SlideShare una empresa de Scribd logo
1 de 15
Descargar para leer sin conexión
Formal Logic




        Mathematical Structures
         for Computer Science
                Chapter 1	





Copyright © 2006 W.H. Freeman & Co.       MSCS Slides   Formal Logic
Variables and Statements
  ●     Variables in Logic	

  ●     A variable is a symbol that stands for an individual in a collection
        or set. For example, the variable x may stand for one of the days.
        We may let x = Monday or x = Tuesday, etc. 	

  ●     Incomplete Statements	

  ●     A sentence containing a variable is called an incomplete statement.
        An incomplete statement is about the individuals in a definite
        domain or set. When we replace the variable by the name of an
        individual in the set we obtain a statement about that individual. 	

          ■    Example of an incomplete statement : “x has 30 days.” 	

  ●     Here, x can be any month and substituting that, we will get a
        complete statement.	





Section 1.3                              Quantifiers, Predicates and Validity    1
Quantifiers and Predicates
     ●        Quantifiers: 	

     ●        Quantifiers are phrases that refer to given quantities, such as
              “for some” or “for all” or “for every,” indicating how many
              objects have a certain property. 	


     ●        Two kinds of quantifiers: Universal and Existential	

     ●        Universal Quantifier: represented by ∀	

     ●        The symbol is translated as and means “for all”, “given any”,
              “for each,” or “for every,” and is known as the universal
              quantifier. 	


     ●        Existential Quantifier: represented by ∃	

     ●        The symbol is translated as and means variously “for some,”
              “there exists,” “there is a,” or “for at least one”.	


Section 1.3                             Quantifiers, Predicates and Validity   2
Quantifiers and Predicates
     ●        Predicate	

     ●        It is the verbal statement that describes the property of a
              variable. Usually represented by the letter P, the notation P(x) is
              used to represent some unspecified property or predicate that x
              may have	

               ■    e.g. P(x) = x has 30 days. 	

               ■    P(April) = April has 30 days.	

     ●        Combining the quantifier and the predicate, we get a complete
              statement of the form ∀(x)P(x) or ∃(x)P(x). 	

     ●        The collection of objects that satisfy the property P(x) is called
              the domain of interpretation.	

     ●        Truth value of expressions formed using quantifiers and
              predicates	

               ■       What is the truth value of ∀(x)P(x) where x is all the months and 	

                    	

P(x) = x has less than 32 days.	

               ■       Undoubtedly, the above is true since no month has 32 days.	


Section 1.3                                   Quantifiers, Predicates and Validity             3
Truth value of the following expressions
     ●        Truth of expression ∀(x)P(x)	

              1.    P(x) is the property that x is yellow, and the domain of
                    interpretation is the collection of all flowers: 	

  not true	

              2.    P(x) is the property that x is a plant, and the domain of
                    interpretation is the collection of all flowers:	

   true	

              3.    P(x) is the property that x is positive, and the domain of
                    interpretation consists of integers:	

              not true	

              ■     Can you find one interpretation in which both ∀(x)P(x) is true and
                    ∃(x)P(x) is false?	

                        Not possible	

              ■     Can you find one interpretation in which both ∃(x)P(x) is true and
                    ∀(x)P(x) is false?      	

                 Case 1 as mentioned above	

     ●        Predicates involving properties of single variables : unary
              predicates 	

     ●        Binary, ternary and n-ary predicates are also possible.	

              ■     (∀x) (∃y)Q(x,y) is a binary predicate. This expression reads as
                    “for every x there exists a y such that Q(x,y).”	



Section 1.3                                 Quantifiers, Predicates and Validity           4
Interpretation
     ●       Formal definition: An interpretation for an expression involving
             predicates consists of the following:	

          	

A collection of objects, called the domain of interpretation,
             which must include at least one object.	

          	

An assignment of a property of the objects in the domain to each
             predicate in the expression.	

          	

An assignment of a particular object in the domain to each
             constant symbol in the expression.	


     ●        Predicate wffs can be built similar to propositional wffs using
              logical connectives with predicates and quantifiers.	

     ●        Examples of predicate wffs	

               ■    (∀x)[P(x) → Q(x)]	

               ■    (∀x) ((∃y)[P(x,y) V Q(x,y)] → R(x)) 	

                        	

	

               ■    S(x,y) Λ R(x,y)	



Section 1.3                                 Quantifiers, Predicates and Validity            5
Scope of a variable in an expression

     ●        Brackets are used wisely to identify the scope of the variable.	

               ■    (∀x) [∃(y)[P(x,y) V Q(x,y)] → R(x)] 	

	

               ■    Scope of (∃y) is P(x,y) V Q(x,y) while the scope of (∀x) is the entire
                    expression.	

               ■    (∀x)S(x) V (∃y)R(y) 	

               ■    Scope of x is S(x) while the scope of y is R(y). 	

               ■    (∀x)[P(x,y) → (∃y) Q(x,y)]	

               ■    Scope of variable y is not defined for P(x,y) hence y is called a free
                    variable. Such expressions might not have a truth value at all.	

