On Operator N and Wittgenstein's Logical Philosophy
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Logic: Representation and Automated Reasoning
Logic Knowledge Representation & Reasoning Mechanisms Logic ● Logic as KR ■ Propositional Logic ■ Predicate Logic (predicate Calculus) ● Automated Reasoning ■ Logical inferences ■ Resolution and Theorem-proving Logic ● Logic as KR ■ Propositional Logic ■ Predicate Logic (predicate Calculus) ● Automated Reasoning ■ Logical inferences ■ Resolution and Theorem-proving Propositional Logic ● Symbols: ■ truth symbols: true, false ■ propositions: a statement that is “true” or “false” but not both E.g., P = “Two plus two equals four” Q = “It rained yesterday.” ■ connectives: ~, →, ∧, ∨, ≡ • Sentences - propositions or truth symbols • Well formed formulas (expressions) - sentences that are legally well-formed with connectives E.g., P ∧ R → and P ~ are not wff but P ∧ R → ~ Q is Examples P Q AI is hard but it is interesting P ∧ Q AI is neither hard nor interesting ~P ∧ ~ Q P Q If you don’t do assignments then you will fail P → Q ≡ Do assignments or fail (Prove by truth table) ~ P ∨ Q None or both of P and Q is true (~ P ∧ ~ Q) ∨ (P ∧ Q) ≡ T Exactly one of P and Q is true (~ P ∧ Q) ∨ (P ∧ ~ Q) ≡ T Predicate Logic ● Symbols: • truth symbols • constants: represents objects in the world • variables: represents ranging objects } Terms • functions: represent properties • Predicates: functions of terms with true/false values e.g., bill_residence_city (vancouver) or lives (bill, vancouver) ● Atomic sentences: true, false, or predicates ● Quantifiers: ∀, ∃ ● Sentences (expressions): sequences of legal applications of connectives and quantifiers to atomic -
1 Lecture 13: Quantifier Laws1 1. Laws of Quantifier Negation There
Ling 409 lecture notes, Lecture 13 Ling 409 lecture notes, Lecture 13 October 19, 2005 October 19, 2005 Lecture 13: Quantifier Laws1 true or there is at least one individual that makes φ(x) true (or both). Those are also equivalent statements. 1. Laws of Quantifier Negation Laws 2 and 3 suggest a fundamental connection between universal quantification and There are a number of equivalences based on the set-theoretic semantics of predicate logic. conjunction and existential quantification and disjunction: Take for instance a statement of the form (∃x)~ φ(x)2. It asserts that there is at least one individual d which makes ~φ(x) true. That means that d makes ϕ(x) false. That, in turn, (∀x) ψ(x) is true iff ψ(a) and ψ(b) and ψ(c) … (where a, b, c … are the names of the means that (∀x) φ(x) is false, which means that ~(∀x) φ(x) is true. The same reasoning individuals in the domain.) can be applied in the reverse direction and the result is the first quantifier law: ∃ ψ ψ ψ ψ ( x) (x) is true iff (a) or (b) or c) … (where a, b, c … are the names of the (1) Laws of Quantifier Negation individuals in the domain.) Law 1: ~(∀x) φ(x) ⇐⇒ (∃x)~ φ(x) From that point of view, Law 1 resembles a generalized DeMorgan’s Law: (Possible English correspondents: Not everyone passed the test and Someone did not pass (4) ~(ϕ(a) & ϕ(b) & ϕ(c) … ) ⇐⇒ ~ϕ(a) v ~ϕ(b) v ϕ(c) … the test.) (5) Law 4: (∀x) ψ(x) v (∀x)φ(x) ⇒ (∀x)( ψ(x) v φ(x)) ϕ ⇐ ϕ Because of the Law of Double Negation (~~ (x) ⇒ (x)), Law 1 could also be written (6) Law 5: (∃x)( ψ(x) & φ(x)) ⇒ (∃x) ψ(x) & (∃x)φ(x) as follows: These two laws are one-way entailments, NOT equivalences. -
Lexical Scoping As Universal Quantification
University of Pennsylvania ScholarlyCommons Technical Reports (CIS) Department of Computer & Information Science March 1989 Lexical Scoping as Universal Quantification Dale Miller University of Pennsylvania Follow this and additional works at: https://repository.upenn.edu/cis_reports Recommended Citation Dale Miller, "Lexical Scoping as Universal Quantification", . March 1989. University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-89-23. