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Debbie Mueller Mathematical Logic Spring 2012
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English sentences take the form Q A B Q is a determiner expression the, every, some, more than, at least, no, etc A is a common noun phrase cube, cat, person, etc B is a verb phrase is, are, eats, etc
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Q A B expresses binary relation between A and B usually the relation is quantitative can sometimes can be expressed with the universal and existential quantifiers as well as truth-functional connectives can express: Nothing, Every, Some, All for those that can’t, supplement FOL with expressions that behave like ∃ and ∀ Generalized Quantifiers
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Another quantification type - Numerical Claims a claim that explicitly uses numbers to say something about the relationship between the A’s and the B’s. FOL does not allow direct talk about numbers, only about elements in the domain of discourse. uses universal and existential quantifiers, together with truth- functional connectives and (most importantly) the identity sign. There are 3 types of claims At least At most Exactly
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At least “n” Requires n quantifiers and non-identity clauses joined by conjunction ex: At least 3 cubes ∃ x ∃ y ∃ z (Cube(x) ∧ Cube(y) ∧ Cube(z) ∧ x ≠ y ∧ y ≠ z ∧ x ≠ z)
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At most “n” Equivalent to less than or equal to Allows there to be no object at all One method: Deny existence of at least n+1 non- identical things ex: There is at most one large thing Denial: There does not exist two (non-identical) large things ~ ∃ x ∃ y(Large(x) ∧ Large(y) ∧ x ≠ y)
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At most “n” con’t Second method: Take n+1 objects. Then at least one pair of them are identical ex: there are at most three large things ∀ w ∀ x ∀ y ∀ z ((Large(w) ∧ Large(x) ∧ Large(y) ∧ Large(z)) → (w=x ∨ w=y ∨ w=z ∨ x=y ∨ x=z ∨ y=z))
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Exactly “n” Similar to no more, no less Conjunction of at least n and at most n ex: At least two cubes ∃ x ∃ y(Cube(x) ∧ Cube(y) ∧ x ≠ y) ∧ ∀ x ∀ y ∀ z ((Cube(x) ∧ Cube(y) ∧ Cube(z)) → (x = y ∨ y = z ∨ x = z)) Compact version ∃ x ∃ y(Cube(x) ∧ Cube(y) ∧ x ≠ y ∧ ∀ z (Cube(z) → (y = z ∨ x = z)))
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Abbreviations for numerical claims Abbreviation scheme: ∃ ≥n x P(x) abbreviates the FOL sentence asserting “There are at least n objects satisfying P(x).” ∃ ≤n x P(x) abbreviates the FOL sentence asserting “There are at most n objects satisfying P(x).” ∃ !n x P(x) abbreviates the FOL sentence asserting “There are exactly n objects satisfying P(x).” For the special case where n = 1, it is customary to write ∃ !x P(x) as a shorthand for ∃ !1 x P(x). This can be read as “there is a unique x such that P(x).”
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The, Both, and Neither “The” combined with a noun phrase forms an expression that suggests to refer to exactly one object called a definite description functions syntactically like names but not semantically does not guarantee a unique object “good” description if there is a unique object can evaluate “bad” description if not Bertrand Russell’s famous Theory of Descriptions (1905 )
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The, Both, and Neither con’t Russell’s Theory of Descriptions a sentence containing a definite description can be thought of as a conjunction with three conjuncts. ex: The cube is small. Russell’s theory: There is at least one cube, and there is at most one cube, and every cube is small. ∃ x Cube(x) ∧ ∀ x ∀ y ((Cube(x) ∧ Cube(y)) → y = x) ∧ ∀ x (Cube(x) → Small(x)) Compact version ∃ x (Cube(x) ∧ ∀ y (Cube(y) → y = x) ∧ Small(x))
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The, Both, and Neither con’t Russell’s analysis can be extended to cover “Both” and “Neither”. “Both” suggests that there are exactly two objects, and each object has the same property. ex: Both cubes are small. Russell’s theory: There are exactly two cubes, and each cube is small. ∃ x ∃ y(Cube(x) ∧ Cube(y) ∧ x ≠ y) ∧ ∀ x ∀ y ∀ z ((Cube(x) ∧ Cube(y) ∧ Cube(z)) → (x = y ∨ y = z ∨ x = z) ∧ Small(x) ∧ Small(y)) ∃ !2 x Cube(x) ∧ ∀ x (Cube(x) → Small(x))
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The, Both, and Neither con’t “Neither” suggests that there are exactly two objects, and no object has the property ex: Neither cube is large Russell’s theory: There are exactly two cubes, and each of them are not large. ∃ x ∃ y(Cube(x) ∧ Cube(y) ∧ x ≠ y) ∧ ∀ x ∀ y ∀ z ((Cube(x) ∧ Cube(y) ∧ Cube(z)) → (x = y ∨ y = z ∨ x = z) ∧ ¬ Large(x) ∧ ¬ Large(y)) ∃ !2 x Cube(x) ∧ ∀ x (Cube(x) → ¬ Large(x))
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Russell’s Theory Two key features provides a truth value for every sentence containing a definite description the introduction of a logical operation such as negation may introduce an ambiguity ex: the cube is not small Exactly one cube and it is not small Not the case that there is exactly one cube and that it is small
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Russell’s Theory con’t Opposing critique by philosopher P.F. Strawson Russell is mistaken in supposing that one who utters the sentence “the cube is small” makes three claims person does not even succeed in making a claim unless there is exactly one cube Presupposition If the presupposition is fulfilled, then the utterer of the sentence is making a claim If the presupposition is not fulfilled(bad description), then the speaker has failed to make any claim
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Russell’s Theory con’t Consequences of Strawson’s analysis introduction of truth value gaps cannot be translated into FOL/weakens it alternative to presuppositions: implicatures use cancellability test for validity Can one conjoin without contradiction?
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“Numerical” Quantifications not expressible in FOL Most indeterminate implies more than half disjunction does not end ex: more than half [ ∃ xA(x) ∧ ∀ x~B(x)] ∨ [ ∃ ≤2 xA(x) ∧ ∃ ≤1 xB(x)] ∨ [ ∃ ≤3 xA(x) ∧ ∃ ≤2 xB(x)] ∨... Many, A lot, A few context dependent
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Barker-Plummer, D. &. (2011). Language, Proof and Logic. Stanford: CSLI Publications. Cohen, S. (2004). Chapter 14: More on Quantification. http://faculty.washington.edu/smcohen/120/Chapter14.pdf Cummins, C. &. (n.d.). Numerically Quantified Expressions. www.crcummins.com/CRCNumerically.pdf Filip, H. (2012, January 18). Lecture 3: Quantification. user.phil-fak.uni- duesseldorf.de/~filip/L3.Tilburg.pdf Guerts, B. (n.d.). Processing Quantifiers. ncs.ruhosting.nl/bart/talks/paris2005/parislides1.pdf Johns, R. (n.d.). Translations Involving Complex Quantifiers. http://faculty.arts.ubc.ca/rjohns/notes5.pdf Shapiro, S. (n.d.). Numerical Quantifiers and Their Use in Reasoning with Negative Information. 128.205.32.53/~shapiro/Papers/sha79b.pdf
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