     ●        What is the truth of the expression	

               ■    ∃(x)[A(x) Λ ∀(y)[B(x,y) → C(y)]] in the interpretation	

               ■    A(x) is “x > 0” , B(x, y) is “x > y” and C(y) is “y ≤ 0” where the
                    domain of x is positive integers and the domain of y is all integers	



   True, x=1 is a positive integer and any integer less than x is ≤ 0 	


Section 1.3                                  Quantifiers, Predicates and Validity             6
Translation: Verbal statements to symbolic form
     ●        “Every person is nice” can be rephrased as “For any thing, if it
              is a person, then it is nice.” So, if P(x) is “x is a person” and Q
              (x) be “x is nice,” the statement can be symbolized as 	

               ■    (∀x)[P(x) → Q(x)]	

               ■    “All persons are nice” or “Each person is nice” will also have the
                    same symbolic form.	

     ●        “There is a nice person” can be rewritten as “There exists
              something that is both a person and nice.”	

               ■    In symbolic form, (∃x)[P(x) Λ Q(x)].	

               ■    Variations: “Some persons are nice” or “There are nice persons.”	

     ●        What would the following form mean for the example above?	

          	

    	

     	

      	

(∃x)[P(x) → Q(x)]	

               ■    This will only be true if there are no persons in the world but that
                    is not the case. 	

               ■    Hence such a statement is false, so almost always, ∃ goes with Λ
                    (conjunction) and ∀ goes with → (implication).	



Section 1.3                                  Quantifiers, Predicates and Validity          7
Translation
●    Hint: Avoid confusion by framing the statement in different forms as
     possible.	

●    The word “only” can be tricky depending on its presence in the
     statement.	

       ■      X loves only Y ⇔ If X loves anything, then that thing is Y.	

       ■      Only X loves Y ⇔ If anything loves Y, then it is X.	

       ■      X only loves Y ⇔ If X does anything to Y, then it is love.	

●    Example for forming symbolic forms from predicate symbols	

       ■      D(x) is “x is dog”; R(x) is “x is a rabbit”; C(x,y) is “x chases y”	

       ■      All dogs chase all rabbits ⇔ For anything, if it is a dog, then for any
              other thing, if it is a rabbit, then the dog chases it ⇔ (∀x)[D(x) → (∀y)(R
              (y) → C(x,y)]	

       ■      Some dogs chase all rabbits ⇔ There is something that is a dog and for
              any other thing, if that thing is a rabbit, then the dog chases it ⇔ (∃x)[D
              (x) Λ (∀y)(R(y) → C(x,y)]	

       ■      Only dogs chase rabbits ⇔ For any two things, if one is a rabbit and the
              other chases it, then the other is a dog ⇔ (∀y) (∀x)[R(y) Λ C(x,y) → D
              (x)]	

Section 1.3                              Quantifiers, Predicates and Validity          8
Negation of statements
   ●     A(x): Everything is fun	

   ●     Negation will be “it is false that everything is fun,” i.e. “something
         is nonfun.” 	

   ●     In symbolic form, [(∀x)A(x)]ʹ′ ⇔ (∃x)[A(x)]ʹ′ 	

   ●     Similarly negation of “Something is fun” is “Nothing is fun” or
         “Everything is boring.”	

   ●     Hence, [(∃x)A(x)]ʹ′ ⇔ (∀x)[A(x)]ʹ′ 	





Section 1.3                         Quantifiers, Predicates and Validity          9
Class Exercise
     ●        What is the negation of “Everybody loves somebody sometime.”	

               ■    Everybody hates somebody sometime	

               ■    Somebody loves everybody all the time	

               ■    Everybody hates everybody all the time	

               ■    Somebody hates everybody all the time	

                 √

     ●        What is the negation of the following statements?	

     ●        Some pictures are old and faded.       	

	

                  Every picture is neither old nor faded.	

     ●        All people are tall and thin. 	

      	

	

                  Someone is short or fat.	

     ●        Some students eat only pizza.   	

    	

	

                  Every student eats something that is not pizza.	

     ●        Only students eat pizza.        	

    	

	

                 There is a non-student who eats pizza.	


Section 1.3                                 Quantifiers, Predicates and Validity   10
Class exercise
     ●        S(x): x is a student; I(x): x is intelligent; M(x): x likes music	

     ●        Write wffs that express the following statements:	

               ■    All students are intelligent.	

                     For anything, if it is a student, then it is intelligent ⇔
                     (∀x)[S(x) → I (x)]
               ■    Some intelligent students like music.	

                     There is something that is intelligent and it is a student and it likes music ⇔
                     (∃x)[I(x) Λ S(x) Λ M(x)]
               ■    Everyone who likes music is a stupid student.	


               For anything, if that thing likes music, then it is a student and it is not intelligent ⇔
                (∀x)(M(x) → S(x) Λ [I (x)]ʹ′)

               ■    Only intelligent students like music.	

                    For any thing, if it likes music, then it is a student and it is intelligent ⇔
                    (∀x)(M(x) → S(x) Λ I(x))

Section 1.3                                     Quantifiers, Predicates and Validity                 11
Validity
     ●        Analogous to a tautology of propositional logic.	

