Propositional Logic
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Here the Handbook of Abstracts
Handbook of the 4th World Congress and School on Universal Logic March 29 { April 07, 2013 Rio de Janeiro, Brazil UNILOG'2013 www.uni-log.org Edited by Jean-Yves B´eziau,Arthur Buchsbaum and Alexandre Costa-Leite Revised by Alvaro Altair Contents 1 Organizers of UNILOG'13 5 1.1 Scientific Committee . .5 1.2 Organizing Committee . .5 1.3 Supporting Organizers . .6 2 Aim of the event 6 3 4th World School on Universal Logic 8 3.1 Aim of the School . .8 3.2 Tutorials . .9 3.2.1 Why Study Logic? . .9 3.2.2 How to get your Logic Article or Book published in English9 3.2.3 Non-Deterministic Semantics . 10 3.2.4 Logic for the Blind as a Stimulus for the Design of Inno- vative Teaching Materials . 13 3.2.5 Hybrid Logics . 16 3.2.6 Psychology of Reasoning . 17 3.2.7 Truth-Values . 18 3.2.8 The Origin of Indian Logic and Indian Syllogism . 23 3.2.9 Logical Forms . 24 3.2.10 An Introduction to Arabic Logic . 25 3.2.11 Quantum Cognition . 27 3.2.12 Towards a General Theory of Classifications . 28 3.2.13 Connecting Logics . 30 3.2.14 Relativity of Mathematical Concepts . 32 3.2.15 Undecidability and Incompleteness are Everywhere . 33 3.2.16 Logic, Algebra and Implication . 33 3.2.17 Hypersequents and Applications . 35 3.2.18 Introduction to Modern Mathematics . 36 3.2.19 Erotetic Logics . 37 3.2.20 History of Paraconsistent Logic . 38 3.2.21 Institutions . -
Which Quantifiers Are Logical? a Combined Semantical and Inferential Criterion Solomon Feferman1
Which Quantifiers are Logical? A combined semantical and inferential criterion Solomon Feferman1 Abstract. The aim of logic is to characterize the forms of reasoning that lead invariably from true sentences to true sentences, independently of the subject matter; thus its concerns combine semantical and inferential notions in an essential way. Up to now most proposed characterizations of logicality of sentence generating operations have been given either in semantical or inferential terms. This paper offers a combined semantical and inferential criterion for logicality (improving one originally proposed by Jeffery Zucker) and shows that any quantifier that is to be counted as logical according to that criterion is definable in first order logic. The aim of logic is to characterize the forms of reasoning that lead invariably from true sentences to true sentences, independently of the subject matter. The sentences involved are analyzed according to their logical (as opposed to grammatical) structure, i.e. how they are compounded from their parts by means of certain operations on propositions and predicates, of which the familiar ones are the connectives and quantifiers of first order logic. To spell this out in general, one must explain how the truth of compounds under given operations is determined by the truth of the parts, and characterize those forms of rules of inference for the given operations that insure preservation of truth. The so-called problem of “logical constants” (Gomez-Torrente 2002) is to determine all such operations. -
Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives
information Article Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives Roberta Calegari 1,* , Giovanni Ciatto 2 , Enrico Denti 3 and Andrea Omicini 2 1 Alma AI—Alma Mater Research Institute for Human-Centered Artificial Intelligence, Alma Mater Studiorum–Università di Bologna, 40121 Bologna, Italy 2 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 47522 Cesena, Italy; [email protected] (G.C.); [email protected] (A.O.) 3 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 40136 Bologna, Italy; [email protected] * Correspondence: [email protected] Received: 25 February 2020; Accepted: 18 March 2020; Published: 22 March 2020 Abstract: Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future. -
The Nature of Logical Constants
Aporia vol. 27 no. 1—2017 The Nature of Logical Constants LAUREN RICHARDSON haracterizing the distinction between the “logical” and “non-logical” expressions of a language proves a challenging task, and one Cwith significant implications for the nature and scope of logic itself. It is often claimed that logical truths are statements that are “true by virtue of form,” and, likewise, that arguments are logically valid because of their respective “forms,” not because of their contents (Sider 2).1 Take, for example, a straightforward piece of reasoning: (P1) Maria is in Berlin or she is in Vienna. (P2) Maria is not in Berlin. (C) Maria is in Vienna. If this argument is valid—which, of course, in classical logic, it is—then it is typically held to be valid because of the structure of the argument and not because of certain material facts about the world. So it seems as if we should 1 Contrast the idea of truth preservation by virtue of form with the idea of an argument that is truth- preserving by virtue of the meaning of certain terms: (P1*):Nick is a bachelor. (C*): Nick is an unmarried man. We might think that the truth of (P1*) guarantees the truth of (C*), but it is not by virtue of the form of the argument; it is by virtue of the meaning of the expressions “bachelor” and “unmarried man.” Lauren Richardson is studying philosophy at the University of Chicago and will graduate in 2018. Her philosophical interests include philosophy of language, logic, metaethics, and feminism. After graduation, Lauren intends to pursue a graduate degree in philosophy. -