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_reports/785 For more information, please contact [email protected]. Lexical Scoping as Universal Quantification Abstract A universally quantified goal can be interpreted intensionally, that is, the goal ∀x.G(x) succeeds if for some new constant c, the goal G(c) succeeds. The constant c is, in a sense, given a scope: it is introduced to solve this goal and is "discharged" after the goal succeeds or fails. This interpretation is similar to the interpretation of implicational goals: the goal D ⊃ G should succeed if when D is assumed, the goal G succeeds. The assumption D is discharged after G succeeds or fails. An interpreter for a logic programming language containing both universal quantifiers and implications in goals and the body of clauses is described. In its non-deterministic form, this interpreter is sound and complete for intuitionistic logic. Universal quantification can provide lexical scoping of individual, function, and predicate constants. Several examples are presented to show how such scoping can be used to provide a Prolog-like language with facilities for local definition of programs, local declarations in modules, abstract data types, and encapsulation of state. -
Sets, Propositional Logic, Predicates, and Quantifiers
COMP 182 Algorithmic Thinking Sets, Propositional Logic, Luay Nakhleh Computer Science Predicates, and Quantifiers Rice University !1 Reading Material ❖ Chapter 1, Sections 1, 4, 5 ❖ Chapter 2, Sections 1, 2 !2 ❖ Mathematics is about statements that are either true or false. ❖ Such statements are called propositions. ❖ We use logic to describe them, and proof techniques to prove whether they are true or false. !3 Propositions ❖ 5>7 ❖ The square root of 2 is irrational. ❖ A graph is bipartite if and only if it doesn’t have a cycle of odd length. ❖ For n>1, the sum of the numbers 1,2,3,…,n is n2. !4 Propositions? ❖ E=mc2 ❖ The sun rises from the East every day. ❖ All species on Earth evolved from a common ancestor. ❖ God does not exist. ❖ Everyone eventually dies. !5 ❖ And some of you might already be wondering: “If I wanted to study mathematics, I would have majored in Math. I came here to study computer science.” !6 ❖ Computer Science is mathematics, but we almost exclusively focus on aspects of mathematics that relate to computation (that can be implemented in software and/or hardware). !7 ❖Logic is the language of computer science and, mathematics is the computer scientist’s most essential toolbox. !8 Examples of “CS-relevant” Math ❖ Algorithm A correctly solves problem P. ❖ Algorithm A has a worst-case running time of O(n3). ❖ Problem P has no solution. ❖ Using comparison between two elements as the basic operation, we cannot sort a list of n elements in less than O(n log n) time. ❖ Problem A is NP-Complete. -
First-Order Logic
First-Order Logic Chapter 8 1 Outline • Why FOL? • Syntax and semantics of FOL • Using FOL • Wumpus world in FOL • Knowledge engineering in FOL 2 Pros and cons of propositional logic ☺ Propositional logic is declarative ☺ Propositional logic allows partial/disjunctive/negated information – (unlike most data structures and databases) ☺ Propositional logic is compositional: – meaning of B1,1 ∧ P1,2 is derived from meaning of B1,1 and of P1,2 ☺ Meaning in propositional logic is context-independent – (unlike natural language, where meaning depends on context) Propositional logic has very limited expressive power – (unlike natural language) – E.g., cannot say "pits cause breezes in adjacent squares“ • except by writing one sentence for each square 3 First-order logic • Whereas propositional logic assumes the world contains facts, • first-order logic (like natural language) assumes the world contains – Objects: people, houses, numbers, colors, baseball games, wars, … – Relations: red, round, prime, brother of, bigger than, part of, comes between, … – Functions: father of, best friend, one more than, plus, … 4 Syntax of FOL: Basic elements • Constants KingJohn, 2, NUS,... • Predicates Brother, >,... • Functions Sqrt, LeftLegOf,... • Variables x, y, a, b,... • Connectives ¬, ⇒, ∧, ∨, ⇔ • Equality = • Quantifiers ∀, ∃ 5 Atomic sentences Atomic sentence = predicate (term1 ,...,termn) or term = term 1 2 Term = function (term1,..., termn) or constant or variable • E.g., Brother(KingJohn,RichardTheLionheart) > (Length(LeftLegOf(Richard)), Length(LeftLegOf(KingJohn))) -