     ●        Truth of a predicate wff depends on the interpretation.	

     ●        A predicate wff is valid if it is true in all possible interpretations
              just like a propositional wff is true if it is true for all rows of the
              truth table.	

     ●        A valid predicate wff is intrinsically true.	


                                        Propositional Wffs	

                            Predicate Wffs	


               Truth values	

 True or false – depends on                   True, false or neither (if the
                               the truth value of statement                 wff has a free variable)	

                               letters	

              Intrinsic truth	

 Tautology – true for all truth             Valid wff – true for all
                                 values of its statements	

                interpretations	

              Methodology	

 Truth table to determine if it                 No algorithm to determine
                             is a tautology	

                              validity	



Section 1.3                                       Quantifiers, Predicates and Validity                       12
Validity examples
     ●        (∀x)P(x) → (∃x)P(x)	

               ■    This is valid because if every object of the domain has a certain
                    property, then there exists an object of the domain that has the
                    same property.	

     ●        (∀x)P(x) → P(a)	

               ■    Valid – quite obvious since is a member of the domain of x.	

     ●        (∃x)P(x) → (∀x)P(x)	

               ■    Not valid since the property cannot be valid for all objects in the
                    domain if it is valid for some objects of than domain. Can use a
                    mathematical context to check as well. 	

               ■    Say P(x) = “x is even,” then there exists an integer that is even but
                    not every integer is even.	

     ●        (∀x)[P(x) V Q(x)] → (∀x)P(x) V (∀x)Q(x) 	

               ■    Invalid, can prove by mathematical context by taking P(x) = x is
                    even and Q(x) = x is odd. 	

               ■    In that case, the hypothesis is true but the conclusion is false
                    because it is not the case that every integer is even or that every
                    integer is odd.	

Section 1.3                                  Quantifiers, Predicates and Validity           13
Class Exercise
     ●        What is the truth of the following wffs where the domain consists
              of integers:	

     ●        (∀x)[L(x) → O(x)] where O(x) is “x is odd” and L(x) is “x < 10”?	

     ●        (∃y)(∀x)(x + y = 0)?	

     ●        (∃y)(∃x)(x2 = y)?	

     ●        (∀x)[x < 0 → (∃y)(y > 0 Λ x + y = 0)]?	


     ●        Using predicate symbols and appropriate quantifiers, write the
              symbolic form of the following English statement:	

     ●        D(x) is “x is a day” 	

M is “Monday”	

           	

T is “Tuesday”	

     ●        S(x) is “x is sunny” 	

       	

R(x) is “x is rainy”	

     ●        Some days are sunny and rainy.	

     ●        It is always a sunny day only if it is a rainy day.	

     ●        It rained both Monday and Tuesday.	

     ●        Every day that is rainy is not sunny.	


Section 1.3                                Quantifiers, Predicates and Validity         14

Más contenido relacionado

La actualidad más candente

Lesson 13: Exponential and Logarithmic Functions (slides)
Lesson 13: Exponential and Logarithmic Functions (slides)Lesson 13: Exponential and Logarithmic Functions (slides)
Lesson 13: Exponential and Logarithmic Functions (slides)Matthew Leingang
 
1.1 Linear Equations
1.1 Linear Equations1.1 Linear Equations
1.1 Linear Equationssmiller5
 
Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Matthew Leingang
 
5 3 Partial Fractions
5 3 Partial Fractions5 3 Partial Fractions
5 3 Partial Fractionssilvia
 
Integration presentation
Integration presentationIntegration presentation
Integration presentationUrmila Bhardwaj
 
Area Under the Curve
Area Under the CurveArea Under the Curve
Area Under the Curvealexbeja
 
Mathematical Logic
Mathematical LogicMathematical Logic
Mathematical LogicJoey Valdriz
 
CMSC 56 | Lecture 2: Propositional Equivalences
CMSC 56 | Lecture 2: Propositional EquivalencesCMSC 56 | Lecture 2: Propositional Equivalences
CMSC 56 | Lecture 2: Propositional Equivalencesallyn joy calcaben
 
Integration By Parts Tutorial & Example- Calculus 2
Integration By Parts Tutorial & Example- Calculus 2Integration By Parts Tutorial & Example- Calculus 2
Integration By Parts Tutorial & Example- Calculus 2empoweringminds
 
The chain rule
The chain ruleThe chain rule
The chain ruleJ M
 
The disk method
The disk methodThe disk method
The disk methodRon Eick
 
Discrete Structure Mathematics lecture 1
Discrete Structure Mathematics lecture 1Discrete Structure Mathematics lecture 1
Discrete Structure Mathematics lecture 1Amr Rashed
 
Linear differential equation with constant coefficient
Linear differential equation with constant coefficientLinear differential equation with constant coefficient
Linear differential equation with constant coefficientSanjay Singh
 
Polynomials by nikund
Polynomials by nikundPolynomials by nikund
Polynomials by nikundsheshank jain
 
