Vagueness and Natural Language Semantics Heather Burnett, Peter Sutton
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A Simple Logic for Comparisons and Vagueness
THEODORE J. EVERETT A SIMPLE LOGIC FOR COMPARISONS AND VAGUENESS ABSTRACT. I provide an intuitive, semantic account of a new logic for comparisons (CL), in which atomic statements are assigned both a classical truth-value and a “how much” value or extension in the range [0, 1]. The truth-value of each comparison is determined by the extensions of its component sentences; the truth-value of each atomic depends on whether its extension matches a separate standard for its predicate; everything else is computed classically. CL is less radical than Casari’s comparative logics, in that it does not allow for the formation of comparative statements out of truth-functional molecules. I argue that CL provides a better analysis of comparisons and predicate vagueness than classical logic, fuzzy logic or supervaluation theory. CL provides a model for descriptions of the world in terms of comparisons only. The sorites paradox can be solved by the elimination of atomic sentences. In his recent book on vagueness, Timothy Williamson (1994) attacks accounts of vagueness arising from either fuzzy logic or supervaluation theory, both of which have well-known problems, stemming in part from their denial of classical bivalence. He then gives his own, epistemic ac- count of vagueness, according to which the vagueness of a predicate is fundamentally a matter of our not knowing whether or not it applies in every case. My main purpose in this paper is not to criticize William- son’s positive view, but to provide an alternative, non-epistemic account of predicate vagueness, based on a very simple logic for comparisons (CL), which preserves bivalence. -
Does Informal Logic Have Anything to Learn from Fuzzy Logic?
University of Windsor Scholarship at UWindsor OSSA Conference Archive OSSA 3 May 15th, 9:00 AM - May 17th, 5:00 PM Does informal logic have anything to learn from fuzzy logic? John Woods University of British Columbia Follow this and additional works at: https://scholar.uwindsor.ca/ossaarchive Part of the Philosophy Commons Woods, John, "Does informal logic have anything to learn from fuzzy logic?" (1999). OSSA Conference Archive. 62. https://scholar.uwindsor.ca/ossaarchive/OSSA3/papersandcommentaries/62 This Paper is brought to you for free and open access by the Conferences and Conference Proceedings at Scholarship at UWindsor. It has been accepted for inclusion in OSSA Conference Archive by an authorized conference organizer of Scholarship at UWindsor. For more information, please contact [email protected]. Title: Does informal logic have anything to learn from fuzzy logic? Author: John Woods Response to this paper by: Rolf George (c)2000 John Woods 1. Motivation Since antiquity, philosophers have been challenged by the apparent vagueness of everyday thought and experience. How, these philosophers have wanted to know, are the things we say and think compatible with logical laws such as Excluded Middle?1 A kindred attraction has been the question of how truth presents itself — relatively and in degrees? in approximations? in resemblances, or in bits and pieces? F.H. Bradley is a celebrated (or as the case may be, excoriated) champion of the degrees view of truth. There are, one may say, two main views of error, the absolute and relative. According to the former view there are perfect truths, and on the other side there are sheer errors.. -
Intransitivity and Vagueness