Understanding the Logical Constants and Dispositions
Baltic International Yearbook of Cognition, Logic and Communication Volume 5 MEANING, UNDERSTANDING AND KNOWLEDGE Article 2 2009 Understanding the Logical Constants and Dispositions Corine Besson St Hugh's College, Oxford Follow this and additional works at: https://newprairiepress.org/biyclc This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. Recommended Citation Besson, Corine (2009) "Understanding the Logical Constants and Dispositions," Baltic International Yearbook of Cognition, Logic and Communication: Vol. 5. https://doi.org/10.4148/biyclc.v5i0.279 This Proceeding of the Symposium for Cognition, Logic and Communication is brought to you for free and open access by the Conferences at New Prairie Press. It has been accepted for inclusion in Baltic International Yearbook of Cognition, Logic and Communication by an authorized administrator of New Prairie Press. For more information, please contact [email protected]. Understanding the Logical Constants and Dispositions 2 The Baltic International Yearbook of not explore here how that explanatory connection might be accounted Cognition, Logic and Communication for. But however it is accounted for, it seems that the following min- imal constraint should be met by any account of competence with an October 2010 Volume 5: Meaning, Understanding and Knowledge expression: pages 1-24 DOI: 10.4148/biyclc.v5i0.279 (CT) An account of speakers’ understanding of an expres- sion should be consistent with their correct performances CORINE BESSON with that expression. St Hugh’s College, Oxford This is a weak constraint, since the connection between competence and performance is much tighter. But this will suffice for the purposes UNDERSTANDING THE LOGICAL CONSTANTS AND of this paper. -
The Constituents of the Propositions of Logic Kevin C
10 The Constituents of the Propositions of Logic Kevin C. Klement 1 Introduction Many founders of modern logic—Frege and Russell among them—bemoan- ed the tendency, still found in most textbook treatments, to define the subject matter of logic as “the laws of thought” or “the principles of inference”. Such descriptions fail to capture logic’s objective nature; they make it too dependent on human psychology or linguistic practices. It is one thing to identify what logic is not about. It is another to say what it is about. I tell my students that logic studies relationships between the truth-values of propositions that hold in virtue of their form. But even this characterization leaves me uneasy. I don’t really know what a “form” is, and even worse perhaps, I don’t really know what these “propositions” are that have these forms. If propositions are considered merely as sentences or linguistic assertions, the definition does not seem like much of an improvement over the psychological definitions. Language is a human invention, but logic is more than that, or so it seems. It is perhaps forgiveable then that at certain times Russell would not have been prepared to give a very good answer to the question “What is Logic?”, such as when he attempted, but failed, to compose a paper with that title in October 1912. Given that Russell had recently completed Principia Mathematica, a work alleging to establish the reducibility of mathematics to logic, one might think this overly generous. What does the claim that mathematics reduces to logic come to if we cannot independently speci- Published in Acquaintance, Knowledge and Logic: New Essays on Bertrand Russell’s Problems of Philosophy, edited by Donovan Wishon and Bernard Linsky. -
The Bounds of Logic Part 2