Math lecture 10 (Introduction to Integration)
Math lecture 10 (Introduction to Integration)Math lecture 10 (Introduction to Integration)
Math lecture 10 (Introduction to Integration)Osama Zahid
 
Application of definite integrals
Application of definite integralsApplication of definite integrals
Application of definite integralsVaibhav Tandel
 

La actualidad más candente (20)

Lesson 13: Exponential and Logarithmic Functions (slides)
Lesson 13: Exponential and Logarithmic Functions (slides)Lesson 13: Exponential and Logarithmic Functions (slides)
Lesson 13: Exponential and Logarithmic Functions (slides)
 
Integral calculus
Integral calculusIntegral calculus
Integral calculus
 
1.1 Linear Equations
1.1 Linear Equations1.1 Linear Equations
1.1 Linear Equations
 
Continuity
ContinuityContinuity
Continuity
 
Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)
 
5 3 Partial Fractions
5 3 Partial Fractions5 3 Partial Fractions
5 3 Partial Fractions
 
Integration presentation
Integration presentationIntegration presentation
Integration presentation
 
Area Under the Curve
Area Under the CurveArea Under the Curve
Area Under the Curve
 
Mathematical Logic
Mathematical LogicMathematical Logic
Mathematical Logic
 
CMSC 56 | Lecture 2: Propositional Equivalences
CMSC 56 | Lecture 2: Propositional EquivalencesCMSC 56 | Lecture 2: Propositional Equivalences
CMSC 56 | Lecture 2: Propositional Equivalences
 
Rules of derivative
Rules of derivativeRules of derivative
Rules of derivative
 
Integration By Parts Tutorial & Example- Calculus 2
Integration By Parts Tutorial & Example- Calculus 2Integration By Parts Tutorial & Example- Calculus 2
Integration By Parts Tutorial & Example- Calculus 2
 
The chain rule
The chain ruleThe chain rule
The chain rule
 
The disk method
The disk methodThe disk method
The disk method
 
Discrete Structure Mathematics lecture 1
Discrete Structure Mathematics lecture 1Discrete Structure Mathematics lecture 1
Discrete Structure Mathematics lecture 1
 
Linear differential equation with constant coefficient
Linear differential equation with constant coefficientLinear differential equation with constant coefficient
Linear differential equation with constant coefficient
 
Radian measure
Radian measureRadian measure
Radian measure
 
Polynomials by nikund
Polynomials by nikundPolynomials by nikund
Polynomials by nikund
 
Math lecture 10 (Introduction to Integration)
Math lecture 10 (Introduction to Integration)Math lecture 10 (Introduction to Integration)
Math lecture 10 (Introduction to Integration)
 
Application of definite integrals
Application of definite integralsApplication of definite integrals
Application of definite integrals
 

Similar a CPSC 125 Ch 1 sec 3

Discrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdfDiscrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdfMuhammadUmerIhtisham
 
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
 
Chapter 01 - p2.pdf
Chapter 01 - p2.pdfChapter 01 - p2.pdf
Chapter 01 - p2.pdfsmarwaneid
 
1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.ppt1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.pptTejasAditya2
 
Discreate structure presentation introduction
Discreate structure presentation introductionDiscreate structure presentation introduction
Discreate structure presentation introductionyashirraza123
 
Probability and statistics - Discrete Random Variables and Probability Distri...
Probability and statistics - Discrete Random Variables and Probability Distri...Probability and statistics - Discrete Random Variables and Probability Distri...
Probability and statistics - Discrete Random Variables and Probability Distri...Asma CHERIF
 
Unit 1 quantifiers
Unit 1  quantifiersUnit 1  quantifiers
Unit 1 quantifiersraksharao
 

Similar a CPSC 125 Ch 1 sec 3 (20)

Discrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdfDiscrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdf
 
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
 
3 fol examples v2
3 fol examples v23 fol examples v2
3 fol examples v2
 
Formal Logic - Lesson 8 - Predicates and Quantifiers
Formal Logic - Lesson 8 - Predicates and QuantifiersFormal Logic - Lesson 8 - Predicates and Quantifiers
Formal Logic - Lesson 8 - Predicates and Quantifiers
 
Quantifier
QuantifierQuantifier
Quantifier
 
PredicateLogic (1).ppt
PredicateLogic (1).pptPredicateLogic (1).ppt
PredicateLogic (1).ppt
 
PredicateLogic.pptx
PredicateLogic.pptxPredicateLogic.pptx
PredicateLogic.pptx
 
Per3 logika
Per3 logikaPer3 logika
Per3 logika
 
Chapter 01 - p2.pdf
Chapter 01 - p2.pdfChapter 01 - p2.pdf
Chapter 01 - p2.pdf
 
Module_5_1.pptx
Module_5_1.pptxModule_5_1.pptx
Module_5_1.pptx
 
1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.ppt1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.ppt
 
Discreate structure presentation introduction
Discreate structure presentation introductionDiscreate structure presentation introduction
Discreate structure presentation introduction
 
Predicates Logic.pptx
Predicates Logic.pptxPredicates Logic.pptx
Predicates Logic.pptx
 
Quantification
QuantificationQuantification
Quantification
 
Ai quantifiers
Ai quantifiersAi quantifiers
Ai quantifiers
 
Probability and statistics - Discrete Random Variables and Probability Distri...
Probability and statistics - Discrete Random Variables and Probability Distri...Probability and statistics - Discrete Random Variables and Probability Distri...
Probability and statistics - Discrete Random Variables and Probability Distri...
 
