A Response to Liu and Chou (2012) and to López Astorga (2013) David P
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Classifying Material Implications Over Minimal Logic
Classifying Material Implications over Minimal Logic Hannes Diener and Maarten McKubre-Jordens March 28, 2018 Abstract The so-called paradoxes of material implication have motivated the development of many non- classical logics over the years [2–5, 11]. In this note, we investigate some of these paradoxes and classify them, over minimal logic. We provide proofs of equivalence and semantic models separating the paradoxes where appropriate. A number of equivalent groups arise, all of which collapse with unrestricted use of double negation elimination. Interestingly, the principle ex falso quodlibet, and several weaker principles, turn out to be distinguishable, giving perhaps supporting motivation for adopting minimal logic as the ambient logic for reasoning in the possible presence of inconsistency. Keywords: reverse mathematics; minimal logic; ex falso quodlibet; implication; paraconsistent logic; Peirce’s principle. 1 Introduction The project of constructive reverse mathematics [6] has given rise to a wide literature where various the- orems of mathematics and principles of logic have been classified over intuitionistic logic. What is less well-known is that the subtle difference that arises when the principle of explosion, ex falso quodlibet, is dropped from intuitionistic logic (thus giving (Johansson’s) minimal logic) enables the distinction of many more principles. The focus of the present paper are a range of principles known collectively (but not exhaustively) as the paradoxes of material implication; paradoxes because they illustrate that the usual interpretation of formal statements of the form “. → . .” as informal statements of the form “if. then. ” produces counter-intuitive results. Some of these principles were hinted at in [9]. Here we present a carefully worked-out chart, classifying a number of such principles over minimal logic. -
Principles of Logic Roehampton : Printed by John Griffin ^Principles of Logic
PRINCIPLES OF LOGIC ROEHAMPTON : PRINTED BY JOHN GRIFFIN ^PRINCIPLES OF LOGIC By GEORGE HAYWARD JOYCE, S.J. M.A., ORIEL COLLEGE, OXFORD PROFESSOR OF LOGIC, ST. MARY S HALL, STONYHURST *.r LONGMANS, GREEN & CO 39, PATERNOSTER ROW, LONDON NEW YORK. BOMBAY, AND CALCUTTA 1908 INTRODUCTION THIS work is an attempt at a presentment of what is fre quently termed the Traditional Logic, and is intended for those who are making acquaintance with philosophical questions for the first time. Yet it is impossible, even in a text-book such as this, to deal with logical questions save in connexion with definite metaphysical and epistemolo- gical principles. Logic, as the theory of the mind s rational processes in regard of their validity, must neces sarily be part of a larger philosophical system. Indeed when this is not the case, it becomes a mere collection of technical rules, possessed of little importance and of less interest. The point of view adopted in this book is that of the Scholastic as far as is philosophy ; and compatible with the size and purpose of the work, some attempt has been made to vindicate the fundamental principles on which that philosophy is based. From one point of view, this position should prove a source of strength. The thinkers who elaborated our sys tem of Logic, were Scholastics. With the principles of that philosophy, its doctrines and its rules are in full accord. In the light of Scholasticism, the system is a connected whole ; and the subjects, traditionally treated in it, have each of them its legitimate place. -
Two Sources of Explosion
Two sources of explosion Eric Kao Computer Science Department Stanford University Stanford, CA 94305 United States of America Abstract. In pursuit of enhancing the deductive power of Direct Logic while avoiding explosiveness, Hewitt has proposed including the law of excluded middle and proof by self-refutation. In this paper, I show that the inclusion of either one of these inference patterns causes paracon- sistent logics such as Hewitt's Direct Logic and Besnard and Hunter's quasi-classical logic to become explosive. 1 Introduction A central goal of a paraconsistent logic is to avoid explosiveness { the inference of any arbitrary sentence β from an inconsistent premise set fp; :pg (ex falso quodlibet). Hewitt [2] Direct Logic and Besnard and Hunter's quasi-classical logic (QC) [1, 5, 4] both seek to preserve the deductive power of classical logic \as much as pos- sible" while still avoiding explosiveness. Their work fits into the ongoing research program of identifying some \reasonable" and \maximal" subsets of classically valid rules and axioms that do not lead to explosiveness. To this end, it is natural to consider which classically sound deductive rules and axioms one can introduce into a paraconsistent logic without causing explo- siveness. Hewitt [3] proposed including the law of excluded middle and the proof by self-refutation rule (a very special case of proof by contradiction) but did not show whether the resulting logic would be explosive. In this paper, I show that for quasi-classical logic and its variant, the addition of either the law of excluded middle or the proof by self-refutation rule in fact leads to explosiveness. -
