Notes on Mathematical Logic David W. Kueker

Total Page:16

File Type:pdf, Size:1020Kb

Notes on Mathematical Logic David W. Kueker Notes on Mathematical Logic David W. Kueker University of Maryland, College Park E-mail address: [email protected] URL: http://www-users.math.umd.edu/~dwk/ Contents Chapter 0. Introduction: What Is Logic? 1 Part 1. Elementary Logic 5 Chapter 1. Sentential Logic 7 0. Introduction 7 1. Sentences of Sentential Logic 8 2. Truth Assignments 11 3. Logical Consequence 13 4. Compactness 17 5. Formal Deductions 19 6. Exercises 20 20 Chapter 2. First-Order Logic 23 0. Introduction 23 1. Formulas of First Order Logic 24 2. Structures for First Order Logic 28 3. Logical Consequence and Validity 33 4. Formal Deductions 37 5. Theories and Their Models 42 6. Exercises 46 46 Chapter 3. The Completeness Theorem 49 0. Introduction 49 1. Henkin Sets and Their Models 49 2. Constructing Henkin Sets 52 3. Consequences of the Completeness Theorem 54 4. Completeness Categoricity, Quantifier Elimination 57 5. Exercises 58 58 Part 2. Model Theory 59 Chapter 4. Some Methods in Model Theory 61 0. Introduction 61 1. Realizing and Omitting Types 61 2. Elementary Extensions and Chains 66 3. The Back-and-Forth Method 69 i ii CONTENTS 4. Exercises 71 71 Chapter 5. Countable Models of Complete Theories 73 0. Introduction 73 1. Prime Models 73 2. Universal and Saturated Models 75 3. Theories with Just Finitely Many Countable Models 77 4. Exercises 79 79 Chapter 6. Further Topics in Model Theory 81 0. Introduction 81 1. Interpolation and Definability 81 2. Saturated Models 84 3. Skolem Functions and Indescernables 87 4. Some Applications 91 5. Exercises 95 95 Appendix A. Appendix A: Set Theory 97 1. Cardinals and Counting 97 2. Ordinals and Induction 100 Appendix B. Appendix B: Notes on Validities and Logical Consequence 103 1. Some Useful Validities of Sentential Logic 103 2. Some Facts About Logical Consequence 104 Appendix C. Appendix C: Gothic Alphabet 105 Bibliography 107 Index 109 CHAPTER 0 Introduction: What Is Logic? Mathematical logic is the study of mathematical reasoning. We do this by developing an abstract model of the process of reasoning in mathematics. We then study this model and determine some of its properties. Mathematical reasoning is deductive; that is, it consists of drawing (correct) inferences from given or already established facts. Thus the basic concept is that of a statement being a logical consequence of some collection of statements. In ordinary mathematical English the use of \therefore" customarily means that the statement following it is a logical consequence of what comes before. Every integer is either even or odd; 7 is not even; therefore 7 is odd. In our model of mathematical reasoning we will need to precisely define logical consequence. To motivate our definition let us examine the everyday notion. When we say that a statement σ is a logical consequence of (\follows from") some other statements θ1; : : : ; θn, we mean, at the very least, that σ is true provided θ1; : : : ; θn are all true. Unfortunately, this does not capture the essence of logical consequence. For example, consider the following: Some integers are odd; some integers are prime; therefore some integers are both odd and prime. Here the hypotheses are both true and the conclusion is true, but the reasoning is not correct. The problem is that for the reasoning to be logically correct it cannot depend on properties of odd or prime integers other than what is explicitly stated. Thus the reasoning would remain correct if odd, prime, and integer were changed to something else. But in the above example if we replaced prime by even we would have true hypotheses but a false conclusion. This shows that the reasoning is false, even in the original version in which the conclusion was true. The key observation here is that in deciding whether a specific piece of rea- soning is or is not correct we must consider alMathematical logic is the study of mathematical reasoning. We do this by developing an abstract model of the process of reasoning in mathematics. We then study this model and determine some of its properties. Mathematical reasoning is deductive; that is, it consists of drawing (correct) inferences from given or already established facts. Thus the basic concept is that of a statement being a logical consequence of some collection of statements. In ordinary mathematical English the use of \therefore" customarily means that the statement following it is a logical consequence of what l ways of interpreting the undefined concepts|integer, odd, and prime in the above example. This is conceptually easier 1 2 0. INTRODUCTION: WHAT IS LOGIC? in a formal language