CHAPTER 1 the Main Subject of Mathematical Logic Is Mathematical
CHAPTER 1 Logic The main subject of Mathematical Logic is mathematical proof. In this chapter we deal with the basics of formalizing such proofs and analysing their structure. The system we pick for the representation of proofs is natu- ral deduction as in Gentzen (1935). Our reasons for this choice are twofold. First, as the name says this is a natural notion of formal proof, which means that the way proofs are represented corresponds very much to the way a careful mathematician writing out all details of an argument would go any- way. Second, formal proofs in natural deduction are closely related (via the Curry-Howard correspondence) to terms in typed lambda calculus. This provides us not only with a compact notation for logical derivations (which otherwise tend to become somewhat unmanageable tree-like structures), but also opens up a route to applying the computational techniques which un- derpin lambda calculus. An essential point for Mathematical Logic is to fix the formal language to be used. We take implication ! and the universal quantifier 8 as basic. Then the logic rules correspond precisely to lambda calculus. The additional connectives (i.e., the existential quantifier 9, disjunction _ and conjunction ^) will be added via axioms. Later we will see that these axioms are deter- mined by particular inductive definitions. In addition to the use of inductive definitions as a unifying concept, another reason for that change of empha- sis will be that it fits more readily with the more computational viewpoint adopted there. This chapter does not simply introduce basic proof theory, but in addi- tion there is an underlying theme: to bring out the constructive content of logic, particularly in regard to the relationship between minimal and classical logic. -
Atomic Sentences
Symbolic Logic Study Guide: Class Notes 5 1.2. Notes for Chapter 2: Atomic Sentences 1.2.1. The Basic Structure of Atomic Sentences (2.1, 2.2, 2.3, and 2.5 of the Text) 1. Comparison between simple English sentences and atomic sentences Simple English Sentences Atomic sentences (FOL) (subject-predicate sentences) John is a freshman Freshman (John) John swims. Swim (John) John loves Jenny. Love (John, Jenny) John prefers Jenny to Amy. Prefer (John, Jenny, Amy) John’s mother loves Jenny. Love (mother (John), Jenny) The father of Jenny is angry. Angry (father (Jenny)) John is the brother of Jenny. John = brother (Jenny) [relational identity] 2. Names Definition: Names are individual constants that refer to some fixed individual objects or other. (1) The rule of naming (p. 10) • No empty name. • No multiple references (do not use one name to refer to different objects). • Multiple names: you can name one object by different names. (2) General terms / names: using a predicate, instead of a constant, to represent a general term. For example, John is a student Student (John) [correct] John = student [wrong!!!] 3. Predicates Definition: Predicates are symbols used to denote some property of objects or some relationship between objects. (1) Arity of predicates • Unary predicates--property • Binary predicates Relations • Ternary predicates (2) The predicates used in Tarski’s World: see p. 11. (3) Two rules of predicates: see p.12. 6 Symbolic Logic Study Guide: Class Notes 4. Functions Definition: A function is an individual constant determined by another constant. (1) Comparison with names: • Both refer to some fixed individual objects. -
1 Winnowing Wittgenstein: What's Worth Salvaging from the Wreck Of
