Predicate Calculus
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Chapter 2 Introduction to Classical Propositional
CHAPTER 2 INTRODUCTION TO CLASSICAL PROPOSITIONAL LOGIC 1 Motivation and History The origins of the classical propositional logic, classical propositional calculus, as it was, and still often is called, go back to antiquity and are due to Stoic school of philosophy (3rd century B.C.), whose most eminent representative was Chryssipus. But the real development of this calculus began only in the mid-19th century and was initiated by the research done by the English math- ematician G. Boole, who is sometimes regarded as the founder of mathematical logic. The classical propositional calculus was ¯rst formulated as a formal ax- iomatic system by the eminent German logician G. Frege in 1879. The assumption underlying the formalization of classical propositional calculus are the following. Logical sentences We deal only with sentences that can always be evaluated as true or false. Such sentences are called logical sentences or proposi- tions. Hence the name propositional logic. A statement: 2 + 2 = 4 is a proposition as we assume that it is a well known and agreed upon truth. A statement: 2 + 2 = 5 is also a proposition (false. A statement:] I am pretty is modeled, if needed as a logical sentence (proposi- tion). We assume that it is false, or true. A statement: 2 + n = 5 is not a proposition; it might be true for some n, for example n=3, false for other n, for example n= 2, and moreover, we don't know what n is. Sentences of this kind are called propositional functions. We model propositional functions within propositional logic by treating propositional functions as propositions. -
Aristotle's Illicit Quantifier Shift: Is He Guilty Or Innocent
Aristos Volume 1 Issue 2 Article 2 9-2015 Aristotle's Illicit Quantifier Shift: Is He Guilty or Innocent Jack Green The University of Notre Dame Australia Follow this and additional works at: https://researchonline.nd.edu.au/aristos Part of the Philosophy Commons, and the Religious Thought, Theology and Philosophy of Religion Commons Recommended Citation Green, J. (2015). "Aristotle's Illicit Quantifier Shift: Is He Guilty or Innocent," Aristos 1(2),, 1-18. https://doi.org/10.32613/aristos/ 2015.1.2.2 Retrieved from https://researchonline.nd.edu.au/aristos/vol1/iss2/2 This Article is brought to you by ResearchOnline@ND. It has been accepted for inclusion in Aristos by an authorized administrator of ResearchOnline@ND. For more information, please contact [email protected]. Green: Aristotle's Illicit Quantifier Shift: Is He Guilty or Innocent ARISTOTLE’S ILLICIT QUANTIFIER SHIFT: IS HE GUILTY OR INNOCENT? Jack Green 1. Introduction Aristotle’s Nicomachean Ethics (from hereon NE) falters at its very beginning. That is the claim of logicians and philosophers who believe that in the first book of the NE Aristotle mistakenly moves from ‘every action and pursuit aims at some good’ to ‘there is some one good at which all actions and pursuits aim.’1 Yet not everyone is convinced of Aristotle’s seeming blunder.2 In lieu of that, this paper has two purposes. Firstly, it is an attempt to bring some clarity to that debate in the face of divergent opinions of the location of the fallacy; some proposing it lies at I.i.1094a1-3, others at I.ii.1094a18-22, making it difficult to wade through the literature. -
Scope Ambiguity in Syntax and Semantics
Scope Ambiguity in Syntax and Semantics Ling324 Reading: Meaning and Grammar, pg. 142-157 Is Scope Ambiguity Semantically Real? (1) Everyone loves someone. a. Wide scope reading of universal quantifier: ∀x[person(x) →∃y[person(y) ∧ love(x,y)]] b. Wide scope reading of existential quantifier: ∃y[person(y) ∧∀x[person(x) → love(x,y)]] 1 Could one semantic representation handle both the readings? • ∃y∀x reading entails ∀x∃y reading. ∀x∃y describes a more general situation where everyone has someone who s/he loves, and ∃y∀x describes a more specific situation where everyone loves the same person. • Then, couldn’t we say that Everyone loves someone is associated with the semantic representation that describes the more general reading, and the more specific reading obtains under an appropriate context? That is, couldn’t we say that Everyone loves someone is not semantically ambiguous, and its only semantic representation is the following? ∀x[person(x) →∃y[person(y) ∧ love(x,y)]] • After all, this semantic representation reflects the syntax: In syntax, everyone c-commands someone. In semantics, everyone scopes over someone. 2 Arguments for Real Scope Ambiguity • The semantic representation with the scope of quantifiers reflecting the order in which quantifiers occur in a sentence does not always represent the most general reading. (2) a. There was a name tag near every plate. b. A guard is standing in front of every gate. c. A student guide took every visitor to two museums. • Could we stipulate that when interpreting a sentence, no matter which order the quantifiers occur, always assign wide scope to every and narrow scope to some, two, etc.? 3 Arguments for Real Scope Ambiguity (cont.) • But in a negative sentence, ¬∀x∃y reading entails ¬∃y∀x reading. -
