime ⇒ x is odd )
Ans: false ( 2 is prime but not odd )
2) ∀x ∀y ( x is odd ∧ y is odd ) ⇒ x ∗ y is odd
Ans: true
3) ∃w ( 2w + 1 = 5 )
Ans: true
4) ∃x ( 2x + 1 = 5 ∧ x 2
= 9 )
Ans: false
B. Determine the truth value of the following sentences where the universe is the set of integers
1. ∀x,[x2
-2 ≥ 0]
2. ∀x,[x2
-10x+21 = 0]
3. ∃x,[x2
-10x+21 = 0]
4. ∀x,[x2
-x-1 ≠ 0]
5. ∃x,[x2
-3 = 0]
6. ∃x,[(x2
>10) ∧ (x is even)]
4 TYPES OF QUANTIFIED PROPOSITIONS
• let : H(x) denote x is human
• M(x) - x is mortal
1. Universal Affirmative
ex: All humans are mortal. ∀x ( H(x) ⇒ M(x) )
2. Universal Negative
ex: No human is mortal. ∀x ( H(x) ⇒ ¬M(x) )
3. Existential Affirmative
ex: Some humans are mortal. ∃x ( H(x) ∧ M(x) )
4. Existential Negative
ex: Some humans are not mortal. ∃x( H(x) ∧ ¬M(x))
Examples and Exercises:
Translate each of the following statements into symbols, using quantifiers, variables, and predicate
symbols.
1. All members are either parents or teachers.
Ans: ∀x [ M(x) ⇒ ( P(x) ∨ T(x) ) ]
2. Some politicians are either disloyal or misguided.
Ans: ∃x [ P(x) ∧ ( D(x) ∨ M(x) ) ]
3. All birds can fly
4. Not all birds can fly
5. All babies are illogical
6. Some babies are illogical
7. If Joseph is a man, then Joseph is a giant
8. There is a student who likes mathematics but not history
QUANTIFICATION RULES( Rules of Inference):
1. Universal Instantiation or Universal Specification
∀x P( x )__
∴P( c ) * c is an arbitrary element of U
example : universe of discourse,U = set of humans
∀x P( x ) : All humans are mortals
P( c ) : Ronald is mortal.
2. Existential Instantiation or Existential Specification
∃x P( x )__
∴P( c ) *where c is some element of U
3. Universal Generalization ( UG )
P ( c )____
∴∀x P( x )
4. Existential Generalization ( EG )
P( c )____
∴∃x P( x )
Construct a proof of validity:
1. No mortals are perfect.
All humans are mortals.
Therefore, no humans are perfect.
1) ∀x ( M(x) ⇒ ¬P(x) )
2) ∀x ( H(x) ⇒ M(x) )____
∴ ∀x ( H(x) ⇒ ¬P(x) )
3) M(c) ⇒ ¬P(c) Universal Instantiation ( 1 )
4) H(c) ⇒ M(c) Universal Instantiation ( 2 )
5) H(c) ⇒ ¬P(c) Hypothetical Syllogism ( 4 )( 3 )
6) ∀x ( H(x) ⇒ ¬P(x) ) Universal Generalization ( 5 )
2. Tigers are fierce and dangerous.
Some tigers are beautiful.
Therefore, some dangerous things are beautiful.
1) ∀x [ T(x) ⇒ ( F(x) ∧ D(x) ) ]
2) ∃x [ T(x) ∧ B(x) ]____
∴∃x ( D(x) ∧ B(x))
3) T(c) ∧ B(c) Existential Instantiation ( 2 )
4) T(c) ⇒ ( F(c) ∧ D(c) ) Universal Instantiation ( 1 )
5) T(c) Simplification ( 3 )
6) F(c) ∧ D(c) Modus Ponens ( 4 )( 5 )
7) D(c) Simplification ( 6 )
8) B(c) Simplification ( 3 )
9) D(c) ∧ B(c) Conjunction ( 7 )( 8 )
10) ∃x [ D(x) ∧ B(x)] Existential Generalization ( 9 )
3. Some cats are animals.
Some dogs are animals.
Therefore, some cats are dogs.
Find the mistake in the given proof.
1) ∃x ( C(x) ∧ A(x) ) [ans: error at line 4, we should not use w to instantiate set D.]
2) ∃x( D(x) ∧ A(x) ) [ w is a cat as used in line 3]
∴∃x( C(x) ∧ D(x) )
3) C(w) ∧ A(w) Existential Instantiation ( 1 )
4 ) D(w) ∧ A(w) Existential Instantiation ( 2 )
5) C(w) Simplification ( 3 )
6) D(w) Simplification ( 4 )
7) C(w) ∧ D(w) Conjunction ( 5 )( 6 )
8) ∃x ( C(x) ∧ D(x) ) Existential Generalization ( 7 )
4. Required: Proof of validity
Mathematicians are neither prophets nor wizards
Hence, if Einstein is a mathematician, he is not a prophet.
1) ∀x [ M(x) ⇒ ¬( P(x) ∨ W(x) ) ]
∴M(e) ⇒ ¬P(e)