Predicate &amp; quantifier
Predicate &amp; quantifierPredicate &amp; quantifier
Predicate &amp; quantifier
 
Unit 1 quantifiers
Unit 1  quantifiersUnit 1  quantifiers
Unit 1 quantifiers
 
continuity of module 2.pptx
continuity of module 2.pptxcontinuity of module 2.pptx
continuity of module 2.pptx
 
Course notes2summer2012
Course notes2summer2012Course notes2summer2012
Course notes2summer2012
 

Más de David Wood

Internet of Things (IoT) two-factor authentication using blockchain
Internet of Things (IoT) two-factor authentication using blockchainInternet of Things (IoT) two-factor authentication using blockchain
Internet of Things (IoT) two-factor authentication using blockchainDavid Wood
 
Returning to Online Privacy?
Returning to Online Privacy?Returning to Online Privacy?
Returning to Online Privacy?David Wood
 
Methods for Securing Spacecraft Tasking and Control via an Enterprise Ethereu...
Methods for Securing Spacecraft Tasking and Control via an Enterprise Ethereu...Methods for Securing Spacecraft Tasking and Control via an Enterprise Ethereu...
Methods for Securing Spacecraft Tasking and Control via an Enterprise Ethereu...David Wood
 
BlockSW 2019 Keynote
BlockSW 2019 KeynoteBlockSW 2019 Keynote
BlockSW 2019 KeynoteDavid Wood
 
Returning to Online Privacy - W3C/ANU Future of the Web Roadshow 20190221
Returning to Online Privacy - W3C/ANU Future of the Web Roadshow 20190221Returning to Online Privacy - W3C/ANU Future of the Web Roadshow 20190221
Returning to Online Privacy - W3C/ANU Future of the Web Roadshow 20190221David Wood
 
Privacy in the Smart City
Privacy in the Smart CityPrivacy in the Smart City
Privacy in the Smart CityDavid Wood
 
Controlling Complexities in Software Development
Controlling Complexities in Software DevelopmentControlling Complexities in Software Development
Controlling Complexities in Software DevelopmentDavid Wood
 
Privacy Concerns related to Verifiable Claims
Privacy Concerns related to Verifiable ClaimsPrivacy Concerns related to Verifiable Claims
Privacy Concerns related to Verifiable ClaimsDavid Wood
 
Implementing the Verifiable Claims data model
Implementing the Verifiable Claims data modelImplementing the Verifiable Claims data model
Implementing the Verifiable Claims data modelDavid Wood
 
So You Wanna be a Startup CTO 20170301
So You Wanna be a Startup CTO 20170301So You Wanna be a Startup CTO 20170301
So You Wanna be a Startup CTO 20170301David Wood
 
Functional manipulations of large data graphs 20160601
Functional manipulations of large data graphs 20160601Functional manipulations of large data graphs 20160601
Functional manipulations of large data graphs 20160601David Wood
 
When Metaphors Kill
When Metaphors KillWhen Metaphors Kill
When Metaphors KillDavid Wood
 
Secularism in Australia
Secularism in AustraliaSecularism in Australia
Secularism in AustraliaDavid Wood
 
Meditations on Writing in Paradoxes, Oxymorons, and Pleonasms
Meditations on Writing in Paradoxes, Oxymorons, and PleonasmsMeditations on Writing in Paradoxes, Oxymorons, and Pleonasms
Meditations on Writing in Paradoxes, Oxymorons, and PleonasmsDavid Wood
 
Building a writer's platform with social media
Building a writer's platform with social mediaBuilding a writer's platform with social media
Building a writer's platform with social mediaDavid Wood
 
Summary of the Hero's Journey
Summary of the Hero's JourneySummary of the Hero's Journey
Summary of the Hero's JourneyDavid Wood
 
Open by Default
Open by DefaultOpen by Default
Open by DefaultDavid Wood
 
Lod Then, Now and Next 20110926
Lod Then, Now and Next 20110926Lod Then, Now and Next 20110926
Lod Then, Now and Next 20110926David Wood
 
Linked Data ROI 20110426
Linked Data ROI 20110426Linked Data ROI 20110426
Linked Data ROI 20110426David Wood
 
Introduction to Linked Data: RDF Vocabularies
Introduction to Linked Data: RDF VocabulariesIntroduction to Linked Data: RDF Vocabularies
Introduction to Linked Data: RDF VocabulariesDavid Wood
 

Más de David Wood (20)

Internet of Things (IoT) two-factor authentication using blockchain
Internet of Things (IoT) two-factor authentication using blockchainInternet of Things (IoT) two-factor authentication using blockchain
Internet of Things (IoT) two-factor authentication using blockchain
 
Returning to Online Privacy?
Returning to Online Privacy?Returning to Online Privacy?
Returning to Online Privacy?
 