in which the basic concepts are represented by symbols (like P , Q) without any standard or intuitive meanings to mislead one. Thus the fundamental building blocks of our model are the following: (1) a formal language L, (2) sentences of L: σ; θ; : : :, (3) interpretations for L: A; B;:::, (4) a relation j= between interpretations for L and sentences of L, with A j= σ read as \σ is true in the interpretation A," or \A is a model of σ." Using these we can define logical consequence as follows: Definition -1.1. Let Γ = fθ1; : : : ; θng where θ1; : : : ; θn are sentences of L, and let σ be a sentence of L. Then σ is a logical consequence of Γ if and only if for every interpretation A of L, A j= σ provided A j= θi for all i = 1; : : : ; n. Our notation for logical consequence is Γ j= σ. In particular note that Γ 6j= σ, that is, σ is not a logical consequence of Γ, if and only if there is some interpretation A of L such that A j= θi for all θi 2 Γ but A 6j= σ, A is not a model of σ. As a special limiting case note that ; j= σ, which we will write simply as j= σ, means that A j= σ for every interpretation A of L. Such a sentence σ is said to be logically true (or valid). How would one actually show that Γ j= σ for specific Γ and σ? There will be infinitely many different interpretations for L so it is not feasible to check each one in turn, and for that matter it may not be possible to decide whether a par- ticular sentence is or is not true on a particular structure. Here is where another fundamental building block comes in, namely the formal analogue of mathematical proofs. A proof of σ from a set Γ of hypotheses is a finite sequence of statements σ0; : : : ; σk where σ is σk and each statement in the sequence is justified by some explicitly stated rule which guarantees that it is a logical consequence of Γ and the preceding statements. The point of requiring use only of rules which are explicitly stated and given in advance is that one should be able to check whether or not a given sequence σ0; : : : ; σk is a proof of σ from Γ. The notation Γ ` σ will mean that there is a formal proof (also called a deduc- tion or derivation) of σ from Γ. Of course this notion only becomes precise when we actually give the rules allowed. Provided the rules are correctly chosen, we will have the implication if Γ ` σ then Γ j= σ. Obviously we want to know that our rules are adequate to derive all logical consequences. That is the content of the following fundamental result: Theorem -1.1 (Completeness Theorem (K. G¨odel)). For sentences of a first- order language L, we have Γ ` σ if and only if Γ j= σ. First-order languages are the most widely studied in modern mathematical logic, largely to obtain the benefit of the Completeness Theorem and its applica- tions. In these notes we will study first-order languages almost exclusively. Part ?? is devoted to the detailed construction of our \model of reasoning" for first-order languages. It culminates in the proof of the Completeness Theorem and derivation of some of its consequences. 0. INTRODUCTION: WHAT IS LOGIC? 3 Part ?? is an introduction to Model Theory. If Γ is a set of sentences of L, then Mod(Γ), the class of all models of Γ, is the class of all interpretations of L which make all sentences in Γ true. Model Theory discusses the properties such classes of interpretations have. One important result of model theory for first-order languages is the Compactness Theorem, which states that if Mod(Γ) = ; then there must be some finite Γ0 ⊆ Γ with Mod(Γ0) = ;. Part ?? discusses the famous incompleteness and undecidability results of G'odel, Church, Tarski, et al. The fundamental problem here (the decision problem) is whether there is an effective procedure to decide whether or not a sentence is logi- cally true. The Completeness Theorem does not automatically yield such a method. Part ?? discusses topics from the abstract theory of computable functions (Re- cursion Theory). Part 1 Elementary Logic CHAPTER 1 Sentential Logic 0. Introduction Our goal, as explained in Chapter 0, is to define a class of formal languages whose sentences include formalizations of the sttements commonly used in math- ematics and whose interpretatins include the usual mathematical structures. The details of this become quite intricate, which obscures the \big picture." We there- fore first consider a much simpler situation and carry out our program in this simpler context. The outline remains the same, and we will use some of the same ideas and techniques{especially the interplay of definition by recursion and proof by induction{when we come to first-order languages.
Recommended publications
  • 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.