Intransitivity and Vagueness Joseph Y. Halpern∗ Cornell University Computer Science Department Ithaca, NY 14853 [email protected] http://www.cs.cornell.edu/home/halpern Abstract preferences. Moreover, it is this intuition that forms the ba- sis of the partitional model for knowledge used in game the- There are many examples in the literature that suggest that indistinguishability is intransitive, despite the fact that the in- ory (see, e.g., [Aumann 1976]) and in the distributed sys- distinguishability relation is typically taken to be an equiv- tems community [Fagin, Halpern, Moses, and Vardi 1995]. alence relation (and thus transitive). It is shown that if the On the other hand, besides the obvious experimental obser- uncertainty perception and the question of when an agent vations, there have been arguments going back to at least reports that two things are indistinguishable are both care- Poincare´ [1902] that the physical world is not transitive in fully modeled, the problems disappear, and indistinguishabil- this sense. In this paper, I try to reconcile our intuitions ity can indeed be taken to be an equivalence relation. More- about indistinguishability with the experimental observa- over, this model also suggests a logic of vagueness that seems tions, in a way that seems (at least to me) both intuitively to solve many of the problems related to vagueness discussed appealing and psychologically plausible. I then go on to ap- in the philosophical literature. In particular, it is shown here ply the ideas developed to the problem of vagueness. how the logic can handle the Sorites Paradox. To understand the vagueness problem, consider the well- n+1 1 Introduction known Sorites Paradox: If grains of sand make a heap, then so do n. -
On the Relationship Between Fuzzy Autoepistemic Logic and Fuzzy Modal Logics of Belief
JID:FSS AID:6759 /FLA [m3SC+; v1.204; Prn:31/03/2015; 9:42] P.1(1-26) Available online at www.sciencedirect.com ScienceDirect Fuzzy Sets and Systems ••• (••••) •••–••• www.elsevier.com/locate/fss On the relationship between fuzzy autoepistemic logic and fuzzy modal logics of belief ∗ Marjon Blondeel d, ,1, Tommaso Flaminio a,2, Steven Schockaert b, Lluís Godo c,3, Martine De Cock d,e a Università dell’Insubria, Department of Theorical and Applied Science, Via Mazzini 5, 21100 Varese, Italy b Cardiff University, School of Computer Science and Informatics, 5The Parade, Cardiff, CF24 3AA, UK c Artficial Intelligence Research Institute (IIIA) – CSIC, Campus UAB, 08193 Bellaterra, Spain d Ghent University, Department of Applied Mathematics, Computer Science and Statistics, Krijgslaan 281 (S9), 9000 Gent, Belgium e University of Washington Tacoma – Center for Web and Data Science, 1900 Commerce Street, Tacoma, WA 98402-3100, USA Received 12 February 2014; received in revised form 30 September 2014; accepted 26 February 2015 Abstract Autoepistemic logic is an important formalism for nonmonotonic reasoning originally intended to model an ideal rational agent reflecting upon his own beliefs. Fuzzy autoepistemic logic is a generalization of autoepistemic logic that allows to represent an agent’s rational beliefs on gradable propositions. It has recently been shown that, in the same way as autoepistemic logic generalizes answer set programming, fuzzy autoepistemic logic generalizes fuzzy answer set programming as well. Besides being related to answer set programming, autoepistemic logic is also closely related to several modal logics. To investigate whether a similar relationship holds in a fuzzy logical setting, we firstly generalize the main modal logics for belief to the setting of finitely-valued c Łukasiewicz logic with truth constants Łk, and secondly we relate them with fuzzy autoepistemic logics. -
A Guide to Dynamic Semantics
A Guide to Dynamic Semantics Paul Dekker ILLC/Department of Philosophy University of Amsterdam September 15, 2008 1 Introduction In this article we give an introduction to the idea and workings of dynamic se- mantics. We start with an overview of its historical background and motivation in this introductory section. An in-depth description of a paradigm version of dynamic semantics, Dynamic Predicate Logic, is given in section 2. In section 3 we discuss some applications of the dynamic kind of interpretation to illustrate how it can be taken to neatly account for a vast number of empirical phenom- ena. In section 4 more radical extensions of the basic paradigm are discussed, all of them systematically incorporating previously deemed pragmatic aspects of meaning in the interpretational system. Finally, a discussion of some more general, philosophical and theoretical, issues surrounding dynamic semantics can be found in section 5. 