12 Propositional Logic
CHAPTER 12 ✦ ✦ ✦ ✦ Propositional Logic In this chapter, we introduce propositional logic, an algebra whose original purpose, dating back to Aristotle, was to model reasoning. In more recent times, this algebra, like many algebras, has proved useful as a design tool. For example, Chapter 13 shows how propositional logic can be used in computer circuit design. A third use of logic is as a data model for programming languages and systems, such as the language Prolog. Many systems for reasoning by computer, including theorem provers, program verifiers, and applications in the field of artificial intelligence, have been implemented in logic-based programming languages. These languages generally use “predicate logic,” a more powerful form of logic that extends the capabilities of propositional logic. We shall meet predicate logic in Chapter 14. ✦ ✦ ✦ ✦ 12.1 What This Chapter Is About Section 12.2 gives an intuitive explanation of what propositional logic is, and why it is useful. The next section, 12,3, introduces an algebra for logical expressions with Boolean-valued operands and with logical operators such as AND, OR, and NOT that Boolean algebra operate on Boolean (true/false) values. This algebra is often called Boolean algebra after George Boole, the logician who first framed logic as an algebra. We then learn the following ideas. ✦ Truth tables are a useful way to represent the meaning of an expression in logic (Section 12.4). ✦ We can convert a truth table to a logical expression for the same logical function (Section 12.5). ✦ The Karnaugh map is a useful tabular technique for simplifying logical expres- sions (Section 12.6). -
Discrete Mathematics, Chapter 1.4-1.5: Predicate Logic
Discrete Mathematics, Chapter 1.4-1.5: Predicate Logic Richard Mayr University of Edinburgh, UK Richard Mayr (University of Edinburgh, UK) Discrete Mathematics. Chapter 1.4-1.5 1 / 23 Outline 1 Predicates 2 Quantifiers 3 Equivalences 4 Nested Quantifiers Richard Mayr (University of Edinburgh, UK) Discrete Mathematics. Chapter 1.4-1.5 2 / 23 Propositional Logic is not enough Suppose we have: “All men are mortal.” “Socrates is a man”. Does it follow that “Socrates is mortal” ? This cannot be expressed in propositional logic. We need a language to talk about objects, their properties and their relations. Richard Mayr (University of Edinburgh, UK) Discrete Mathematics. Chapter 1.4-1.5 3 / 23 Predicate Logic Extend propositional logic by the following new features. Variables: x; y; z;::: Predicates (i.e., propositional functions): P(x); Q(x); R(y); M(x; y);::: . Quantifiers: 8; 9. Propositional functions are a generalization of propositions. Can contain variables and predicates, e.g., P(x). Variables stand for (and can be replaced by) elements from their domain. Richard Mayr (University of Edinburgh, UK) Discrete Mathematics. Chapter 1.4-1.5 4 / 23 Propositional Functions Propositional functions become propositions (and thus have truth values) when all their variables are either I replaced by a value from their domain, or I bound by a quantifier P(x) denotes the value of propositional function P at x. The domain is often denoted by U (the universe). Example: Let P(x) denote “x > 5” and U be the integers. Then I P(8) is true. I P(5) is false. -
The Comparative Predictive Validity of Vague Quantifiers and Numeric
c European Survey Research Association Survey Research Methods (2014) ISSN 1864-3361 Vol.8, No. 3, pp. 169-179 http://www.surveymethods.org Is Vague Valid? The Comparative Predictive Validity of Vague Quantifiers and Numeric Response Options Tarek Al Baghal Institute of Social and Economic Research, University of Essex A number of surveys, including many student surveys, rely on vague quantifiers to measure behaviors important in evaluation. The ability of vague quantifiers to provide valid information, particularly compared to other measures of behaviors, has been questioned within both survey research generally and educational research specifically. Still, there is a dearth of research on whether vague quantifiers or numeric responses perform better in regards to validity. This study examines measurement properties of frequency estimation questions through the assessment of predictive validity, which has been shown to indicate performance of competing question for- mats. Data from the National Survey of Student Engagement (NSSE), a preeminent survey of university students, is analyzed in which two psychometrically tested benchmark scales, active and collaborative learning and student-faculty interaction, are measured through both vague quantifier and numeric responses. Predictive validity is assessed through correlations and re- gression models relating both vague and numeric scales to grades in school and two education experience satisfaction measures. Results support the view that the predictive validity is higher for vague quantifier scales, and hence better measurement properties, compared to numeric responses. These results are discussed in light of other findings on measurement properties of vague quantifiers and numeric responses, suggesting that vague quantifiers may be a useful measurement tool for behavioral data, particularly when it is the relationship between variables that are of interest. -