    [Show full text]
  • COMPSCI 501: Formal Language Theory Insights on Computability Turing Machines Are a Model of Computation Two (No Longer) Surpris
    Insights on Computability Turing machines are a model of computation COMPSCI 501: Formal Language Theory Lecture 11: Turing Machines Two (no longer) surprising facts: Marius Minea Although simple, can describe everything [email protected] a (real) computer can do. University of Massachusetts Amherst Although computers are powerful, not everything is computable! Plus: “play” / program with Turing machines! 13 February 2019 Why should we formally define computation? Must indeed an algorithm exist? Back to 1900: David Hilbert’s 23 open problems Increasingly a realization that sometimes this may not be the case. Tenth problem: “Occasionally it happens that we seek the solution under insufficient Given a Diophantine equation with any number of un- hypotheses or in an incorrect sense, and for this reason do not succeed. known quantities and with rational integral numerical The problem then arises: to show the impossibility of the solution under coefficients: To devise a process according to which the given hypotheses or in the sense contemplated.” it can be determined in a finite number of operations Hilbert, 1900 whether the equation is solvable in rational integers. This asks, in effect, for an algorithm. Hilbert’s Entscheidungsproblem (1928): Is there an algorithm that And “to devise” suggests there should be one. decides whether a statement in first-order logic is valid? Church and Turing A Turing machine, informally Church and Turing both showed in 1936 that a solution to the Entscheidungsproblem is impossible for the theory of arithmetic. control To make and prove such a statement, one needs to define computability. In a recent paper Alonzo Church has introduced an idea of “effective calculability”, read/write head which is equivalent to my “computability”, but is very differently defined.
    [Show full text]
  • Algebras in Which Every Subalgebra Is Noetherian
    PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 142, Number 9, September 2014, Pages 2983–2990 S 0002-9939(2014)12052-1 Article electronically published on May 21, 2014 ALGEBRAS IN WHICH EVERY SUBALGEBRA IS NOETHERIAN D. ROGALSKI, S. J. SIERRA, AND J. T. STAFFORD (Communicated by Birge Huisgen-Zimmermann) Abstract. We show that the twisted homogeneous coordinate rings of elliptic curves by infinite order automorphisms have the curious property that every subalgebra is both finitely generated and noetherian. As a consequence, we show that a localisation of a generic Skylanin algebra has the same property. 1. Introduction Throughout this paper k will denote an algebraically closed field and all algebras will be k-algebras. It is not hard to show that if C is a commutative k-algebra with the property that every subalgebra is finitely generated (or noetherian), then C must be of Krull dimension at most one. (See Section 3 for one possible proof.) The aim of this paper is to show that this property holds more generally in the noncommutative universe. Before stating the result we need some notation. Let X be a projective variety ∗ with invertible sheaf M and automorphism σ and write Mn = M⊗σ (M) ···⊗ n−1 ∗ (σ ) (M). Then the twisted homogeneous coordinate ring of X with respect to k M 0 M this data is the -algebra B = B(X, ,σ)= n≥0 H (X, n), under a natu- ral multiplication. This algebra is fundamental to the theory of noncommutative algebraic geometry; see for example [ATV1, St, SV]. In particular, if S is the 3- dimensional Sklyanin algebra, as defined for example in [SV, Example 8.3], then B(E,L,σ)=S/gS for a central element g ∈ S and some invertible sheaf L over an elliptic curve E.WenotethatS is a graded ring that can be regarded as the “co- P2 P2 ordinate ring of a noncommutative ” or as the “coordinate ring of nc”.