Methods for Securing Spacecraft Tasking and Control via an Enterprise Ethereu...
Methods for Securing Spacecraft Tasking and Control via an Enterprise Ethereu...Methods for Securing Spacecraft Tasking and Control via an Enterprise Ethereu...
Methods for Securing Spacecraft Tasking and Control via an Enterprise Ethereu...
 
BlockSW 2019 Keynote
BlockSW 2019 KeynoteBlockSW 2019 Keynote
BlockSW 2019 Keynote
 
Returning to Online Privacy - W3C/ANU Future of the Web Roadshow 20190221
Returning to Online Privacy - W3C/ANU Future of the Web Roadshow 20190221Returning to Online Privacy - W3C/ANU Future of the Web Roadshow 20190221
Returning to Online Privacy - W3C/ANU Future of the Web Roadshow 20190221
 
Privacy in the Smart City
Privacy in the Smart CityPrivacy in the Smart City
Privacy in the Smart City
 
Controlling Complexities in Software Development
Controlling Complexities in Software DevelopmentControlling Complexities in Software Development
Controlling Complexities in Software Development
 
Privacy Concerns related to Verifiable Claims
Privacy Concerns related to Verifiable ClaimsPrivacy Concerns related to Verifiable Claims
Privacy Concerns related to Verifiable Claims
 
Implementing the Verifiable Claims data model
Implementing the Verifiable Claims data modelImplementing the Verifiable Claims data model
Implementing the Verifiable Claims data model
 
So You Wanna be a Startup CTO 20170301
So You Wanna be a Startup CTO 20170301So You Wanna be a Startup CTO 20170301
So You Wanna be a Startup CTO 20170301
 
Functional manipulations of large data graphs 20160601
Functional manipulations of large data graphs 20160601Functional manipulations of large data graphs 20160601
Functional manipulations of large data graphs 20160601
 
When Metaphors Kill
When Metaphors KillWhen Metaphors Kill
When Metaphors Kill
 
Secularism in Australia
Secularism in AustraliaSecularism in Australia
Secularism in Australia
 
Meditations on Writing in Paradoxes, Oxymorons, and Pleonasms
Meditations on Writing in Paradoxes, Oxymorons, and PleonasmsMeditations on Writing in Paradoxes, Oxymorons, and Pleonasms
Meditations on Writing in Paradoxes, Oxymorons, and Pleonasms
 
Building a writer's platform with social media
Building a writer's platform with social mediaBuilding a writer's platform with social media
Building a writer's platform with social media
 
Summary of the Hero's Journey
Summary of the Hero's JourneySummary of the Hero's Journey
Summary of the Hero's Journey
 
Open by Default
Open by DefaultOpen by Default
Open by Default
 
Lod Then, Now and Next 20110926
Lod Then, Now and Next 20110926Lod Then, Now and Next 20110926
Lod Then, Now and Next 20110926
 
Linked Data ROI 20110426
Linked Data ROI 20110426Linked Data ROI 20110426
Linked Data ROI 20110426
 
Introduction to Linked Data: RDF Vocabularies
Introduction to Linked Data: RDF VocabulariesIntroduction to Linked Data: RDF Vocabularies
Introduction to Linked Data: RDF Vocabularies
 

Último

Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsRavi Sanghani
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesThousandEyes
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Manual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance AuditManual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance AuditSkynet Technologies
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Alkin Tezuysal
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI AgeCprime
 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterMydbops
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfNeo4j
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...Wes McKinney
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Strongerpanagenda
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality AssuranceInflectra
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesKari Kakkonen
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rick Flair
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Hiroshi SHIBATA
 
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...Scott Andery
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityIES VE
 

Último (20)

Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and Insights
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Manual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance AuditManual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance Audit
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI Age
 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL Router
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examples
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024
 