To Ik a Logical Term 37 Chapter 3 To Be a Logical Ternl logic; at the same time, the principles developed by Tarski in the 1930s are still the principles underlying logic in the early 1990s. My interest in Tarski is, Ileedless to say, not historical. I am interested in the modern conception of logic as it evolved out of Tarski's early work in semantics. The Task of Logic and the Origins of Semantics In "The Concept of Truth in Formalized Languages" (1933). "On the Concept of Logical Consequence" (1936a), and "The Establishment of Scientific Semantics" (1936b), Tarski describes the semantic project as comprising two tasks: Since the discovery ofgeneralized quantifiers by A. Mostowski (1957), the I. Definition of the gelleral concept of truth for formalized languages question "What is a logical term?" has taken on a significance it did not 2. Dclinition of the logical concepts of truth, consequence, consistency. have before. Are Mostowski's quantifiers Hlogical" quantifiers'! Do they etc. , differ in any significant way from the standard existential and universal The main purpose of (I) is to secure meta logic against semantic para I quantifiers? What logical operators, if any, has he left out? What. ill doxes. Tarski worried lest the ullcritical usc of semantic concepts prior to I all, are the first- and second-level predicates and rcla tions that can be his work concealed an inconsistency: a hidden fallacy would undermine construed as logical? the cntire venturc. Be therefore sought precise, materially, as well as One way in which I do not want to ask the question is, "What, ill Ihe formally, correct definitions of "truth" and related notions to serve as a I nature ofthings, makes a property or a relation logical'!" On this road lie hedge against paradox. -
Axiomatization of Logic. Truth and Proof. Resolution
H250: Honors Colloquium – Introduction to Computation Axiomatization of Logic. Truth and Proof. Resolution Marius Minea [email protected] How many do we need? (best: few) Are they enough? Can we prove everything? Axiomatization helps answer these questions Motivation: Determining Truth In CS250, we started with truth table proofs. Propositional formula: can always do truth table (even if large) Predicate formula: can’t do truth tables (infinite possibilities) ⇒ must use other proof rules Motivation: Determining Truth In CS250, we started with truth table proofs. Propositional formula: can always do truth table (even if large) Predicate formula: can’t do truth tables (infinite possibilities) ⇒ must use other proof rules How many do we need? (best: few) Are they enough? Can we prove everything? Axiomatization helps answer these questions Symbols of propositional logic: propositions: p, q, r (usually lowercase letters) operators (logical connectives): negation ¬, implication → parentheses () Formulas of propositional logic: defined by structural induction (how to build complex formulas from simpler ones) A formula (compound proposition) is: any proposition (aka atomic formula or variable) (¬α) where α is a formula (α → β) if α and β are formulas Implication and negation suffice! First, Define Syntax We define a language by its symbols and the rules to correctly combine symbols (the syntax) Formulas of propositional logic: defined by structural induction (how to build complex formulas from simpler ones) A formula (compound proposition) is: any -
A Program for the Semantics of Science
MARIO BUNGE A PROGRAM FOR THE SEMANTICS OF SCIENCE I. PROBLEM, METHOD AND GOAL So far exact semantics has been successful only in relation to logic and mathematics. It has had little if anything to say about factual or empirical science. Indeed, no semantical theory supplies an exact and adequate elucidation and systematization of the intuitive notions of factual referen- ce and factual representation, or of factual sense and partial truth of fact, which are peculiar to factual science and therefore central to its philosophy. The semantics of first order logic and the semantics of mathematics (i.e., model theory) do not handle those semantical notions, for they are not interested in external reference and in partial satisfaction. On the other hand factual science is not concerned with interpreting a theory in terms of another theory but in interpreting a theory by reference to things in the real world and their properties. Surely there have been attempts to tackle the semantic peculiarities of factual science. However, the results are rather poor. We have either vigorous intuitions that remain half-baked and scattered, or rigorous formalisms that are irrelevant to real science. The failure to pass from intuition to theory suggests that semanticists have not dealt with genuine factual science but with some oversimplified images of it, such as the view that a scientific theory is just a special case of set theory, so that model theory accounts for factual meaning and for truth of fact. If we wish to do justice to the semantic peculiarities of factual science we must not attempt to force it into any preconceived Procrustean bed: we must proceed from within science. -
Towards a Unified Theory of Intensional Logic Programming