Winnowing Wittgenstein: What’s Worth Salvaging from the Wreck of the Tractatus Peter Simons Trinity College Dublin Abstract Wittgenstein’s Tractatus still harbours valuable lessons for contemporary philosophy, but which ones? Wittgenstein’s long list of things we cannot speak about is set aside, but his insistence that the logical constants do not represent is retained, as is the absolute distinction between names and sentences. We preserve his atomism of elementary sentences but discard the atomism of simple objects in states of affairs. The fundamental harmony between language and the world is rejected: it is the source of much that is wrong in the Tractatus. What remains is a clarified role for items in making elementary sentences true. 1 Introduction Kevin Mulligan has maintained throughout his philosophical career a keen and judicious appreciation of the chief figures of that great philosophical explosion centred on Austria in the 19th and early 20th century. Of these figures, the best-known and most widely influential is Ludwig Wittgenstein.1 Kevin has maintained, quite rightly, that it is impossible to appreciate the extent to which Wittgenstein’s contributions to philosophy are as original as his many admirers contend without a great deal more knowledge of the Central European milieu from which Wittgenstein emerged than the majority of these admirers care to or are prepared to investigate. In this respect the more diffuse and ample later philosophy presents a much greater challenge than the early work which culminated in the Logisch-philosophische Abhandlung.2 The modest extent of the Tractatus and its more limited period of genesis, as well as its relatively crisper form and content, render it a more manageable and ultimately less controversial work than the post-Tractarian writings, whose thrust is even today occasionally obscure, despite more than a half century of frenzied exegesis. -
Chapter 1. Sentential Logic
CHAPTER 1. SENTENTIAL LOGIC 1. Introduction In sentential logic we study how given sentences may be combined to form more complicated compound sentences. For example, from the sentences 7 is prime, 7 is odd, 2 is prime, 2 is odd, we can obtain the following sentences: • 7 is prime and 7 is odd • 7 is odd and 2 is odd • 7 is odd or 2 is odd • 7 is not odd • If 2 is odd then 2 is prime Of course, this process can be iterated as often as we want, obtaining also: • If 7 is not odd then 2 is odd • If 7 is odd and 2 is odd then 2 is not prime • (7 is odd and 2 is odd) or 2 is prime We have improved on English in the last example by using parentheses to resolve an ambiguity. And, or, not, if . then (or implies) are called (sentential) connectives. Using them we can define more connectives, for example 2 is prime iff 2 is odd can be defined as (If 2 is prime then 2 is odd) and (if 2 is odd then 2 is prime). The truth value of any compound sentence is determined completely by the truth values of its component parts. For example, assuming 2, 7, odd, prime all have their usual meanings then 7 is odd and 2 is odd is false but (7 is odd and 2 is odd) or 2 is prime is true. We will discuss implication later. In the formal system of sentential logic we study in this Chapter, we do not use English sentences, but build the compound sentences from an infinite collection of symbols which we think of as referring to sentences — since these are not built up from other sentences we refer to them as atomic sentences. -
Precomplete Negation and Universal Quantification
PRECOMPLETE NEGATION AND UNIVERSAL QUANTIFICATION by Paul J. Vada Technical Report 86-9 April 1986 Precomplete Negation And Universal Quantification. Paul J. Voda Department of Computer Science, The University of British Columbia, Vancouver, B.C. V6T IW5, Canada. ABSTRACT This paper is concerned with negation in logic programs. We propose to extend negation as failure by a stronger form or negation called precomplete negation. In contrast to negation as failure, precomplete negation has a simple semantic charaterization given in terms of computational theories which deli berately abandon the law of the excluded middle (and thus classical negation) in order to attain computational efficiency. The computation with precomplete negation proceeds with the direct computation of negated formulas even in the presence of free variables. Negated formulas are computed in a mode which is dual to the standard positive mode of logic computations. With uegation as failure the formulas with free variables must be delayed until the latter obtain values. Consequently, in situations where delayed formulas are never sufficiently instantiated, precomplete negation can find solutions unattainable with negation as failure. As a consequence of delaying, negation as failure cannot compute unbounded universal quantifiers whereas precomplete negation can. Instead of concentrating on the model-theoretical side of precomplete negation this paper deals with questions of complete computations and efficient implementations. April 1986 Precomplete Negation And Universal Quanti.flcation. Paul J. Voda Department or Computer Science, The University or British Columbia, Vancouver, B.C. V6T 1W5, Canada. 