Propositional Calculus
CSC 438F/2404F Notes Winter, 2014 S. Cook REFERENCES The first two references have especially influenced these notes and are cited from time to time: [Buss] Samuel Buss: Chapter I: An introduction to proof theory, in Handbook of Proof Theory, Samuel Buss Ed., Elsevier, 1998, pp1-78. [B&M] John Bell and Moshe Machover: A Course in Mathematical Logic. North- Holland, 1977. Other logic texts: The first is more elementary and readable. [Enderton] Herbert Enderton: A Mathematical Introduction to Logic. Academic Press, 1972. [Mendelson] E. Mendelson: Introduction to Mathematical Logic. Wadsworth & Brooks/Cole, 1987. Computability text: [Sipser] Michael Sipser: Introduction to the Theory of Computation. PWS, 1997. [DSW] M. Davis, R. Sigal, and E. Weyuker: Computability, Complexity and Lan- guages: Fundamentals of Theoretical Computer Science. Academic Press, 1994. Propositional Calculus Throughout our treatment of formal logic it is important to distinguish between syntax and semantics. Syntax is concerned with the structure of strings of symbols (e.g. formulas and formal proofs), and rules for manipulating them, without regard to their meaning. Semantics is concerned with their meaning. 1 Syntax Formulas are certain strings of symbols as specified below. In this chapter we use formula to mean propositional formula. Later the meaning of formula will be extended to first-order formula. (Propositional) formulas are built from atoms P1;P2;P3;:::, the unary connective :, the binary connectives ^; _; and parentheses (,). (The symbols :; ^ and _ are read \not", \and" and \or", respectively.) We use P; Q; R; ::: to stand for atoms. Formulas are defined recursively as follows: Definition of Propositional Formula 1) Any atom P is a formula. -
Propositional Logic, Truth Tables, and Predicate Logic (Rosen, Sections 1.1, 1.2, 1.3) TOPICS
Propositional Logic, Truth Tables, and Predicate Logic (Rosen, Sections 1.1, 1.2, 1.3) TOPICS • Propositional Logic • Logical Operations • Equivalences • Predicate Logic Logic? What is logic? Logic is a truth-preserving system of inference Truth-preserving: System: a set of If the initial mechanistic statements are transformations, based true, the inferred on syntax alone statements will be true Inference: the process of deriving (inferring) new statements from old statements Proposi0onal Logic n A proposion is a statement that is either true or false n Examples: n This class is CS122 (true) n Today is Sunday (false) n It is currently raining in Singapore (???) n Every proposi0on is true or false, but its truth value (true or false) may be unknown Proposi0onal Logic (II) n A proposi0onal statement is one of: n A simple proposi0on n denoted by a capital leJer, e.g. ‘A’. n A negaon of a proposi0onal statement n e.g. ¬A : “not A” n Two proposi0onal statements joined by a connecve n e.g. A ∧ B : “A and B” n e.g. A ∨ B : “A or B” n If a connec0ve joins complex statements, parenthesis are added n e.g. A ∧ (B∨C) Truth Tables n The truth value of a compound proposi0onal statement is determined by its truth table n Truth tables define the truth value of a connec0ve for every possible truth value of its terms Logical negaon n Negaon of proposi0on A is ¬A n A: It is snowing. n ¬A: It is not snowing n A: Newton knew Einstein. n ¬A: Newton did not know Einstein. -
Formal Logic: Quantifiers, Predicates, and Validity
Formal Logic: Quantifiers, Predicates, and Validity CS 130 – Discrete Structures Variables and Statements • Variables: 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, x = Tuesday, etc. • We normally use letters at the end of the alphabet as variables, such as x, y, z. • A collection of objects is called the domain of objects. For the above example, the days in the week is the domain of variable x. CS 130 – Discrete Structures 55 Quantifiers • Propositional wffs have rather limited expressive power. E.g., “For every x, x > 0”. • 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 Quantifier: represented by , “for all”, “for every”, “for each”, or “for any”. – Existential Quantifier: represented by , “for some”, “there exists”, “there is a”, or “for at least one”. CS 130 – Discrete Structures 56 Predicates • Predicate: It is the verbal statement which 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. – P(x) = x has 30 days. – P(April) = April has 30 days. – What is the truth value of (x)P(x) where x is all the months and P(x) = x has less than 32 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 is called the domain of interpretation, and it must contain at least one object. -