Relevant and Substructural Logics
Relevant and Substructural Logics GREG RESTALL∗ PHILOSOPHY DEPARTMENT, MACQUARIE UNIVERSITY [email protected] June 23, 2001 http://www.phil.mq.edu.au/staff/grestall/ Abstract: This is a history of relevant and substructural logics, written for the Hand- book of the History and Philosophy of Logic, edited by Dov Gabbay and John Woods.1 1 Introduction Logics tend to be viewed of in one of two ways — with an eye to proofs, or with an eye to models.2 Relevant and substructural logics are no different: you can focus on notions of proof, inference rules and structural features of deduction in these logics, or you can focus on interpretations of the language in other structures. This essay is structured around the bifurcation between proofs and mod- els: The first section discusses Proof Theory of relevant and substructural log- ics, and the second covers the Model Theory of these logics. This order is a natural one for a history of relevant and substructural logics, because much of the initial work — especially in the Anderson–Belnap tradition of relevant logics — started by developing proof theory. The model theory of relevant logic came some time later. As we will see, Dunn's algebraic models [76, 77] Urquhart's operational semantics [267, 268] and Routley and Meyer's rela- tional semantics [239, 240, 241] arrived decades after the initial burst of ac- tivity from Alan Anderson and Nuel Belnap. The same goes for work on the Lambek calculus: although inspired by a very particular application in lin- guistic typing, it was developed first proof-theoretically, and only later did model theory come to the fore. -
Logic for Computer Science. Knowledge Representation and Reasoning
Lecture Notes 1 Logic for Computer Science. Knowledge Representation and Reasoning. Lecture Notes for Computer Science Students Faculty EAIiIB-IEiT AGH Antoni Lig˛eza Other support material: http://home.agh.edu.pl/~ligeza https://ai.ia.agh.edu.pl/pl:dydaktyka:logic: start#logic_for_computer_science2020 c Antoni Lig˛eza:2020 Lecture Notes 2 Inference and Theorem Proving in Propositional Calculus • Tasks and Models of Automated Inference, • Theorem Proving models, • Some important Inference Rules, • Theorems of Deduction: 1 and 2, • Models of Theorem Proving, • Examples of Proofs, • The Resolution Method, • The Dual Resolution Method, • Logical Derivation, • The Semantic Tableau Method, • Constructive Theorem Proving: The Fitch System, • Example: The Unicorn, • Looking for Models: Towards SAT. c Antoni Lig˛eza Lecture Notes 3 Logic for KRR — Tasks and Tools • Theorem Proving — Verification of Logical Consequence: ∆ j= H; • Method of Theorem Proving: Automated Inference —- Derivation: ∆ ` H; • SAT (checking for models) — satisfiability: j=I H (if such I exists); • un-SAT verification — unsatisfiability: 6j=I H (for any I); • Tautology verification (completeness): j= H • Unsatisfiability verification 6j= H Two principal issues: • valid inference rules — checking: (∆ ` H) −! (∆ j= H) • complete inference rules — checking: (∆ j= H) −! (∆ ` H) c Antoni Lig˛eza Lecture Notes 4 Two Possible Fundamental Approaches: Checking of Interpretations versus Logical Inference Two basic approaches – reasoning paradigms: • systematic evaluation of possible interpretations — the 0-1 method; problem — combinatorial explosion; for n propositional variables we have 2n interpretations! • logical inference— derivation — with rules preserving logical conse- quence. Notation: formula H (a Hypothesis) is derivable from ∆ (a Knowledge Base; a set of domain axioms): ∆ ` H This means that there exists a sequence of applications of inference rules, such that H is mechanically derived from ∆. -
CS245 Logic and Computation
CS245 Logic and Computation Alice Gao December 9, 2019 Contents 1 Propositional Logic 3 1.1 Translations .................................... 3 1.2 Structural Induction ............................... 8 1.2.1 A template for structural induction on well-formed propositional for- mulas ................................... 8 1.3 The Semantics of an Implication ........................ 12 1.4 Tautology, Contradiction, and Satisfiable but Not a Tautology ........ 13 1.5 Logical Equivalence ................................ 14 1.6 Analyzing Conditional Code ........................... 16 1.7 Circuit Design ................................... 17 1.8 Tautological Consequence ............................ 18 1.9 Formal Deduction ................................. 21 1.9.1 Rules of Formal Deduction ........................ 21 1.9.2 Format of a Formal Deduction Proof .................. 23 1.9.3 Strategies for writing a formal deduction proof ............ 23 1.9.4 And elimination and introduction .................... 25 1.9.5 Implication introduction and elimination ................ 26 1.9.6 Or introduction and elimination ..................... 28 1.9.7 Negation introduction and elimination ................. 30 1.9.8 Putting them together! .......................... 33 1.9.9 Putting them together: Additional exercises .............. 37 1.9.10 Other problems .............................. 38 1.10 Soundness and Completeness of Formal Deduction ............... 39 1.10.1 The soundness of inference rules ..................... 39 1.10.2 Soundness and Completeness -