2) M(e) Rule of Conditional Proof
∴¬P(e)
3) M(e) ⇒ ¬( P(e) ∨ W(e) ) Universal Instantiation ( 1 )
4) ¬( P(e) ∨ W(e) ) Modus Ponens ( 2 )( 3 )
5) ¬P(e) ∧ ¬W(e) de Morgan’s law ( 4 )
6) ¬P(e) Simplification ( 5 )
QUANTIFICATION NEGATION ( use ≡ instead of ⇔ in the ff )
1. ¬∃xA(x) ⇔ ∀x ¬( A(x) )
2. ¬∀xA(x) ⇔ ∃x ¬( A(x) )
3. ¬∃x ( A(x) ∧ B(x) ) ⇔ ∀x ( A(x) ⇒ ¬B(x) )
4. ¬∀x ( A(x) ⇒ B(x) ) ⇔ ∃x ( A(x) ∧ ¬B(x) )
State the negation of the following :
1. All officers are fighters.
∀x ( O(x) ⇒ F(x) )
negation: ¬∀x ( O(x) ⇒ F(x) ) ⇔ ∃x [ O(x) ∧ ¬F(x) ]
Some officers are not fighters.
2. Some members are fighters.
∃x [ M(x) ∧ F(x) ]
negation: ¬∃x [ M(x) ∧ F(x) ] ⇔ ∀x [ M(x) ⇒ ¬F(x) ]
All members are not fighter or No members are fighters.
Symbolizing Relations
The following expresses relations between two individuals:
1. Plato was a student of Socrates.
2. Baguio is north of Manila.
3. Chicago is smaller than New York.
These are called “BINARY” or “ DYADIC” relations
Let A( x, y ) : x attracts y then
1) b attracts everything ∀x A( b, x )
2 ) b attracts something ∃x A( b, x )
3) everything attracts b ∀x A( x, b )
4) something attracts b ∃x A( x, b )
5) everything attracts everything ∀x∀y( x, y )
example: Helen likes David
Whoever likes David likes Tom
Helen likes only good looking men
Therefore, Tom is a good looking man
let : h = Helen; d = David; t = Tom
G(x) = x is a good looking man
L( x, y ) : x likes y
1) L( h, d )
2) ∀x [ L( x, d ) ⇒ L( x, t ) ]
3) ∀x[ L( h, x ) ⇒ G(x) ]
∴G(t)
4) L( h, d ) ⇒ L( h, t ) Universal Instantiation( 2 )
5) L( h, t ) Modus Ponens( 1 )( 4 )
6) L( h, t ) ⇒ G(t) Universal Instantiation ( 3 )
7) G(t) Modus Ponens( 5 )( 6 )
“There is a man whom all men despise.
Therefore, at least one man despises himself.”
Let D( x, y ) : x despises y
M(x) : x is a man
1) ∃x [ M(x) ∧ ∀y ( M(y) ⇒ D( y, x ) ]
∴∃x [ M(x) ∧ D( x, x ) ]
2) M(c) ∧ ∀y [ M(y) ⇒ D( y, c ) ] Existential Instantiation ( 1 )
3) M(c) ∧ [ M(c) ⇒ D( c, c ) ] Universal Instantiation ( 2 )
4) M(c) ⇒ D( c, c ) Simplification ( 3 )
8) ∃x [ M(x) ∧ D( x, x ) ] Existential Generalization ( )
Transcribe the following assertions into logical notations
Let P(x,y,z) denote x - y = z
1. For every x and y, there are some z such that x-z=y
∀x ∀y [ ∃zP( x, z, y ) ]
2. There is an x such that for all y, y - x = y
∃x [ ∀y P( y, x, y ) ]
Quantification Negation
a) For all x [ x is prime implies (x2
+ 1) is even ]
∀x ( x is prime ⇒ x2
+ 1 is even )
negation:
≡ ¬∀x ( x is prime ⇒ x2
+ 1 is even )
≡ ∃x ( x is prime ∧ x2
+ 1 is not even )
“There exists an x such that x is prime and x2
+1 is odd”
b) There exists an x ( x is rational ∧ x2
= 3 )
∃x ( x is rational ∧ x2
= 3 )
negation:
≡ ¬∃x ( x is rational ∧ x2
= 3 )
≡ ∀x ( x is rational ⇒ x2
< > 3 )
“ For all x, x is rational and 2x is not equal to three”
c)There exist an x such that for all y ( xy = y )
∃x [ ∀y ( xy = y ) ]
negation:
≡ ¬∃x [ ∀y ( xy = y ) ]
≡ ∀x [ ∃y ( xy < > y ) ]
“For all x , there exists a y such that xy is not equal to y .”
A Peek at PROLOG
ü The following are statements in Prolog. The statements describe a pile of colored blocks.
isabove(g,b1)
isabove(b1,w1)
isabove(w2,b2)
color(g,gray)
color(b1,blue)
color(b2,blue)
color(b3,blue)
color(w1,white)
color(w2,white)
isabove(X,Z) if isabove(X,Y) and isabove(Y,Z)
ü The following are statements in Prolog. The statements ask questions about the piled blocks
?color(b1,blue)
?isabove(X,w1

