1 Tarski's Influence on Computer Science
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Poznań Studies in the Philosophy of the Sciences and the Humanities), 21Pp
Forthcoming in: Uncovering Facts and Values, ed. A. Kuzniar and J. Odrowąż-Sypniewska (Poznań Studies in the Philosophy of the Sciences and the Humanities), 21pp. Abstract Alfred Tarski seems to endorse a partial conception of truth, the T-schema, which he believes might be clarified by the application of empirical methods, specifically citing the experimental results of Arne Næss (1938a). The aim of this paper is to argue that Næss’ empirical work confirmed Tarski’s semantic conception of truth, among others. In the first part, I lay out the case for believing that Tarski’s T-schema, while not the formal and generalizable Convention-T, provides a partial account of truth that may be buttressed by an examination of the ordinary person’s views of truth. Then, I address a concern raised by Tarski’s contemporaries who saw Næss’ results as refuting Tarski’s semantic conception. Following that, I summarize Næss’ results. Finally, I will contend with a few objections that suggest a strict interpretation of Næss’ results might recommend an overturning of Tarski’s theory. Keywords: truth, Alfred Tarski, Arne Næss, Vienna Circle, experimental philosophy Joseph Ulatowski ORDINARY TRUTH IN TARSKI AND NÆSS 1. Introduction Many of Alfred Tarski's better known papers on truth (e.g. 1944; 1983b), logical consequence (1983c), semantic concepts in general (1983a), or definability (1948) identify two conditions that successful definitions of “truth,” “logical consequence,” or “definition” require: formal correctness and material (or intuitive) adequacy.1 The first condition Tarski calls “formal correctness” because a definition of truth (for a given formal language) is formally correct when it is constructed in a manner that allows us to avoid both circular definition and semantic paradoxes. -
Alfred Tarski His:Bio:Tar: Sec Alfred Tarski Was Born on January 14, 1901 in Warsaw, Poland (Then Part of the Russian Empire)
bio.1 Alfred Tarski his:bio:tar: sec Alfred Tarski was born on January 14, 1901 in Warsaw, Poland (then part of the Russian Empire). Described as \Napoleonic," Tarski was boisterous, talkative, and intense. His energy was often reflected in his lectures|he once set fire to a wastebasket while disposing of a cigarette during a lecture, and was forbidden from lecturing in that building again. Tarski had a thirst for knowledge from a young age. Although later in life he would tell students that he stud- ied logic because it was the only class in which he got a B, his high school records show that he got A's across the board| even in logic. He studied at the Univer- sity of Warsaw from 1918 to 1924. Tarski Figure 1: Alfred Tarski first intended to study biology, but be- came interested in mathematics, philosophy, and logic, as the university was the center of the Warsaw School of Logic and Philosophy. Tarski earned his doctorate in 1924 under the supervision of Stanislaw Le´sniewski. Before emigrating to the United States in 1939, Tarski completed some of his most important work while working as a secondary school teacher in Warsaw. His work on logical consequence and logical truth were written during this time. In 1939, Tarski was visiting the United States for a lecture tour. During his visit, Germany invaded Poland, and because of his Jewish heritage, Tarski could not return. His wife and children remained in Poland until the end of the war, but were then able to emigrate to the United States as well. -
Probabilistic Databases
Series ISSN: 2153-5418 SUCIU • OLTEANU •RÉ •KOCH M SYNTHESIS LECTURES ON DATA MANAGEMENT &C Morgan & Claypool Publishers Series Editor: M. Tamer Özsu, University of Waterloo Probabilistic Databases Probabilistic Databases Dan Suciu, University of Washington, Dan Olteanu, University of Oxford Christopher Ré,University of Wisconsin-Madison and Christoph Koch, EPFL Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk DATABASES PROBABILISTIC assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic Dan Suciu inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and Dan Olteanu can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-kquery processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. -
Alfred Tarski and a Watershed Meeting in Logic: Cornell, 1957 Solomon Feferman1
Alfred Tarski and a watershed meeting in logic: Cornell, 1957 Solomon Feferman1 For Jan Wolenski, on the occasion of his 60th birthday2 In the summer of 1957 at Cornell University the first of a cavalcade of large-scale meetings partially or completely devoted to logic took place--the five-week long Summer Institute for Symbolic Logic. That meeting turned out to be a watershed event in the development of logic: it was unique in bringing together for such an extended period researchers at every level in all parts of the subject, and the synergetic connections established there would thenceforth change the face of mathematical logic both qualitatively and quantitatively. Prior to the Cornell meeting there had been nothing remotely like it for logicians. Previously, with the growing importance in the twentieth century of their subject both in mathematics and philosophy, it had been natural for many of the broadly representative meetings of mathematicians and of philosophers to include lectures by logicians or even have special sections devoted to logic. Only with the establishment of the Association for Symbolic Logic in 1936 did logicians begin to meet regularly by themselves, but until the 1950s these occasions were usually relatively short in duration, never more than a day or two. Alfred Tarski was one of the principal organizers of the Cornell institute and of some of the major meetings to follow on its heels. Before the outbreak of World War II, outside of Poland Tarski had primarily been involved in several Unity of Science Congresses, including the first, in Paris in 1935, and the fifth, at Harvard in September, 1939. -
Download a Copy of the 264-Page Publication