Natural Deduction
Introduction to Logic Natural Deduction Michael Genesereth Computer Science Department Stanford University Example - Transitivity Given (p ⇒ q) and (q ⇒ r), prove (p ⇒ r). 1. p ⇒ q Premise 2. q ⇒ r Premise 3. (q ⇒ r) ⇒ (p ⇒ (q ⇒ r)) IC 4. (p ⇒ (q ⇒ r)) IE: 2, 3 5. (p ⇒ (q ⇒ r)) ⇒ ((p ⇒ q) ⇒ (p ⇒ r)) ID 6. (p ⇒ q) ⇒ (p ⇒ r) IE: 4, 5 7. p ⇒ r IE: 1, 6 Structured Proofs Natural Deduction Making Assumptions e.g. assume p Applying Ordinary Rules of Inference to derive conclusions e.g. derive q Discharging Assumptions leading to implications e.g. conclude p ⇒ q Conditional Proofs Making Assumptions In a conditional proof, it is permissible to make an arbitrary assumption or hypothetical in a nested proof. The assumption need not be in the original premise set. Such assumptions can be used within the nested proof. However, they may not be used outside of the subproof in which they appear. Example Ordinary Rules of Inference An ordinary rule of inference applies to a proof at any level of nesting if and only there is an instance of the rule in which all of the premises occur earlier in the nested proof or in some “superproof” of the nested proof. Importantly, it is not permissible to apply an ordinary rule of inference to premises in subproofs of a nested proof or in other subproofs of a superproof of a nested proof. Example Bad Proof X X Bad Proof X X Structured Rules of Inference A structured rule of inference is a pattern of reasoning consisting of one or more schemas, called premises, and one or more additional schemas, called conclusions, in which one of the premises is a condition of the form φ ⊢ ψ. -
Truth Determinants. Another Theory of Meaning for the Propositional Connectives
Philosophica 19, 1977 (1), pp. 11-32 11 TRUTH DETERMINANTS. ANOTHER THEORY OF MEANING FOR THE PROPOSITIONAL CONNECTIVES Niels Egmont Christensen --- The aim of this paper is to show that the truth-functional theory of meaning of the propositional connectives could be contrasted with another related, so-called truth-determinantal theory. With both theories and their ensuing different logics at hand we understand better the inadequacy of truth-functional logic and the difficulties in trying to make up for its shortcomings. 1. Introduction When raising the question about the philosophical relevance of the concept of meaning, the answer is in a sense both immediate and 1 impressive. For as Michael Dummett, in particular, has pointed out , there is, according to Frege, one part of philosophy that is the basis on which all other disciplines rest, viz. the theory of meaning. This has not always been the view of philosophers; Descartes, for instance, and many of his followers saw epistemology as the fundamental theory on which all other forms of philosophy should be based. But if we are prepared to take the Fregean approach and to admit instead theory of meaning as the starting point of philosophy there are two things to note. First, this order of approach will never prevent us from moving on from theory of meaning to questions about what we know and how to justify it. Merely we should be clear about the meaning of epistemological words before entering into a territory that difficult. Second, being clear about the meaning of a philosophical interesting word is very seldom, if ever, the same as to be able in language, ones own or in another language, to state the meaning of the wo rd in question. -
Paradox and Foundation Zach Weber Submitted in Total Fulfilment of The
Paradox and Foundation Zach Weber Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy May 2009 School of Philosophy, Anthropology and Social Inquiry The University of Melbourne This is to certify that - the thesis comprises only my original work towards the PhD, - due acknowledgement has been made in the text to all other material used, - the thesis is less than 100,000 words in length. Preface Dialethic paraconsistency is an approach to formal and philosophical theories in which some but not all contradictions are true. Advancing that program, this thesis is about paradoxes and the foundations of mathematics, and is divided accordingly into two main parts. The first part concerns the history and philosophy of set theory from Cantor through the independence proofs, focusing on the set concept. A set is any col- lection of objects that is itself an object, with identity completely determined by membership. The set concept is called naive because it is inconsistent. I argue that the set concept is inherently and rightly paradoxical, because sets are both intensional and extensional objects: Sets are predicates in extension. All consistent characterizations of sets are either not explanatory or not coherent. To understand sets, we need to reason about them with an appropriate logic; paraconsistent naive set theory is situated as a continuation of the original foundational project. The second part produces a set theory deduced from an unrestricted compre- hension principle using the weak relevant logic DLQ, dialethic logic with quantifiers. I discuss some of the problems involved with embedding in DLQ, especially related to identity and substitution. -