1.1 Theoretical Background What is dynamic semantics. Some people claim it embodies a radical new view of meaning, departing from the main logical paradigm as it has been prominent in most of the previous century. Meaning, or so it is said, is not some object, or some Platonic entity, but it is something that changes information states. A very simple-minded way of putting the idea is that people uses languages, they have cognitive states, and what language does is change these states. “Natu- ral languages are programming languages for minds”, it has been said. Others build on the assumption that natural language and its interpretation is not just concerned with describing an independently given world, but that there are lots of others things relevant in the interpretation of discourse, and lots of other functions of language than a merely descriptive one. -
Chapter 3 Describing Syntax and Semantics
Chapter 3 Describing Syntax and Semantics 3.1 Introduction 110 3.2 The General Problem of Describing Syntax 111 3.3 Formal Methods of Describing Syntax 113 3.4 Attribute Grammars 128 3.5 Describing the Meanings of Programs: Dynamic Semantics 134 Summary • Bibliographic Notes • Review Questions • Problem Set 155 CMPS401 Class Notes (Chap03) Page 1 / 25 Dr. Kuo-pao Yang Chapter 3 Describing Syntax and Semantics 3.1 Introduction 110 Syntax – the form of the expressions, statements, and program units Semantics - the meaning of the expressions, statements, and program units. Ex: the syntax of a Java while statement is while (boolean_expr) statement – The semantics of this statement form is that when the current value of the Boolean expression is true, the embedded statement is executed. – The form of a statement should strongly suggest what the statement is meant to accomplish. 3.2 The General Problem of Describing Syntax 111 A sentence or “statement” is a string of characters over some alphabet. The syntax rules of a language specify which strings of characters from the language’s alphabet are in the language. A language is a set of sentences. A lexeme is the lowest level syntactic unit of a language. It includes identifiers, literals, operators, and special word (e.g. *, sum, begin). A program is strings of lexemes. A token is a category of lexemes (e.g., identifier). An identifier is a token that have lexemes, or instances, such as sum and total. Ex: index = 2 * count + 17; Lexemes Tokens index identifier = equal_sign 2 int_literal * mult_op count identifier + plus_op 17 int_literal ; semicolon CMPS401 Class Notes (Chap03) Page 2 / 25 Dr. -
Semantics and Context-Dependence: Towards a Strawsonian Account∗
Semantics and Context-Dependence: Towards a Strawsonian Account∗ Richard G Heck, Jr Brown University Some time in the mid-1990s, I began to encounter variations on the following argument, which was popular with certain of my colleagues and with students who had fallen under their influence. (i) Semantics is about truth-conditions. (ii) Only utterances have truth-conditions. (iii) Hence, a semantic theory must assign truth-conditions to utterances. (iv) Only a theory that incorporated a complete theory of human rationality could assign truth-conditions to utterances. (v) So there is no such thing as a semantic theory (in anything like the usual sense). The argument need not be formulated in terms of truth-conditions. The first premise could equally be stated as: Semantics is about the expression of propositions. The rest of the argument then adapts smoothly. So (i) is meant to be obvious and uncontroversial. With regard to the second premise, it is important to appreciate the force of the word “only”: (ii) asserts that (perhaps with a very few exceptions, such as statements of pure mathematics) sentences never have truth-conditions; only utterances do. So what underwrites (ii) is not just the existence of context-dependence but, rather, its ubiquity. As has become clear over the last few decades, it is not just the obvious expressions—like “I”, “here”, “this”, and the like—whose meaning seems to vary with the circumstances of utterance. All of the following sentences seem as if they could express different propositions on different occasions, due to context-dependence connected with the italicized expressions: • No-one passed the test. -