1. Introduction. Logic programming languages ba.5ed on Prolog experience significant difficulties with negation. There are many Prolog implementations with unsound negation where the computation succeeds with wrong results. -
Symbolic Logic: Grammar, Semantics, Syntax
Symbolic Logic: Grammar, Semantics, Syntax Logic aims to give a precise method or recipe for determining what follows from a given set of sentences. So we need precise definitions of `sentence' and `follows from.' 1. Grammar: What's a sentence? What are the rules that determine whether a string of symbols is a sentence, and when it is not? These rules are known as the grammar of a particular language. We have actually been operating with two different but very closely related grammars this term: a grammar for propositional logic and a grammar for first-order logic. The grammar for propositional logic (thus far) is simple: 1. There are an indefinite number of propositional variables, which we have been symbolizing as A; B; ::: and P; Q; :::; each of these is a sen- tence. ? is also a sentence. 2. If A; B are sentences, then: •:A is a sentence, • A ^ B is a sentence, and • A _ B is a sentence. 3. Nothing else is a sentence: anything that is neither a member of 1, nor constructable via successive applications of 2 to the members of 1, is not a sentence. The grammar for first-order logic (thus far) is more complex. 2 and 3 remain exactly the same as above, but 1 must be replaced by some- thing more complex. First, a list of all predicates (e.g. Tet or SameShape) and proper names (e.g. max; b) of a particular language L must be specified. Then we can define: 1F OL. A series of symbols s is an atomic sentence = s is an n-place predicate followed by an ordered sequence of n proper names. -
Not All There: the Interactions of Negation and Universal Quantifier Pu
Not all there: The interactions of negation and universal quantifier Puk'w in PayPaˇȷuT@m∗ Roger Yu-Hsiang Lo University of British Columbia Abstract: The current paper examines the ambiguity between negation and the univer- sal quantifier Puk'w in PayPaˇȷuT@m, a critically endangered Central Salish language. I ar- gue that the ambiguity in PayPaˇȷuT@m arises from the nonmaximal, exception-tolerating property of Salish all, instead of resorting to the scopal interaction between negation and the universal quantifier, as in English. Specifically, by assuming that negation in PayPaˇȷuT@m is always interpreted with the maximal force, the ambiguity can be under- stood as originating from exceptions to this canonical interpretation. Whether or not this ambiguity is only available in PayPaˇȷuT@m is still unclear, and further data elicitation and cross-Salish comparison are underway. Keywords: PayPaˇȷuT@m (Mainland Comox), semantics, ambiguities, negation, univer- sal quantifier 1 Introduction This paper presents a preliminary analysis of the semantic ambiguity involved in the combination of negation and the universal quantifier Puk'w in PayPaˇȷuT@m, a critically endangered Central Salish language. The ambiguity between a negative element and a universal quantifier is also found in English. For example, consider the English paradigm in (1) from Carden (1976), where (1a) has only one reading while (1b) is ambiguous. (1) a. Not all the boys will run. :(8x;boy(x);run(x)) b. [ All the boys ] won’t run. i. :(8x;boy(x);run(x)) ii. (8x;boy(x));:(run(x)) In the traditional account, with the readings in (1a) and (1b-i), negation takes higher scope than the quantified DP at LF.