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# Propositional And First-Order Logic

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### Propositional And First-Order Logic

1. 1. Propositional andFirst-Order Logic 1
2. 2. Propositional Logic 2
3. 3. Propositional logic• Proposition : A proposition is classified as a declarative sentence which is either true or false. eg: 1) It rained yesterday.• Propositional symbols/variables: P, Q, S, ... (atomic sentences)• Sentences are combined by Connectives: ∧ ...and [conjunction] ∨ ...or [disjunction] ⇒ ...implies [implication / conditional] ⇔ ..is equivalent [biconditional] ¬ ...not [negation]• Literal: atomic sentence or negated atomic sentence 3
4. 4. Propositional logic (PL) Sentence or well formed formula• A sentence (well formed formula) is defined as follows: – A symbol is a sentence – If S is a sentence, then ¬S is a sentence – If S is a sentence, then (S) is a sentence – If S and T are sentences, then (S ∨ T), (S ∧ T), (S → T), and (S ↔ T) are sentences – A sentence results from a finite number of applications of the above rules 5
5. 5. Laws of Algebra of Propositions• Idempotent: pVp≡p pΛp≡p• Commutative: pVq≡qVp pΛq≡qΛp• Complement: p V ~p ≡ T p Λ ~p ≡ F• Double Negation: ~(~p) ≡ p 7
6. 6. • Associative: p V (q V r) ≡ (p V q) V r p Λ (q Λ r) ≡ (p Λ q) Λ r• Distributive: p V (q Λ r) ≡ (p V q) Λ (p V r) p Λ (q V r) ≡ (p Λ q) V (p Λ r)• Absorbtion: p V (p Λ q) ≡ p p Λ (p V q) ≡ p• Identity: pVT≡T pΛT≡p pVF≡p pΛF≡F 8
7. 7. • De Morgan’s ~(p V q) ≡ ~p Λ ~q ~(p Λ q) ≡ ~p V ~q• Equivalence of Contrapositive: p → q ≡ ~q → ~p• Others: p → q ≡ ~p V q p ↔ q ≡ (p → q) Λ (q → p) 9
8. 8. Tautologies and contradictions• A tautology is a sentence that is True under all interpretations.• An contradiction is a sentence that is False under all interpretations. p ¬p p ∨¬p p ¬p p ∧¬p F T T F T F T F T T F F 10 L3
9. 9. Tautology by truth tablep q ¬p p ∨q ¬p ∧(p ∨q ) [¬p ∧(p ∨q )]→qT T F T F TT F F T F TF T T T T TF F T F F T 11 L3
10. 10. Propositional Logic - one last proof Show that [p ∧ (p → q)] → q is a tautology. We use ≡ to show that [p ∧ (p → q)] → q ≡ T.[p ∧ (p → q)] → q ≡ [p ∧ (¬p ∨ q)] → q substitution for → ≡ [(p ∧ ¬p) ∨ (p ∧ q)] → q distributive ≡ [ F ∨ (p ∧ q)] → q complement ≡ (p ∧ q) → q identity ≡ ¬(p ∧ q) ∨ q substitution for → ≡ (¬p ∨ ¬q) ∨ q DeMorgan’s ≡ ¬p ∨ (¬q ∨ q ) associative ≡ ¬p ∨ T complement ≡T identity 11/09/12 12
11. 11. Logical Equivalence of Conditional and ContrapositiveThe easiest way to check for logical equivalence is to see if the truth tables of both variants have identical last columns: p q p →q p q ¬q ¬p ¬q→¬p T T T T T F F T T F F T F T F F F T T F T F T T F F T F F T T T 13 L3
12. 12. Table of Logical Equivalences• Identity laws Like adding 0• Domination laws Like multiplying by 0• Idempotent laws Delete redundancies• Double negation “I don’t like you, not”• Commutativity Like “x+y = y+x”• Associativity Like “(x+y)+z = y+(x+z)”• Distributivity Like “(x+y)z = xz+yz” 14• De Morgan L3
13. 13. Table of Logical Equivalences• Excluded middle• Negating creates opposite• Definition of implication in terms of Not and Or 15 L3
14. 14. Inference rules• Logical inference is used to create new sentences that logically follow from a given set of predicate calculus sentences (KB). 16
15. 15. Sound rules of inference• Here are some examples of sound rules of inference – A rule is sound if its conclusion is true whenever the premise is true• Each can be shown to be sound using a truth table RULE PREMISE CONCLUSION Modus Ponens A, A → B B And Introduction/Conjuction A, B A∧B And Elimination/SimplificationA ∧ B A Double Negation ¬¬A A Unit Resolution A ∨ B, ¬B A Resolution A ∨ B, ¬B ∨ C A∨ C 17
16. 16. Soundness of modus ponens A B A→B OK?True True True √True False False √False True True √False False True √ 18
17. 17. Soundness of theresolution inference rule 19
18. 18. Proving things• A proof is a sequence of sentences, where each sentence is either a premise or a sentence derived from earlier sentences in the proof by one of the rules of inference.• The last sentence is the theorem (also called goal or query) that we want to prove.• Example for the “weather problem” given above. 1 Hu Premise “It is humid” 2 Hu→Ho Premise “If it is humid, it is hot” 3 Ho Modus Ponens(1,2) “It is hot” 4 (Ho∧Hu)→R Premise “If it’s hot & humid, it’s raining” 5 Ho∧Hu And Introduction(1,3) “It is hot and humid” 6R Modus Ponens(4,5) “It is raining” 20
19. 19. Problems with Propositional Logic 21