More Related Content

What's hot

Andrei rusu-2013-amaa-workshop
Andrei rusu-2013-amaa-workshopAndrei rusu-2013-amaa-workshop
Andrei rusu-2013-amaa-workshopAndries Rusu
 
Harmonic Analysis and Deep Learning
Harmonic Analysis and Deep LearningHarmonic Analysis and Deep Learning
Harmonic Analysis and Deep LearningSungbin Lim
 
Fixed points of contractive and Geraghty contraction mappings under the influ...
Fixed points of contractive and Geraghty contraction mappings under the influ...Fixed points of contractive and Geraghty contraction mappings under the influ...
Fixed points of contractive and Geraghty contraction mappings under the influ...IJERA Editor
 
2.1 limits i
2.1 limits i2.1 limits i
2.1 limits imath265
 
Truth, deduction, computation lecture a
Truth, deduction, computation   lecture aTruth, deduction, computation   lecture a
Truth, deduction, computation lecture aVlad Patryshev
 
Advanced Microeconomics - Lecture Slides
Advanced Microeconomics - Lecture SlidesAdvanced Microeconomics - Lecture Slides
Advanced Microeconomics - Lecture SlidesYosuke YASUDA
 
Limits and continuity[1]
Limits and continuity[1]Limits and continuity[1]
Limits and continuity[1]indu thakur
 
2.3 continuity
2.3 continuity2.3 continuity
2.3 continuitymath265
 
Introduction to Decision Making Theory
Introduction to Decision Making TheoryIntroduction to Decision Making Theory
Introduction to Decision Making TheoryYosuke YASUDA
 
Higher Derivatives & Partial Differentiation
Higher Derivatives & Partial DifferentiationHigher Derivatives & Partial Differentiation
Higher Derivatives & Partial DifferentiationRaymundo Raymund
 

What's hot (16)

Legendre Function
Legendre FunctionLegendre Function
Legendre Function
 
Andrei rusu-2013-amaa-workshop
Andrei rusu-2013-amaa-workshopAndrei rusu-2013-amaa-workshop
Andrei rusu-2013-amaa-workshop
 
Matrix calculus
Matrix calculusMatrix calculus
Matrix calculus
 
Harmonic Analysis and Deep Learning
Harmonic Analysis and Deep LearningHarmonic Analysis and Deep Learning
Harmonic Analysis and Deep Learning
 
Fixed points of contractive and Geraghty contraction mappings under the influ...
Fixed points of contractive and Geraghty contraction mappings under the influ...Fixed points of contractive and Geraghty contraction mappings under the influ...
Fixed points of contractive and Geraghty contraction mappings under the influ...
 