Logic and Proof Release 3.18.4
Logic and Proof Release 3.18.4 Jeremy Avigad, Robert Y. Lewis, and Floris van Doorn Sep 10, 2021 CONTENTS 1 Introduction 1 1.1 Mathematical Proof ............................................ 1 1.2 Symbolic Logic .............................................. 2 1.3 Interactive Theorem Proving ....................................... 4 1.4 The Semantic Point of View ....................................... 5 1.5 Goals Summarized ............................................ 6 1.6 About this Textbook ........................................... 6 2 Propositional Logic 7 2.1 A Puzzle ................................................. 7 2.2 A Solution ................................................ 7 2.3 Rules of Inference ............................................ 8 2.4 The Language of Propositional Logic ................................... 15 2.5 Exercises ................................................. 16 3 Natural Deduction for Propositional Logic 17 3.1 Derivations in Natural Deduction ..................................... 17 3.2 Examples ................................................. 19 3.3 Forward and Backward Reasoning .................................... 20 3.4 Reasoning by Cases ............................................ 22 3.5 Some Logical Identities .......................................... 23 3.6 Exercises ................................................. 24 4 Propositional Logic in Lean 25 4.1 Expressions for Propositions and Proofs ................................. 25 4.2 More commands ............................................ -
Propositional Logic
Propositional logic Readings: Sections 1.1 and 1.2 of Huth and Ryan. In this module, we will consider propositional logic, which will look familiar to you from Math 135 and CS 251. The difference here is that we first define a formal proof system and practice its use before talking about a semantic interpretation (which will also be familiar) and showing that these two notions coincide. 1 Declarative sentences (1.1) A proposition or declarative sentence is one that can, in principle, be argued as being true or false. Examples: “My car is green” or “Susan was born in Canada”. Many sentences are not declarative, such as “Help!”, “What time is it?”, or “Get me something to eat.” The declarative sentences above are atomic; they cannot be decomposed further. A sentence like “My car is green AND you do not have a car” is a compound sentence or compositional sentence. 2 To clarify the manipulations we perform in logical proofs, we will represent declarative sentences symbolically by atoms such as p, q, r. (We avoid t, f , T , F for reasons which will become evident.) Compositional sentences will be represented by formulas, which combine atoms with connectives. Formulas are intended to symbolically represent statements in the type of mathematical or logical reasoning we have done in the past. Our standard set of connectives will be , , , and . (In Math : ^ _ ! 135, you also used , which we will not use.) Soon, we will $ describe the set of formulas as a formal language; for the time being, we use an informal description. -
CHAPTER 3 Methods of Inference
04-108 C03 p115-188 pp3 8/30/04 2:48 PM Page 115 CHAPTER 3 Methods of Inference 3.1 INTRODUCTION This chapter is an introduction to reasoning about knowledge. It is important to separate the meaning of the words used in reasoning from the reasoning itself so we will cover it in a formal way by discussing various methods of inference (Russell 03). In particular we will look at an important type of reasoning in ex- pert systems in which conclusions follow from facts by using rules. It is particu- larly important in expert systems because making inferences is the fundamental method by which expert systems solve problems (Klir 98). As mentioned in Chapter 1, expert systems are commonly used when an in- adequate algorithm or no algorithmic solution exists. The expert system should make a chain of inferences to come up with a solution just as a person would, especially when faced with incomplete or missing information. A simple com- puter program that uses an algorithm to solve a problem cannot supply an answer if all the parameters needed are not given. However an expert system makes a best guess just like a human would when a problem needs to be solved and the optimum solution cannot be determined. The idea is to come up with a solution just as people come up with a (hopefully) good solution rather than none at all. While the solution may only be 95% as ef- ficient as the optimum solution, that’s still better than no solution. This type of approach is really nothing new.