    [Show full text]
  • Lie Group Homomorphism Φ: → Corresponds to a Linear Map Φ: →
    More on Lie algebras Wednesday, February 14, 2018 8:27 AM Simple Lie algebra: dim 2 and the only ideals are 0 and itself. Have classification by Dynkin diagram. For instance, , is simple: take the basis 01 00 10 , , . 00 10 01 , 2,, 2,, . Suppose is in the ideal. , 2. , , 2. If 0, then X is in the ideal. Then ,,, 2imply that H and Y are also in the ideal which has to be 2, . The case 0: do the same argument for , , , then the ideal is 2, unless 0. The case 0: , 2,, 2implies the ideal is 2, unless 0. The ideal is {0} if 0. Commutator ideal: , Span,: , ∈. An ideal is in particular a Lie subalgebra. Keep on taking commutator ideals, get , , ⊂, … called derived series. Solvable: 0 for some j. If is simple, for all j. Levi decomposition: any Lie algebra is the semi‐direct product of a solvable ideal and a semisimple subalgebra. Similar concept: , , , , …, ⊂. Sequence of ideals called lower central series. Nilpotent: 0 for some j. ⊂ . Hence nilpotent implies solvable. For instance, the Lie algebra of nilpotent upper triangular matrices is nilpotent and hence solvable. Can take the basis ,, . Then ,,, 0if and , if . is spanned by , for and hence 0. The Lie algebra of upper triangular matrices is solvable but not nilpotent. Can take the basis ,,,, ,,…,,. Similar as above and ,,,0. . So 0. But for all 1. Recall that any Lie group homomorphism Φ: → corresponds to a linear map ϕ: → . In the proof that a continuous homomorphism is smooth. Lie group Page 1 Since Φ ΦΦΦ , ∘ Ad AdΦ ∘.
    [Show full text]
  • Against Logical Form
    Against logical form Zolta´n Gendler Szabo´ Conceptions of logical form are stranded between extremes. On one side are those who think the logical form of a sentence has little to do with logic; on the other, those who think it has little to do with the sentence. Most of us would prefer a conception that strikes a balance: logical form that is an objective feature of a sentence and captures its logical character. I will argue that we cannot get what we want. What are these extreme conceptions? In linguistics, logical form is typically con- ceived of as a level of representation where ambiguities have been resolved. According to one highly developed view—Chomsky’s minimalism—logical form is one of the outputs of the derivation of a sentence. The derivation begins with a set of lexical items and after initial mergers it splits into two: on one branch phonological operations are applied without semantic effect; on the other are semantic operations without phono- logical realization. At the end of the first branch is phonological form, the input to the articulatory–perceptual system; and at the end of the second is logical form, the input to the conceptual–intentional system.1 Thus conceived, logical form encompasses all and only information required for interpretation. But semantic and logical information do not fully overlap. The connectives “and” and “but” are surely not synonyms, but the difference in meaning probably does not concern logic. On the other hand, it is of utmost logical importance whether “finitely many” or “equinumerous” are logical constants even though it is hard to see how this information could be essential for their interpretation.
    [Show full text]
  • Scheme Representation for First-Logic
    Scheme representation for first-order logic Spencer Breiner Carnegie Mellon University Advisor: Steve Awodey Committee: Jeremy Avigad Hans Halvorson Pieter Hofstra February 12, 2014 arXiv:1402.2600v1 [math.LO] 11 Feb 2014 Contents 1 Logical spectra 11 1.1 The spectrum M0 .......................... 11 1.2 Sheaves on M0 ............................ 20 1.3 Thegenericmodel .......................... 24 1.4 The spectral groupoid M = Spec(T)................ 27 1.5 Stability, compactness and definability . 31 1.6 Classicalfirst-orderlogic. 37 2 Pretopos Logic 43 2.1 Coherentlogicandpretoposes. 43 2.2 Factorization in Ptop ........................ 51 2.3 Semantics, slices and localization . 57 2.4 Themethodofdiagrams. .. .. .. .. .. .. .. .. .. 61 2.5 ClassicaltheoriesandBooleanpretoposes . 70 3 Logical Schemes 74 3.1 StacksandSheaves.......................... 74 3.2 Affineschemes ............................ 80 3.3 Thecategoryoflogicalschemes . 87 3.4 Opensubschemesandgluing . 93 3.5 Limitsofschemes........................... 98 1 4 Applications 110 4.1 OT asasite..............................111 4.2 Structuresheafasuniverse . 119 4.3 Isotropy ................................125 4.4 ConceptualCompleteness . 134 2 Introduction Although contemporary model theory has been called “algebraic geometry mi- nus fields” [15], the formal methods of the two fields are radically different. This dissertation aims to shrink that gap by presenting a theory of “logical schemes,” geometric entities which relate to first-order logical theories in much the same way that
    [Show full text]
  • Thinking Recursively Part Four