Consequence and Degrees of Truth in Many-Valued Logic
Consequence and degrees of truth in many-valued logic Josep Maria Font Published as Chapter 6 (pp. 117–142) of: Peter Hájek on Mathematical Fuzzy Logic (F. Montagna, ed.) vol. 6 of Outstanding Contributions to Logic, Springer-Verlag, 2015 Abstract I argue that the definition of a logic by preservation of all degrees of truth is a better rendering of Bolzano’s idea of consequence as truth- preserving when “truth comes in degrees”, as is often said in many-valued contexts, than the usual scheme that preserves only one truth value. I review some results recently obtained in the investigation of this proposal by applying techniques of abstract algebraic logic in the framework of Łukasiewicz logics and in the broader framework of substructural logics, that is, logics defined by varieties of (commutative and integral) residuated lattices. I also review some scattered, early results, which have appeared since the 1970’s, and make some proposals for further research. Keywords: many-valued logic, mathematical fuzzy logic, truth val- ues, truth degrees, logics preserving degrees of truth, residuated lattices, substructural logics, abstract algebraic logic, Leibniz hierarchy, Frege hierarchy. Contents 6.1 Introduction .............................2 6.2 Some motivation and some history ...............3 6.3 The Łukasiewicz case ........................8 6.4 Widening the scope: fuzzy and substructural logics ....9 6.5 Abstract algebraic logic classification ............. 12 6.6 The Deduction Theorem ..................... 16 6.7 Axiomatizations ........................... 18 6.7.1 In the Gentzen style . 18 6.7.2 In the Hilbert style . 19 6.8 Conclusions .............................. 21 References .................................. 23 1 6.1 Introduction Let me begin by calling your attention to one of the main points made by Petr Hájek in the introductory, vindicating section of his influential book [34] (the italics are his): «Logic studies the notion(s) of consequence. -
Borderline Vs. Unknown: Comparing Three-Valued Representations of Imperfect Information Davide Ciucci, Didier Dubois, Jonathan Lawry
Borderline vs. unknown: comparing three-valued representations of imperfect information Davide Ciucci, Didier Dubois, Jonathan Lawry To cite this version: Davide Ciucci, Didier Dubois, Jonathan Lawry. Borderline vs. unknown: comparing three-valued representations of imperfect information. International Journal of Approximate Reasoning, Elsevier, 2014, vol. 55 (n° 9), pp. 1866-1889. 10.1016/j.ijar.2014.07.004. hal-01154064 HAL Id: hal-01154064 https://hal.archives-ouvertes.fr/hal-01154064 Submitted on 21 May 2015 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. Open Archive TOULOUSE Archive Ouverte ( OATAO ) OATAO is a n open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse .fr/ Eprints ID : 13234 To link to this article : DOI:10.1016/j.ijar.2014.07.004 URL : http://dx.doi.org/10.1016/j.ijar.2014.07.004 To cite this version : Ciucci, Davide and Dubois, Didier and Lawry, Jonathan Borderline vs. unknown : comparing three-valued representations of imperfect information. (2014) International Journal of Approximate Reasoning, vol. 55 (n° 9). -
A Dynamic Semantics of Single-Wh and Multiple-Wh Questions*
Proceedings of SALT 30: 376–395, 2020 A dynamic semantics of single-wh and multiple-wh questions* Jakub Dotlacilˇ Floris Roelofsen Utrecht University University of Amsterdam Abstract We develop a uniform analysis of single-wh and multiple-wh questions couched in dynamic inquisitive semantics. The analysis captures the effects of number marking on which-phrases, and derives both mention-some and mention-all readings as well as an often neglected partial mention- some reading in multiple-wh questions. Keywords: question semantics, multiple-wh questions, mention-some, dynamic inquisitive semantics. 