Legendre
LegendreLegendre
Legendre
 
2.1 limits i
2.1 limits i2.1 limits i
2.1 limits i
 
Truth, deduction, computation lecture a
Truth, deduction, computation   lecture aTruth, deduction, computation   lecture a
Truth, deduction, computation lecture a
 
Advanced Microeconomics - Lecture Slides
Advanced Microeconomics - Lecture SlidesAdvanced Microeconomics - Lecture Slides
Advanced Microeconomics - Lecture Slides
 
HERMITE SERIES
HERMITE SERIESHERMITE SERIES
HERMITE SERIES
 
Limits and continuity[1]
Limits and continuity[1]Limits and continuity[1]
Limits and continuity[1]
 
2.3 continuity
2.3 continuity2.3 continuity
2.3 continuity
 
Introduction to Decision Making Theory
Introduction to Decision Making TheoryIntroduction to Decision Making Theory
Introduction to Decision Making Theory
 
Higher Derivatives & Partial Differentiation
Higher Derivatives & Partial DifferentiationHigher Derivatives & Partial Differentiation
Higher Derivatives & Partial Differentiation
 
Numerical Methods 3
Numerical Methods 3Numerical Methods 3
Numerical Methods 3
 
Imc2017 day1-solutions
Imc2017 day1-solutionsImc2017 day1-solutions
Imc2017 day1-solutions
 

Viewers also liked

Viewers also liked (6)

W&b crisis escape
W&b crisis escapeW&b crisis escape
W&b crisis escape
 
From Paper to Person
From Paper to Person From Paper to Person
From Paper to Person
 
Pr1 pemulihan
Pr1 pemulihanPr1 pemulihan
Pr1 pemulihan
 
Connecting to elf
Connecting to elfConnecting to elf
Connecting to elf
 
MDS 3.0 Changes and Confusing issues
MDS 3.0 Changes and Confusing issuesMDS 3.0 Changes and Confusing issues
MDS 3.0 Changes and Confusing issues
 
Mind over matter
Mind over matterMind over matter
Mind over matter
 

Similar to Course notes2summer2012

Finance Enginering from Columbia.pdf
Finance Enginering from Columbia.pdfFinance Enginering from Columbia.pdf
Finance Enginering from Columbia.pdfCarlosLazo45
 
Can we estimate a constant?
Can we estimate a constant?Can we estimate a constant?
Can we estimate a constant?Christian Robert
 
Use of quantifiers
Use of quantifiersUse of quantifiers
Use of quantifiersLakshmi R
 
A common fixed point theorem in cone metric spaces
A common fixed point theorem in cone metric spacesA common fixed point theorem in cone metric spaces
A common fixed point theorem in cone metric spacesAlexander Decker
 
A Note on “   Geraghty contraction type mappings”
A Note on “   Geraghty contraction type mappings”A Note on “   Geraghty contraction type mappings”
A Note on “   Geraghty contraction type mappings”IOSRJM
 
Expectation of Discrete Random Variable.ppt
Expectation of Discrete Random Variable.pptExpectation of Discrete Random Variable.ppt
Expectation of Discrete Random Variable.pptAlyasarJabbarli
 
Discrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdfDiscrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdfMuhammadUmerIhtisham
 
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...IOSR Journals
 
IVR - Chapter 1 - Introduction
IVR - Chapter 1 - IntroductionIVR - Chapter 1 - Introduction
IVR - Chapter 1 - IntroductionCharles Deledalle
 
Chapter 01 - p2.pdf
Chapter 01 - p2.pdfChapter 01 - p2.pdf
Chapter 01 - p2.pdfsmarwaneid
 
Quantifiers and its Types
Quantifiers and its TypesQuantifiers and its Types
Quantifiers and its TypesHumayunNaseer4
 
Physical Chemistry Assignment Help
Physical Chemistry Assignment HelpPhysical Chemistry Assignment Help
Physical Chemistry Assignment HelpEdu Assignment Help
 
Lecture 2 predicates quantifiers and rules of inference
Lecture 2 predicates quantifiers and rules of inferenceLecture 2 predicates quantifiers and rules of inference
Lecture 2 predicates quantifiers and rules of inferenceasimnawaz54
 

Similar to Course notes2summer2012 (20)

3 fol examples v2
3 fol examples v23 fol examples v2
3 fol examples v2
 
Finance Enginering from Columbia.pdf
Finance Enginering from Columbia.pdfFinance Enginering from Columbia.pdf
Finance Enginering from Columbia.pdf
 
CLIM Fall 2017 Course: Statistics for Climate Research, Statistics of Climate...
CLIM Fall 2017 Course: Statistics for Climate Research, Statistics of Climate...CLIM Fall 2017 Course: Statistics for Climate Research, Statistics of Climate...
CLIM Fall 2017 Course: Statistics for Climate Research, Statistics of Climate...
 