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a reality
 

CPSC 125 Ch 1 sec 3

  • 1. Formal Logic Mathematical Structures for Computer Science Chapter 1 Copyright © 2006 W.H. Freeman & Co. MSCS Slides Formal Logic
  • 2. Variables and Statements ●  Variables in Logic ●  A variable is a symbol that stands for an individual in a collection or set. For example, the variable x may stand for one of the days. We may let x = Monday or x = Tuesday, etc. ●  Incomplete Statements ●  A sentence containing a variable is called an incomplete statement. An incomplete statement is about the individuals in a definite domain or set. When we replace the variable by the name of an individual in the set we obtain a statement about that individual. ■  Example of an incomplete statement : “x has 30 days.” ●  Here, x can be any month and substituting that, we will get a complete statement. Section 1.3 Quantifiers, Predicates and Validity 1
  • 3. Quantifiers and Predicates ●  Quantifiers: ●  Quantifiers are phrases that refer to given quantities, such as “for some” or “for all” or “for every,” indicating how many objects have a certain property. ●  Two kinds of quantifiers: Universal and Existential ●  Universal Quantifier: represented by ∀ ●  The symbol is translated as and means “for all”, “given any”, “for each,” or “for every,” and is known as the universal quantifier. ●  Existential Quantifier: represented by ∃ ●  The symbol is translated as and means variously “for some,” “there exists,” “there is a,” or “for at least one”. Section 1.3 Quantifiers, Predicates and Validity 2
  • 4. Quantifiers and Predicates ●  Predicate ●  It is the verbal statement that describes the property of a variable. Usually represented by the letter P, the notation P(x) is used to represent some unspecified property or predicate that x may have ■  e.g. P(x) = x has 30 days. ■  P(April) = April has 30 days. ●  Combining the quantifier and the predicate, we get a complete statement of the form ∀(x)P(x) or ∃(x)P(x). ●  The collection of objects that satisfy the property P(x) is called the domain of interpretation. ●  Truth value of expressions formed using quantifiers and predicates ■  What is the truth value of ∀(x)P(x) where x is all the months and P(x) = x has less than 32 days. ■  Undoubtedly, the above is true since no month has 32 days. Section 1.3 Quantifiers, Predicates and Validity 3
  • 5. Truth value of the following expressions ●  Truth of expression ∀(x)P(x) 1.  P(x) is the property that x is yellow, and the domain of interpretation is the collection of all flowers: not true 2.  P(x) is the property that x is a plant, and the domain of interpretation is the collection of all flowers: true 3.  P(x) is the property that x is positive, and the domain of interpretation consists of integers: not true ■  Can you find one interpretation in which both ∀(x)P(x) is true and ∃(x)P(x) is false? Not possible ■  Can you find one interpretation in which both ∃(x)P(x) is true and ∀(x)P(x) is false? Case 1 as mentioned above ●  Predicates involving properties of single variables : unary predicates ●  Binary, ternary and n-ary predicates are also possible. ■  (∀x) (∃y)Q(x,y) is a binary predicate. This expression reads as “for every x there exists a y such that Q(x,y).” Section 1.3 Quantifiers, Predicates and Validity 4
  • 6. Interpretation ●  Formal definition: An interpretation for an expression involving predicates consists of the following: A collection of objects, called the domain of interpretation, which must include at least one object. An assignment of a property of the objects in the domain to each predicate in the expression. An assignment of a particular object in the domain to each constant symbol in the expression. ●  Predicate wffs can be built similar to propositional wffs using logical connectives with predicates and quantifiers. ●  Examples of predicate wffs ■  (∀x)[P(x) → Q(x)] ■  (∀x) ((∃y)[P(x,y) V Q(x,y)] → R(x)) ■  S(x,y) Λ R(x,y) Section 1.3 Quantifiers, Predicates and Validity 5
  • 7. Scope of a variable in an expression ●  Brackets are used wisely to identify the scope of the variable. ■  (∀x) [∃(y)[P(x,y) V Q(x,y)] → R(x)] ■  Scope of (∃y) is P(x,y) V Q(x,y) while the scope of (∀x) is the entire expression. ■  (∀x)S(x) V (∃y)R(y) ■  Scope of x is S(x) while the scope of y is R(y). ■  (∀x)[P(x,y) → (∃y) Q(x,y)] ■  Scope of variable y is not defined for P(x,y) hence y is called a free variable. Such expressions might not have a truth value at all. ●  What is the truth of the expression ■  ∃(x)[A(x) Λ ∀(y)[B(x,y) → C(y)]] in the interpretation ■  A(x) is “x > 0” , B(x, y) is “x > y” and C(y) is “y ≤ 0” where the domain of x is positive integers and the domain of y is all integers True, x=1 is a positive integer and any integer less than x is ≤ 0 Section 1.3 Quantifiers, Predicates and Validity 6
  • 8. Translation: Verbal statements to symbolic form ●  “Every person is nice” can be rephrased as “For any thing, if it is a person, then it is nice.” So, if P(x) is “x is a person” and Q (x) be “x is nice,” the statement can be symbolized as ■  (∀x)[P(x) → Q(x)] ■  “All persons are nice” or “Each person is nice” will also have the same symbolic form. ●  “There is a nice person” can be rewritten as “There exists something that is both a person and nice.” ■  In symbolic form, (∃x)[P(x) Λ Q(x)]. ■  Variations: “Some persons are nice” or “There are nice persons.” ●  What would the following form mean for the example above? (∃x)[P(x) → Q(x)] ■  This will only be true if there are no persons in the world but that is not the case. ■  Hence such a statement is false, so almost always, ∃ goes with Λ (conjunction) and ∀ goes with → (implication). Section 1.3 Quantifiers, Predicates and Validity 7