Quantifiers and Dependent Types
Quantifiers and dependent types Constructive Logic (15-317) Instructor: Giselle Reis Lecture 05 We have largely ignored the quantifiers so far. It is time to give them the proper attention, and: (1) design its rules, (2) show that they are locally sound and complete and (3) give them a computational interpretation. Quantifiers are statements about some formula parametrized by a term. Since we are working with a first-order logic, this term will have a simple type τ, different from the type o of formulas. A second-order logic allows terms to be of type τ ! ι, for simple types τ and ι, and higher-order logics allow terms to have arbitrary types. In particular, one can quantify over formulas in second- and higher-order logics, but not on first-order logics. As a consequence, the principle of induction (in its general form) can be expressed on those logics as: 8P:8n:(P (z) ^ (8x:P (x) ⊃ P (s(x))) ⊃ P (n)) But this comes with a toll, so we will restrict ourselves to first-order in this course. Also, we could have multiple types for terms (many-sorted logic), but since a logic with finitely many types can be reduced to a single-sorted logic, we work with a single type for simplicity, called τ. 1 Rules in natural deduction To design the natural deduction rules for the quantifiers, we will follow the same procedure as for the other connectives and look at their meanings. Some examples will also help on the way. Since we now have a new ele- ment in our language, namely, terms, we will have a new judgment a : τ denoting that the term a has type τ. -
Why Would You Trust B? Eric Jaeger, Catherine Dubois
Why Would You Trust B? Eric Jaeger, Catherine Dubois To cite this version: Eric Jaeger, Catherine Dubois. Why Would You Trust B?. Logic for Programming, Artificial In- telligence, and Reasoning, Nov 2007, Yerevan, Armenia. pp.288-302, 10.1007/978-3-540-75560-9. hal-00363345 HAL Id: hal-00363345 https://hal.archives-ouvertes.fr/hal-00363345 Submitted on 23 Feb 2009 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Why Would You Trust B? Eric´ Jaeger12 and Catherine Dubois3 1 LIP6, Universit´eParis 6, 4 place Jussieu, 75252 Paris Cedex 05, France 2 LTI, Direction centrale de la s´ecurit´edes syst`emes d’information, 51 boulevard de La Tour-Maubourg, 75700 Paris 07 SP, France 3 CEDRIC, Ecole´ nationale sup´erieure d’informatique pour l’industrie et l’entreprise, 18 all´ee Jean Rostand, 91025 Evry Cedex, France Abstract. The use of formal methods provides confidence in the cor- rectness of developments. Yet one may argue about the actual level of confidence obtained when the method itself – or its implementation – is not formally checked. We address this question for the B, a widely used formal method that allows for the derivation of correct programs from specifications. -
Vagueness and Quantification
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Institutional Research Information System University of Turin Vagueness and Quantification (postprint version) Journal of Philosophical Logic first online, 2015 Andrea Iacona This paper deals with the question of what it is for a quantifier expression to be vague. First it draws a distinction between two senses in which quantifier expressions may be said to be vague, and provides an account of the distinc- tion which rests on independently grounded assumptions. Then it suggests that, if some further assumptions are granted, the difference between the two senses considered can be represented at the formal level. Finally, it out- lines some implications of the account provided which bear on three debated issues concerning quantification. 1 Preliminary clarifications Let us start with some terminology. First of all, the term `quantifier expres- sion' will designate expressions such as `all', `some' or `more than half of', which occurs in noun phrases as determiners. For example, in `all philoso- phers', `all' occurs as a determiner of `philosophers', and the same position can be occupied by `some' or `more than half of'. This paper focuses on simple quantified sentences containing quantifier expressions so understood, such as the following: (1) All philosophers are rich (2) Some philosophers are rich (3) More than half of philosophers are rich Although this is a very restricted class of sentences, it is sufficiently repre- sentative to deserve consideration on its own. In the second place, the term `domain' will designate the totality of things over which a quantifier expression is taken to range.