Can we estimate a constant?
Can we estimate a constant?Can we estimate a constant?
Can we estimate a constant?
 
Use of quantifiers
Use of quantifiersUse of quantifiers
Use of quantifiers
 
A common fixed point theorem in cone metric spaces
A common fixed point theorem in cone metric spacesA common fixed point theorem in cone metric spaces
A common fixed point theorem in cone metric spaces
 
Slides mc gill-v4
Slides mc gill-v4Slides mc gill-v4
Slides mc gill-v4
 
Slides mc gill-v3
Slides mc gill-v3Slides mc gill-v3
Slides mc gill-v3
 
A Note on “   Geraghty contraction type mappings”
A Note on “   Geraghty contraction type mappings”A Note on “   Geraghty contraction type mappings”
A Note on “   Geraghty contraction type mappings”
 
Expectation of Discrete Random Variable.ppt
Expectation of Discrete Random Variable.pptExpectation of Discrete Random Variable.ppt
Expectation of Discrete Random Variable.ppt
 
Discrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdfDiscrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdf
 
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
 
IVR - Chapter 1 - Introduction
IVR - Chapter 1 - IntroductionIVR - Chapter 1 - Introduction
IVR - Chapter 1 - Introduction
 
Chapter 01 - p2.pdf
Chapter 01 - p2.pdfChapter 01 - p2.pdf
Chapter 01 - p2.pdf
 
Predicates
PredicatesPredicates
Predicates
 
X02PredCalculus.ppt
X02PredCalculus.pptX02PredCalculus.ppt
X02PredCalculus.ppt
 
Quantifiers and its Types
Quantifiers and its TypesQuantifiers and its Types
Quantifiers and its Types
 
metric spaces
metric spacesmetric spaces
metric spaces
 
Physical Chemistry Assignment Help
Physical Chemistry Assignment HelpPhysical Chemistry Assignment Help
Physical Chemistry Assignment Help
 
Lecture 2 predicates quantifiers and rules of inference
Lecture 2 predicates quantifiers and rules of inferenceLecture 2 predicates quantifiers and rules of inference
Lecture 2 predicates quantifiers and rules of inference
 

More from Von Adam Martinez

More from Von Adam Martinez (6)

Executive support system
Executive support systemExecutive support system
Executive support system
 
Logic parti
Logic partiLogic parti
Logic parti
 
Course notes1
Course notes1Course notes1
Course notes1
 
Reviewer cpu scheduling
Reviewer cpu schedulingReviewer cpu scheduling
Reviewer cpu scheduling
 
Template 1 ch5
Template 1 ch5Template 1 ch5
Template 1 ch5
 
Template 1 ch3
Template 1 ch3Template 1 ch3
Template 1 ch3
 

Recently uploaded

Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersThousandEyes
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Paola De la Torre
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Alan Dix
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 

Recently uploaded (20)

Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 

Course notes2summer2012

  • 1. QUANTIFICATION THEORY Definition: (Open Proposition or Propositional Function) An open proposition is a declarative sentence which 1. contains one or more variables 2. is not a proposition, and 3. produces a proposition when each of its variables is replaced by a specific object from a designated set. The set of objects which the variables in an open proposition can represent is the UNIVERSE OF DISCOURSE of the open proposition. To be precise it is necessary to establish the universe explicitly but frequently the universe is left implicit. examples of open propositions 1. the number x + 1 is an even integer. 2. 2x + y = 5 ∧ x - 3y = 8 3. x1 2 + x2 2 + x3 2 = 14 4. x is a rational number. 5. y > 5 . 6. x + y = 5 7. x climbed Mount Everest. 8. He is a lawyer and she is a scientist. Something to think about: Implicitly, the universe of discourse for open propositions 1, 2, 3, 4, 5, and 6 is the set of integers. What about propositions 7 and 8? FUNCTIONAL NOTATIONS ( The above propositional functions can be denoted using capital letters and literal variables ) Example: 1. P( x ) can denote “x is a rational number” 2. Q( x, y ) can denote x+y=5 3. R( X1, X2, X3 ) can denote “x1 2 + x2 2 + x3 2 = 14” BINDING A VARIABLE To change an open proposition (propositional function) into a proposition, each individual variable must be bound. This can be done in two ways: a)By assigning values: ex. 1) P ( 1 ) is true When x = 1, the number x + 1 is an even integer. 2) Q ( 2, 1 ) is false When x=2 and y=1, ( 2x + y = 5 ∧ x - 3y = 8 ) 3) R ( 1, 2, 3 ) is true When X1 = 1, X2 = 2 and X3 = 3, X1 2 + X2 2 + X3 2 = 14 B)By Quantification 1. Universal Quantification Notation: ∀x P( x ) read as “For all x, P( x )”
  • 2. For an open sentence P(x) with variable x, the sentence ∀x P( x ) is read “for all x, P(x)” and is true precisely when the truth set for P(x) is the entire universe. The symbol ∀ is called the universal quantifier. similar forms: For all x, All x such that For every x, Every x such that For each x, Each x such that ex: 1) Let P( x ) denote X2 ≥ 4 consider U = R set of real nos. universe of discourse ∀x P( x ) is false For all real numbers x, It is false that x2 ≥ 4 2) P( x ) : x2 > 0 U = Z+ set of positive integers ∀x P( x ) is true For all positive integers x , x2 > 0 is true 2. Existential Quantification Notation : ∃x P( x ) read as “there exist an x such that P(x)” The sentence ∃x P( x ) is read “there exist an x such that P( x )” and is true precisely when the truth set for P(x) is nonempty. The symbol ∃ is called the existential quantifier similar forms: There exist an x such that… There is an x such that… For some x … There is at least one x such that… Some x is such that … ex: Let P( x ) denote x + 75 = 80 U = Z+ set of positive integers ∃x P( x ) is true “There exist a positive integer x such that x+75=80” is true DETERMINING THE TRUTH VALUE OF QUANTIFIED PROPOSITIONS STATEMENT WHEN IS IT TRUE? WHEN IS IT FALSE? ∃x P( x ) if there is at least one c ∈ U For all c ∈ U such that P( x ) is satisfied P(c) is false ∀x P( x ) If all elements of U If there is at least one c ∈ U satisfies P( x ) such that P( x ) is false Quantified Propositions with 2 variables