    Thinking Recursively Part Four Announcements ● Assignment 2 due right now. ● Assignment 3 out, due next Monday, April 30th at 10:00AM. ● Solve cool problems recursively! ● Sharpen your recursive skillset! A Little Word Puzzle “What nine-letter word can be reduced to a single-letter word one letter at a time by removing letters, leaving it a legal word at each step?” Shrinkable Words ● Let's call a word with this property a shrinkable word. ● Anything that isn't a word isn't a shrinkable word. ● Any single-letter word is shrinkable ● A, I, O ● Any multi-letter word is shrinkable if you can remove a letter to form a word, and that word itself is shrinkable. ● So how many shrinkable words are there? Recursive Backtracking ● The function we wrote last time is an example of recursive backtracking. ● At each step, we try one of many possible options. ● If any option succeeds, that's great! We're done. ● If none of the options succeed, then this particular problem can't be solved. Recursive Backtracking if (problem is sufficiently simple) { return whether or not the problem is solvable } else { for (each choice) { try out that choice. if it succeeds, return success. } return failure } Failure in Backtracking S T A R T L I N G Failure in Backtracking S T A R T L I N G S T A R T L I G Failure in Backtracking S T A R T L I N G S T A R T L I G Failure in Backtracking S T A R T L I N G Failure in Backtracking S T A R T L I N G S T A R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G S T R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G S T R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G S T A R I N G Failure in Backtracking ● Returning false in recursive backtracking does not mean that the entire problem is unsolvable! ● Instead, it just means that the current subproblem is unsolvable.
    [Show full text]
  • Truth-Bearers and Truth Value*
    Truth-Bearers and Truth Value* I. Introduction The purpose of this document is to explain the following concepts and the relationships between them: statements, propositions, and truth value. In what follows each of these will be discussed in turn. II. Language and Truth-Bearers A. Statements 1. Introduction For present purposes, we will define the term “statement” as follows. Statement: A meaningful declarative sentence.1 It is useful to make sure that the definition of “statement” is clearly understood. 2. Sentences in General To begin with, a statement is a kind of sentence. Obviously, not every string of words is a sentence. Consider: “John store.” Here we have two nouns with a period after them—there is no verb. Grammatically, this is not a sentence—it is just a collection of words with a dot after them. Consider: “If I went to the store.” This isn’t a sentence either. “I went to the store.” is a sentence. However, using the word “if” transforms this string of words into a mere clause that requires another clause to complete it. For example, the following is a sentence: “If I went to the store, I would buy milk.” This issue is not merely one of conforming to arbitrary rules. Remember, a grammatically correct sentence expresses a complete thought.2 The construction “If I went to the store.” does not do this. One wants to By Dr. Robert Tierney. This document is being used by Dr. Tierney for teaching purposes and is not intended for use or publication in any other manner. 1 More precisely, a statement is a meaningful declarative sentence-type.
    [Show full text]
  • Continuous First-Order Logic
    Continuous First-Order Logic Wesley Calvert Calcutta Logic Circle 4 September 2011 Wesley Calvert (SIU / IMSc) Continuous First-Order Logic 4 September 2011 1 / 45 Definition A first-order theory T is said to be stable iff there are less than the maximum possible number of types over T , up to equivalence. Theorem (Shelah's Main Gap Theorem) If T is a first-order theory and is stable and . , then the class of models looks like . Otherwise, there's no hope. Model-Theoretic Beginnings Problem Given a theory T , describe the structure of models of T . Wesley Calvert (SIU / IMSc) Continuous First-Order Logic 4 September 2011 2 / 45 Theorem (Shelah's Main Gap Theorem) If T is a first-order theory and is stable and . , then the class of models looks like . Otherwise, there's no hope. Model-Theoretic Beginnings Problem Given a theory T , describe the structure of models of T . Definition A first-order theory T is said to be stable iff there are less than the maximum possible number of types over T , up to equivalence. Wesley Calvert (SIU / IMSc) Continuous First-Order Logic 4 September 2011 2 / 45 Model-Theoretic Beginnings Problem Given a theory T , describe the structure of models of T . Definition A first-order theory T is said to be stable iff there are less than the maximum possible number of types over T , up to equivalence. Theorem (Shelah's Main Gap Theorem) If T is a first-order theory and is stable and . , then the class of models looks like . Otherwise, there's no hope.