J. LOGIC PROGRAMMING 1992:13:413-440 413 TOWARDS A UNIFIED THEORY OF INTENSIONAL LOGIC PROGRAMMING MEHMET A. ORGUN AND WILLIAM W. WADGE D Intensional Logic Programming is a new form of logic programming based on intensional logic and possible worlds semantics. Intensional logic allows us to use logic programming to specify nonterminating computations and to capture the dynamic aspects of certain problems in a natural and problem-oriented style. The meanings of formulas of an intensional first- order language are given according to intensional interpretations and to elements of a set of possible worlds. Neighborhood semantics is employed as an abstract formulation of the denotations of intensional operators. Then we investigate general properties of intensional operators such as universality, monotonicity, finitariness and conjunctivity. These properties are used as constraints on intensional logic programming systems. The model-theoretic and fixpoint semantics of intensional logic programs are developed in terms of least (minimum) intensional Herbrand models. We show in particular that our results apply to a number of intensional logic programming languages such as Chronolog proposed by Wadge and Tem- plog by Abadi and Manna. We consider some elementary extensions to the theory and show that intensional logic program clauses can be used to define new intensional operators. Intensional logic programs with inten- sional operator definitions are regarded as metatheories. a 1. INTRODUCTION Intensional logic programming (ILP) is a new form of logic programming based on intensional logic and possible world semantics. Intensional logic [20] allows us to describe context-dependent properties of certain problems in a natural and prob- Address correspondence to M. -
What Does It Mean to Say That Logic Is Formal?
WHAT DOES IT MEAN TO SAY THAT LOGIC IS FORMAL? by John Gordon MacFarlane A.B., Philosophy, Harvard College, 1991 M.A., Philosophy, University of Pittsburgh, 1994 M.A., Classics, University of Pittsburgh, 1997 Submitted to the Graduate Faculty of Arts and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2000 i Robert Brandom, Distinguished Service Professor of Philosophy (Director) Nuel Belnap, Alan Ross Anderson Distinguished Professor of Philosophy (Second Reader) Joseph Camp, Professor of Philosophy Danielle Macbeth, Associate Professor of Philosophy, Haverford College (Outside Reader) Kenneth Manders, Associate Professor of Philosophy Gerald Massey, Distinguished Service Professor of Philosophy ii WHAT DOES IT MEAN TO SAY THAT LOGIC IS FORMAL? John Gordon MacFarlane, PhD University of Pittsburgh, 2000 Much philosophy of logic is shaped, explicitly or implicitly, by the thought that logic is distinctively formal and abstracts from material content. The distinction between formal and material does not appear to coincide with the more familiar contrasts between a pri- ori and empirical, necessary and contingent, analytic and synthetic—indeed, it is often invoked to explain these. Nor, it turns out, can it be explained by appeal to schematic inference patterns, syntactic rules, or grammar. What does it mean, then, to say that logic is distinctively formal? Three things: logic is said to be formal (or “topic-neutral”) (1) in the sense that it provides constitutive norms for thought as such, (2) in the sense that it is indifferent to the particular identities of objects, and (3) in the sense that it abstracts entirely from the semantic content of thought. -
Logic for Problem Solving
LOGIC FOR PROBLEM SOLVING Robert Kowalski Imperial College London 12 October 2014 PREFACE It has been fascinating to reflect and comment on the way the topics addressed in this book have developed over the past 40 years or so, since the first version of the book appeared as lecture notes in 1974. Many of the developments have had an impact, not only in computing, but also more widely in such fields as math- ematical, philosophical and informal logic. But just as interesting (and perhaps more important) some of these developments have taken different directions from the ones that I anticipated at the time. I have organised my comments, chapter by chapter, at the end of the book, leaving the 1979 text intact. This should make it easier to compare the state of the subject in 1979 with the subsequent developments that I refer to in my commentary. Although the main purpose of the commentary is to look back over the past 40 years or so, I hope that these reflections will also help to point the way to future directions of work. In particular, I remain confident that the logic-based approach to knowledge representation and problem solving that is the topic of this book will continue to contribute to the improvement of both computer and human intelligence for a considerable time into the future. SUMMARY When I was writing this book, I was clear about the problem-solving interpretation of Horn clauses, and I knew that Horn clauses needed to be extended. However, I did not know what extensions would be most important.