  • 3. In general, if P(x,y) is any predicate involving the two variables x and y, then the following possibilities exist: (∀x)(∀y)P(x,y) (∀x)(∃y)P(x,y) (∃x)(∀y)P(x,y) (∃x)(∃y)P(x,y) (∀y)(∀x)P(x,y) (∃y)(∀x)P(x,y) (∀y)(∃x)P(x,y) (∃y)(∃x)P(x,y) If a sentence involves both the universal and the existential quantifiers, one must be careful about the order in which they are written. One always works from left to right. For instance, consider the two sentences concerning real numbers: 1. (∀x)(∃y)[x+y=5] 2. (∃y)(∀x)[x+y=5] Statement 1 is true. Why? Statement 2 is false. Why? Question: Consider (∀x)(∀y)[x∈R ∧ y∈R ⇒ xy ∈ R] a. What does it mean?(R=set of real numbers) b. What is its truth value? Exercises: A. Determine the truth values: let U = { 1,2,3... } = Z+ (set of positive integers) 1) ∀x ( x is a prime ⇒ x is odd ) Ans: false ( 2 is prime but not odd ) 2) ∀x ∀y ( x is odd ∧ y is odd ) ⇒ x ∗ y is odd Ans: true 3) ∃w ( 2w + 1 = 5 ) Ans: true 4) ∃x ( 2x + 1 = 5 ∧ x 2 = 9 ) Ans: false B. Determine the truth value of the following sentences where the universe is the set of integers 1. ∀x,[x2 -2 ≥ 0] 2. ∀x,[x2 -10x+21 = 0] 3. ∃x,[x2 -10x+21 = 0] 4. ∀x,[x2 -x-1 ≠ 0] 5. ∃x,[x2 -3 = 0] 6. ∃x,[(x2 >10) ∧ (x is even)] 4 TYPES OF QUANTIFIED PROPOSITIONS • let : H(x) denote x is human • M(x) - x is mortal 1. Universal Affirmative ex: All humans are mortal. ∀x ( H(x) ⇒ M(x) ) 2. Universal Negative ex: No human is mortal. ∀x ( H(x) ⇒ ¬M(x) ) 3. Existential Affirmative ex: Some humans are mortal. ∃x ( H(x) ∧ M(x) ) 4. Existential Negative ex: Some humans are not mortal. ∃x( H(x) ∧ ¬M(x))
  • 4. Examples and Exercises: Translate each of the following statements into symbols, using quantifiers, variables, and predicate symbols. 1. All members are either parents or teachers. Ans: ∀x [ M(x) ⇒ ( P(x) ∨ T(x) ) ] 2. Some politicians are either disloyal or misguided. Ans: ∃x [ P(x) ∧ ( D(x) ∨ M(x) ) ] 3. All birds can fly 4. Not all birds can fly 5. All babies are illogical 6. Some babies are illogical 7. If Joseph is a man, then Joseph is a giant 8. There is a student who likes mathematics but not history QUANTIFICATION RULES( Rules of Inference): 1. Universal Instantiation or Universal Specification ∀x P( x )__ ∴P( c ) * c is an arbitrary element of U example : universe of discourse,U = set of humans ∀x P( x ) : All humans are mortals P( c ) : Ronald is mortal. 2. Existential Instantiation or Existential Specification ∃x P( x )__ ∴P( c ) *where c is some element of U 3. Universal Generalization ( UG ) P ( c )____ ∴∀x P( x ) 4. Existential Generalization ( EG ) P( c )____ ∴∃x P( x ) Construct a proof of validity: 1. No mortals are perfect. All humans are mortals. Therefore, no humans are perfect. 1) ∀x ( M(x) ⇒ ¬P(x) ) 2) ∀x ( H(x) ⇒ M(x) )____ ∴ ∀x ( H(x) ⇒ ¬P(x) ) 3) M(c) ⇒ ¬P(c) Universal Instantiation ( 1 ) 4) H(c) ⇒ M(c) Universal Instantiation ( 2 ) 5) H(c) ⇒ ¬P(c) Hypothetical Syllogism ( 4 )( 3 ) 6) ∀x ( H(x) ⇒ ¬P(x) ) Universal Generalization ( 5 ) 2. Tigers are fierce and dangerous. Some tigers are beautiful. Therefore, some dangerous things are beautiful. 1) ∀x [ T(x) ⇒ ( F(x) ∧ D(x) ) ] 2) ∃x [ T(x) ∧ B(x) ]____ ∴∃x ( D(x) ∧ B(x)) 3) T(c) ∧ B(c) Existential Instantiation ( 2 ) 4) T(c) ⇒ ( F(c) ∧ D(c) ) Universal Instantiation ( 1 )
  • 5. 5) T(c) Simplification ( 3 ) 6) F(c) ∧ D(c) Modus Ponens ( 4 )( 5 ) 7) D(c) Simplification ( 6 ) 8) B(c) Simplification ( 3 ) 9) D(c) ∧ B(c) Conjunction ( 7 )( 8 ) 10) ∃x [ D(x) ∧ B(x)] Existential Generalization ( 9 ) 3. Some cats are animals. Some dogs are animals. Therefore, some cats are dogs. Find the mistake in the given proof. 1) ∃x ( C(x) ∧ A(x) ) [ans: error at line 4, we should not use w to instantiate set D.] 2) ∃x( D(x) ∧ A(x) ) [ w is a cat as used in line 3] ∴∃x( C(x) ∧ D(x) ) 3) C(w) ∧ A(w) Existential Instantiation ( 1 ) 4 ) D(w) ∧ A(w) Existential Instantiation ( 2 ) 5) C(w) Simplification ( 3 ) 6) D(w) Simplification ( 4 ) 7) C(w) ∧ D(w) Conjunction ( 5 )( 6 ) 8) ∃x ( C(x) ∧ D(x) ) Existential Generalization ( 7 ) 4. Required: Proof of validity Mathematicians are neither prophets nor wizards Hence, if Einstein is a mathematician, he is not a prophet. 