    [Show full text]
  • Gauging the Octonion Algebra
    UM-P-92/60_» Gauging the octonion algebra A.K. Waldron and G.C. Joshi Research Centre for High Energy Physics, University of Melbourne, Parkville, Victoria 8052, Australia By considering representation theory for non-associative algebras we construct the fundamental and adjoint representations of the octonion algebra. We then show how these representations by associative matrices allow a consistent octonionic gauge theory to be realized. We find that non-associativity implies the existence of new terms in the transformation laws of fields and the kinetic term of an octonionic Lagrangian. PACS numbers: 11.30.Ly, 12.10.Dm, 12.40.-y. Typeset Using REVTEX 1 L INTRODUCTION The aim of this work is to genuinely gauge the octonion algebra as opposed to relating properties of this algebra back to the well known theory of Lie Groups and fibre bundles. Typically most attempts to utilise the octonion symmetry in physics have revolved around considerations of the automorphism group G2 of the octonions and Jordan matrix representations of the octonions [1]. Our approach is more simple since we provide a spinorial approach to the octonion symmetry. Previous to this work there were already several indications that this should be possible. To begin with the statement of the gauge principle itself uno theory shall depend on the labelling of the internal symmetry space coordinates" seems to be independent of the exact nature of the gauge algebra and so should apply equally to non-associative algebras. The octonion algebra is an alternative algebra (the associator {x-1,y,i} = 0 always) X -1 so that the transformation law for a gauge field TM —• T^, = UY^U~ — ^(c^C/)(/ is well defined for octonionic transformations U.
    [Show full text]
  • Chapter 6 Formal Language Theory
    Chapter 6 Formal Language Theory In this chapter, we introduce formal language theory, the computational theories of languages and grammars. The models are actually inspired by formal logic, enriched with insights from the theory of computation. We begin with the definition of a language and then proceed to a rough characterization of the basic Chomsky hierarchy. We then turn to a more de- tailed consideration of the types of languages in the hierarchy and automata theory. 6.1 Languages What is a language? Formally, a language L is defined as as set (possibly infinite) of strings over some finite alphabet. Definition 7 (Language) A language L is a possibly infinite set of strings over a finite alphabet Σ. We define Σ∗ as the set of all possible strings over some alphabet Σ. Thus L ⊆ Σ∗. The set of all possible languages over some alphabet Σ is the set of ∗ all possible subsets of Σ∗, i.e. 2Σ or ℘(Σ∗). This may seem rather simple, but is actually perfectly adequate for our purposes. 6.2 Grammars A grammar is a way to characterize a language L, a way to list out which strings of Σ∗ are in L and which are not. If L is finite, we could simply list 94 CHAPTER 6. FORMAL LANGUAGE THEORY 95 the strings, but languages by definition need not be finite. In fact, all of the languages we are interested in are infinite. This is, as we showed in chapter 2, also true of human language. Relating the material of this chapter to that of the preceding two, we can view a grammar as a logical system by which we can prove things.
    [Show full text]
  • Proof Diagrams for Multiplicative Linear Logic
    Proof Diagrams for Multiplicative Linear Logic Matteo Acclavio I2M Marseille, France Aix-Marseile Universit´e [email protected] The original idea of proof nets can be formulated by means of interaction nets syntax. Additional machinery as switching, jumps and graph connectivity is needed in order to ensure correspondence between a proof structure and a correct proof in sequent calculus. In this paper we give an interpretationof proof nets in the syntax of string diagrams. Even though we lose standard proof equivalence, our construction allows to define a framework where soundness and well-typeness of a diagram can be verified in linear time. Introduction Proof nets are a geometrical representation of linear logic proofs introduced by J-Y.Girard [5]. The build- ing blocks of proof nets are called proof structures that have been generalized by Y. Lafont [11] in the so-called interaction nets. To recognize if a proof structure is a proof net one needs to verify its sequen- tializability property, that is, whether it corresponds to a linear logic proof derivation. Following Girard’s original correction criterion, others methods have been introduced, notably by Danos-Regnier [4], that ensures graph acyclicity by a notion of switchings on ⊗ cells, and by Guerrini [7], that reformulates correction by means of graph contractability. Proof structures allow to recover the semantic equivalence of derivation under commutation and permutation of some inference rules. Unfortunately this property makes ineffective the aforementioned criteria in presence of the multiplicative unit ⊥. In order to recover a sequentialization condition for the multiplicative fragment with units, Girard has introduced the notion of jumps [6].
    [Show full text]