  • 9. Translation ●  Hint: Avoid confusion by framing the statement in different forms as possible. ●  The word “only” can be tricky depending on its presence in the statement. ■  X loves only Y ⇔ If X loves anything, then that thing is Y. ■  Only X loves Y ⇔ If anything loves Y, then it is X. ■  X only loves Y ⇔ If X does anything to Y, then it is love. ●  Example for forming symbolic forms from predicate symbols ■  D(x) is “x is dog”; R(x) is “x is a rabbit”; C(x,y) is “x chases y” ■  All dogs chase all rabbits ⇔ For anything, if it is a dog, then for any other thing, if it is a rabbit, then the dog chases it ⇔ (∀x)[D(x) → (∀y)(R (y) → C(x,y)] ■  Some dogs chase all rabbits ⇔ There is something that is a dog and for any other thing, if that thing is a rabbit, then the dog chases it ⇔ (∃x)[D (x) Λ (∀y)(R(y) → C(x,y)] ■  Only dogs chase rabbits ⇔ For any two things, if one is a rabbit and the other chases it, then the other is a dog ⇔ (∀y) (∀x)[R(y) Λ C(x,y) → D (x)] Section 1.3 Quantifiers, Predicates and Validity 8
  • 10. Negation of statements ●  A(x): Everything is fun ●  Negation will be “it is false that everything is fun,” i.e. “something is nonfun.” ●  In symbolic form, [(∀x)A(x)]ʹ′ ⇔ (∃x)[A(x)]ʹ′ ●  Similarly negation of “Something is fun” is “Nothing is fun” or “Everything is boring.” ●  Hence, [(∃x)A(x)]ʹ′ ⇔ (∀x)[A(x)]ʹ′ Section 1.3 Quantifiers, Predicates and Validity 9
  • 11. Class Exercise ●  What is the negation of “Everybody loves somebody sometime.” ■  Everybody hates somebody sometime ■  Somebody loves everybody all the time ■  Everybody hates everybody all the time ■  Somebody hates everybody all the time √ ●  What is the negation of the following statements? ●  Some pictures are old and faded. Every picture is neither old nor faded. ●  All people are tall and thin. Someone is short or fat. ●  Some students eat only pizza. Every student eats something that is not pizza. ●  Only students eat pizza. There is a non-student who eats pizza. Section 1.3 Quantifiers, Predicates and Validity 10
  • 12. Class exercise ●  S(x): x is a student; I(x): x is intelligent; M(x): x likes music ●  Write wffs that express the following statements: ■  All students are intelligent. For anything, if it is a student, then it is intelligent ⇔ (∀x)[S(x) → I (x)] ■  Some intelligent students like music. There is something that is intelligent and it is a student and it likes music ⇔ (∃x)[I(x) Λ S(x) Λ M(x)] ■  Everyone who likes music is a stupid student. For anything, if that thing likes music, then it is a student and it is not intelligent ⇔ (∀x)(M(x) → S(x) Λ [I (x)]ʹ′) ■  Only intelligent students like music. For any thing, if it likes music, then it is a student and it is intelligent ⇔ (∀x)(M(x) → S(x) Λ I(x)) Section 1.3 Quantifiers, Predicates and Validity 11
  • 13. Validity ●  Analogous to a tautology of propositional logic. ●  Truth of a predicate wff depends on the interpretation. ●  A predicate wff is valid if it is true in all possible interpretations just like a propositional wff is true if it is true for all rows of the truth table. ●  A valid predicate wff is intrinsically true. Propositional Wffs Predicate Wffs Truth values True or false – depends on True, false or neither (if the the truth value of statement wff has a free variable) letters Intrinsic truth Tautology – true for all truth Valid wff – true for all values of its statements interpretations Methodology Truth table to determine if it No algorithm to determine is a tautology validity Section 1.3 Quantifiers, Predicates and Validity 12
  • 14. Validity examples ●  (∀x)P(x) → (∃x)P(x) ■  This is valid because if every object of the domain has a certain property, then there exists an object of the domain that has the same property. ●  (∀x)P(x) → P(a) ■  Valid – quite obvious since is a member of the domain of x. ●  (∃x)P(x) → (∀x)P(x) ■  Not valid since the property cannot be valid for all objects in the domain if it is valid for some objects of than domain. Can use a mathematical context to check as well. ■  Say P(x) = “x is even,” then there exists an integer that is even but not every integer is even. ●  (∀x)[P(x) V Q(x)] → (∀x)P(x) V (∀x)Q(x) ■  Invalid, can prove by mathematical context by taking P(x) = x is even and Q(x) = x is odd. ■  In that case, the hypothesis is true but the conclusion is false because it is not the case that every integer is even or that every integer is odd. Section 1.3 Quantifiers, Predicates and Validity 13
  • 15. Class Exercise ●  What is the truth of the following wffs where the domain consists of integers: ●  (∀x)[L(x) → O(x)] where O(x) is “x is odd” and L(x) is “x < 10”? ●  (∃y)(∀x)(x + y = 0)? ●  (∃y)(∃x)(x2 = y)? ●  (∀x)[x < 0 → (∃y)(y > 0 Λ x + y = 0)]? ●  Using predicate symbols and appropriate quantifiers, write the symbolic form of the following English statement: ●  D(x) is “x is a day” M is “Monday” T is “Tuesday” ●  S(x) is “x is sunny” R(x) is “x is rainy” ●  Some days are sunny and rainy. ●  It is always a sunny day only if it is a rainy day. ●  It rained both Monday and Tuesday. ●  Every day that is rainy is not sunny. Section 1.3 Quantifiers, Predicates and Validity 14