1) ∀x [ M(x) ⇒ ¬( P(x) ∨ W(x) ) ] ∴M(e) ⇒ ¬P(e) 2) M(e) Rule of Conditional Proof ∴¬P(e) 3) M(e) ⇒ ¬( P(e) ∨ W(e) ) Universal Instantiation ( 1 ) 4) ¬( P(e) ∨ W(e) ) Modus Ponens ( 2 )( 3 ) 5) ¬P(e) ∧ ¬W(e) de Morgan’s law ( 4 ) 6) ¬P(e) Simplification ( 5 ) QUANTIFICATION NEGATION ( use ≡ instead of ⇔ in the ff ) 1. ¬∃xA(x) ⇔ ∀x ¬( A(x) ) 2. ¬∀xA(x) ⇔ ∃x ¬( A(x) ) 3. ¬∃x ( A(x) ∧ B(x) ) ⇔ ∀x ( A(x) ⇒ ¬B(x) ) 4. ¬∀x ( A(x) ⇒ B(x) ) ⇔ ∃x ( A(x) ∧ ¬B(x) ) State the negation of the following : 1. All officers are fighters. ∀x ( O(x) ⇒ F(x) ) negation: ¬∀x ( O(x) ⇒ F(x) ) ⇔ ∃x [ O(x) ∧ ¬F(x) ] Some officers are not fighters. 2. Some members are fighters. ∃x [ M(x) ∧ F(x) ] negation: ¬∃x [ M(x) ∧ F(x) ] ⇔ ∀x [ M(x) ⇒ ¬F(x) ] All members are not fighter or No members are fighters. Symbolizing Relations The following expresses relations between two individuals: 1. Plato was a student of Socrates. 2. Baguio is north of Manila.
  • 6. 3. Chicago is smaller than New York. These are called “BINARY” or “ DYADIC” relations Let A( x, y ) : x attracts y then 1) b attracts everything ∀x A( b, x ) 2 ) b attracts something ∃x A( b, x ) 3) everything attracts b ∀x A( x, b ) 4) something attracts b ∃x A( x, b ) 5) everything attracts everything ∀x∀y( x, y ) example: Helen likes David Whoever likes David likes Tom Helen likes only good looking men Therefore, Tom is a good looking man let : h = Helen; d = David; t = Tom G(x) = x is a good looking man L( x, y ) : x likes y 1) L( h, d ) 2) ∀x [ L( x, d ) ⇒ L( x, t ) ] 3) ∀x[ L( h, x ) ⇒ G(x) ] ∴G(t) 4) L( h, d ) ⇒ L( h, t ) Universal Instantiation( 2 ) 5) L( h, t ) Modus Ponens( 1 )( 4 ) 6) L( h, t ) ⇒ G(t) Universal Instantiation ( 3 ) 7) G(t) Modus Ponens( 5 )( 6 ) “There is a man whom all men despise. Therefore, at least one man despises himself.” Let D( x, y ) : x despises y M(x) : x is a man 1) ∃x [ M(x) ∧ ∀y ( M(y) ⇒ D( y, x ) ] ∴∃x [ M(x) ∧ D( x, x ) ] 2) M(c) ∧ ∀y [ M(y) ⇒ D( y, c ) ] Existential Instantiation ( 1 ) 3) M(c) ∧ [ M(c) ⇒ D( c, c ) ] Universal Instantiation ( 2 ) 4) M(c) ⇒ D( c, c ) Simplification ( 3 ) 8) ∃x [ M(x) ∧ D( x, x ) ] Existential Generalization ( ) Transcribe the following assertions into logical notations Let P(x,y,z) denote x - y = z 1. For every x and y, there are some z such that x-z=y ∀x ∀y [ ∃zP( x, z, y ) ] 2. There is an x such that for all y, y - x = y ∃x [ ∀y P( y, x, y ) ]
  • 7. Quantification Negation a) For all x [ x is prime implies (x2 + 1) is even ] ∀x ( x is prime ⇒ x2 + 1 is even ) negation: ≡ ¬∀x ( x is prime ⇒ x2 + 1 is even ) ≡ ∃x ( x is prime ∧ x2 + 1 is not even ) “There exists an x such that x is prime and x2 +1 is odd” b) There exists an x ( x is rational ∧ x2 = 3 ) ∃x ( x is rational ∧ x2 = 3 ) negation: ≡ ¬∃x ( x is rational ∧ x2 = 3 ) ≡ ∀x ( x is rational ⇒ x2 < > 3 ) “ For all x, x is rational and 2x is not equal to three” c)There exist an x such that for all y ( xy = y ) ∃x [ ∀y ( xy = y ) ] negation: ≡ ¬∃x [ ∀y ( xy = y ) ] ≡ ∀x [ ∃y ( xy < > y ) ] “For all x , there exists a y such that xy is not equal to y .” A Peek at PROLOG ü The following are statements in Prolog. The statements describe a pile of colored blocks. isabove(g,b1) isabove(b1,w1) isabove(w2,b2) color(g,gray) color(b1,blue) color(b2,blue) color(b3,blue) color(w1,white) color(w2,white) isabove(X,Z) if isabove(X,Y) and isabove(Y,Z) ü The following are statements in Prolog. The statements ask questions about the piled blocks ?color(b1,blue) ?isabove(X,w1