1 Introduction The goal of this paper is to develop an analysis of single-wh and multiple-wh questions in English satisfying the following desiderata: a) Uniformity. The interpretation of single-wh and multiple-wh questions is derived uniformly. The same mechanisms are operative in both types of questions. b) Mention-some vs mention-all. Mention-some and mention-all readings are derived in a principled way, including ‘partial mention-some readings’ in multiple-wh questions (mention-all w.r.t. one of the wh-phrases but mention-some w.r.t. the other wh-phrase). c) Number marking. The effects of number marking on wh-phrases are captured. In particular, singular which-phrases induce a uniqueness requirement in single-wh questions but do not block pair-list readings in multiple-wh questions. To our knowledge, no existing account fully satisfies these desiderata. While we cannot do justice to the rich literature on the topic, let us briefly mention a number of prominent proposals. Groenendijk & Stokhof(1984) provide a uniform account of single-wh and multiple-wh ques- tions, but their proposal does not successfully capture mention-some readings and the effects of number marking. -
Free Choice and Homogeneity
Semantics & Pragmatics Volume 12, Article 23, 2019 https://doi.org/10.3765/sp.12.23 This is an early access version of Goldstein, Simon. 2019. Free choice and homogeneity. Semantics and Prag- matics 12(23). 1–47. https://doi.org/10.3765/sp.12.23. This version will be replaced with the final typeset version in due course. Note that page numbers will change, so cite with caution. ©2019 Simon Goldstein This is an open-access article distributed under the terms of a Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/). early access Free choice and homogeneity* Simon Goldstein Australian Catholic University Abstract This paper develops a semantic solution to the puzzle of Free Choice permission. The paper begins with a battery of impossibility results showing that Free Choice is in tension with a variety of classical principles, including Disjunction Introduction and the Law of Excluded Middle. Most interestingly, Free Choice appears incompatible with a principle concerning the behavior of Free Choice under negation, Double Prohibition, which says that Mary can’t have soup or salad implies Mary can’t have soup and Mary can’t have salad. Alonso-Ovalle 2006 and others have appealed to Double Prohibition to motivate pragmatic accounts of Free Choice. Aher 2012, Aloni 2018, and others have developed semantic accounts of Free Choice that also explain Double Prohibition. This paper offers a new semantic analysis of Free Choice designed to handle the full range of impossibility results involved in Free Choice. The paper develops the hypothesis that Free Choice is a homogeneity effect. -
A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability and Statistics
FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (fourth edition) NL(A1 A2) = ( T1 ({1}T2) T2 ({1}T1) T1T2 ({1}T1) ({1}T2), I1 ({1}I2) I2 ({1}I1) I1 I2 ({1}I1) ({1} I2), F1 ({1}F2) F2 ({1} F1) F1 F2 ({1}F1) ({1}F2) ). NL(A1 A2) = ( {1}T1T1T2, {1}I1I1I2, {1}F1F1F2 ). NL(A1 A2) = ( ({1}T1T1T2) ({1}T2T1T2), ({1} I1 I1 I2) ({1}I2 I1 I2), ({1}F1F1 F2) ({1}F2F1 F2) ). ISBN 978-1-59973-080-6 American Research Press Rehoboth 1998, 2000, 2003, 2005 FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (fourth edition) NL(A1 A2) = ( T1 ({1}T2) T2 ({1}T1) T1T2 ({1}T1) ({1}T2), I1 ({1}I2) I2 ({1}I1) I1 I2 ({1}I1) ({1} I2), F1 ({1}F2) F2 ({1} F1) F1 F2 ({1}F1) ({1}F2) ). NL(A1 A2) = ( {1}T1T1T2, {1}I1I1I2, {1}F1F1F2 ). NL(A1 A2) = ( ({1}T1T1T2) ({1}T2T1T2), ({1} I1 I1 I2) ({1}I2 I1 I2), ({1}F1F1 F2) ({1}F2F1 F2) ). ISBN 978-1-59973-080-6 American Research Press Rehoboth 1998, 2000, 2003, 2005 1 Contents: Preface by C. Le: 3 0. Introduction: 9 1. Neutrosophy - a new branch of philosophy: 15 2. Neutrosophic Logic - a unifying field in logics: 90 3. Neutrosophic Set - a unifying field in sets: 125 4. Neutrosophic Probability - a generalization of classical and imprecise probabilities - and Neutrosophic Statistics: 129 5. Addenda: Definitions derived from Neutrosophics: 133 2 Preface to Neutrosophy and Neutrosophic Logic by C.