20. 20. Propositional logic is a weak language• Hard to identify “individuals” (e.g., Mary, 3)• Can’t directly talk about properties of individuals or relations between individuals (e.g., “Bill is tall”)• Generalizations, patterns, regularities can’t easily be represented (e.g., “all triangles have 3 sides”)• First-Order Logic (abbreviated FOL or FOPC) is expressive enough to concisely represent this kind of information FOL adds relations, variables, and quantifiers, e.g., • “Every elephant is gray”: ∀ x (elephant(x) → gray(x)) • “There is a white alligator”: ∃ x (alligator(X) ^ white(X)) 22
21. 21. First-Order Logic 23
22. 22. First-order logic• First-order logic (FOL) models the world in terms of – Objects, which are things with individual identities – Properties of objects that distinguish them from other objects – Relations that hold among sets of objects – Functions, which are a subset of relations where there is only one “value” for any given “input”• Examples: – Objects: Students, lectures, companies, cars ... – Relations: Brother-of, bigger-than, outside, part-of, has-color, occurs-after, owns, visits, precedes, ... – Properties: blue, oval, even, large, ... – Functions: father-of, best-friend, second-half, one-more-than ... 24
23. 23. User provides• Constant symbols, which represent individuals in the world – Mary –3 – Green• Function symbols, which map individuals to individuals – father-of(Mary) = John – color-of(Sky) = Blue• Predicate symbols, which map individuals to truth values – greater(5,3) – green(Grass) – color(Grass, Green) 25
24. 24. FOL Provides• Variable symbols – E.g., x, y, foo• Connectives – Same as in PL: not (¬), and (∧), or (∨), implies (→), if and only if (biconditional ↔)• Quantifiers – Universal ∀x or (Ax) – Existential ∃x or (Ex) 26
25. 25. Quantifiers• Universal quantification – (∀x)P(x) means that P holds for all values of x in the domain associated with that variable – E.g., (∀x) dolphin(x) → mammal(x)• Existential quantification – (∃ x)P(x) means that P holds for some value of x in the domain associated with that variable – E.g., (∃ x) mammal(x) ∧ lays-eggs(x) – Permits one to make a statement about some object without naming it 27
26. 26. Quantifiers• Universal quantifiers are often used with “implies” to form “rules”: (∀x) student(x) → smart(x) means “All students are smart”• Universal quantification is rarely used to make blanket statements about every individual in the world: (∀x)student(x)∧smart(x) means “Everyone in the world is a student and is smart”• Existential quantifiers are usually used with “and” to specify a list of properties about an individual: (∃x) student(x) ∧ smart(x) means “There is a student who is smart”• A common mistake is to represent this English sentence as the FOL sentence: (∃x) student(x) → smart(x) – But what happens when there is a person who is not a student? 28
27. 27. Quantifier Scope• Switching the order of universal quantifiers does not change the meaning: – (∀x)(∀y)P(x,y) ↔ (∀y)(∀x) P(x,y)• Similarly, you can switch the order of existential quantifiers: – (∃x)(∃y)P(x,y) ↔ (∃y)(∃x) P(x,y)• Switching the order of universals and existentials does change meaning: – Everyone likes someone: (∀x)(∃y) likes(x,y) – Someone is liked by everyone: (∃y)(∀x) likes(x,y) 29
28. 28. Connections between All and Exists We can relate sentences involving ∀ and ∃ using De Morgan’s laws: (∀x) ¬P(x) ↔ ¬(∃x) P(x) ¬(∀x) P ↔ (∃x) ¬P(x) (∀x) P(x) ↔ ¬ (∃x) ¬P(x) (∃x) P(x) ↔ ¬(∀x) ¬P(x) 30
29. 29. Quantified inference rules• Universal instantiation ∀x P(x) ∴ P(A)• Universal generalization – P(A) ∧ P(B) … ∴ ∀x P(x)• Existential instantiation ∃x P(x) ∴P(F)• Existential generalization – P(A) ∴ ∃x P(x) 31
30. 30. Universal instantiation (a.k.a. universal elimination)• If (∀x) P(x) is true, then P(C) is true, where C is any constant in the domain of x• Example: (∀x) eats(Ziggy, x) ⇒ eats(Ziggy, IceCream) 32
31. 31. Translating English to FOLEvery gardener likes the sun. ∀x gardener(x) → likes(x,Sun)You can fool some of the people all of the time. ∃x ∀t person(x) ∧time(t) → can-fool(x,t)You can fool all of the people some of the time. ∀x ∃t (person(x) → time(t) ∧can-fool(x,t)) Equivalent ∀x (person(x) → ∃t (time(t) ∧can-fool(x,t))All purple mushrooms are poisonous. ∀x (mushroom(x) ∧ purple(x)) → poisonous(x)No purple mushroom is poisonous. ¬∃x purple(x) ∧ mushroom(x) ∧ poisonous(x) ∀x (mushroom(x) ∧ purple(x)) → ¬poisonous(x) EquivalentClinton is not tall. ¬tall(Clinton) 33
32. 32. 34