2020 Department of Neurological Surgery Annual Report Reporting period July 1, 2019 through June 30, 2020 Table of Contents: Introduction .................................................................3 Faculty and Residents ...................................................5 Faculty ...................................................................6 Residents ...............................................................8 Stuart Rowe Lecturers .........................................10 Peter J. Jannetta Lecturers ................................... 11 Department Overview ............................................... 13 History ............................................................... 14 Goals/Mission .................................................... 16 Organization ...................................................... 16 Accomplishments of Note ................................ 29 Education Programs .................................................. 35 Faculty Biographies ................................................... 47 Resident Biographies ................................................171 Research ....................................................................213 Overview ...........................................................214 Investigator Research Summaries ................... 228 Research Grant Summary ................................ 242 Alumni: Past Residents ........................................... 249 Donations ................................................................ 259 Statistics -
Best Answers Over Incomplete Data : Complexity and First-Order Rewritings
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) Best Answers over Incomplete Data : Complexity and First-Order Rewritings Amelie´ Gheerbrant and Cristina Sirangelo Universite´ de Paris, IRIF, CNRS, F-75013 Paris, France famelie, [email protected] Abstract as if nulls were usual data values, thus merely using the stan- dard database query engine to compute certain answers. Answering queries over incomplete data is ubiqui- In general though it is a common occurrence that few if tous in data management and in many AI applica- any certain answers can be found. If there are no certain an- tions that use query rewriting to take advantage of swers, it is still useful to provide a user with some answers, relational database technology. In these scenarios with suitable guarantees. To address this need, a framework one lacks full information on the data but queries to measure how close an answer is to certainty has recently still need to be answered with certainty. The cer- been proposed [Libkin, 2018b], setting the foundations to tainty aspect often makes query answering unfeasi- both a quantitative and a qualitative approach. We focus on ble except for restricted classes, such as unions of the qualitative notion of best answers. Those are a refinement conjunctive queries. In addition often there are no, of certain answers based on comparing query answers; one or very few, certain answers, thus expensive com- that is supported by a larger set of complete interpretations is putation is in vain. Therefore we study a relax- better. Best answers are those answers for which there is no ation of certain answers called best answers. -
Alfred Tarski, Friend and Daemon Benjamin Wells
Alfred Tarski, Friend and Daemon Benjamin Wells Engaging Tarski When I had passed doctoral exams in 1963 and Alfred Tarski’s name stayed with me after I read sounded Bob Vaught and John Addison on their about the Banach-Tarski paradox in [3] during availability for supervising my research, both said high school. I then discovered logic (and Tarski’s that the department recognized me as Tarski’s definition of truth) in the last year of college but student. News to me, despite the second opinion. still considered myself to be a topologist, not from So I called him to learn more about my fate. He love but from intimate contact in four courses as acknowledged the claim, “Good,” and invited me an undergraduate. So before arriving at UC Berke- for the first serious nighttime talk. ley in fall 1962 for graduate work, I thought only Source of the Title vaguely about taking a course with him. But my inclinations were shifting: dutifully registering Throughout cooperation and separation, Alfred for fall classes in topological groups and algebraic and I were friends, cordial and personable, but geometry, I added metamathematics and Tarski’s not really personal. He also established another general algebraic systems. The next fall, Tarski of- role, that of daemon in the sense of [4]: a leonine fered set theory. These two courses were my only externalized conscience, at least. The scope of Tarski lectures, but there were numerous seminars. this conscience was foremost mathematical, with a hope for political, a goal of cultural, a reserva- Visitors to Berkeley constantly identified Tarski tion on philosophical and moral, and a hint of with whatever topic occupied him so fruitfully and spiritual. -
Toward a Mathematical Semantics for Computer Languages
(! 1 J TOWARD A MATHEMATICAL SEMANTICS FOR COMPUTER LANGUAGES by Dana Scott and - Christopher Strachey Oxford University Computing Laboratory Programming Research Group-Library 8-11 Keble Road Oxford OX, 3QD Oxford (0865) 54141 Oxford University Computing Laboratory Programming Research Group tI. cr• "';' """, ":.\ ' OXFORD UNIVERSITY COMPUTING LABORATORY PROGRAMMING RESEARCH GROUP ~ 4S BANBURY ROAD \LJ OXFORD ~ .. 4 OCT 1971 ~In (UY'Y L TOWARD A ~ATHEMATICAL SEMANTICS FOR COMPUTER LANGUAGES by Dana Scott Princeton University and Christopher Strachey Oxford University Technical Monograph PRG-6 August 1971 Oxford University Computing Laboratory. Programming Research Group, 45 Banbury Road, Oxford. ~ 1971 Dana Scott and Christopher Strachey Department of Philosophy, Oxford University Computing Laboratory. 1879 lIall, Programming Research Group. Princeton University, 45 Banbury Road. Princeton. New Jersey 08540. Oxford OX2 6PE. This pape r is also to appear in Fl'(.'ceedinBs 0;- the .';y-,;;;o:illT:: on ComputeT's and AutoJ7'ata. lo-licroloo'ave Research Institute Symposia Series Volume 21. Polytechnic Institute of Brooklyn. and appears as a Technical Monograph by special aJ"rangement ...·ith the publishers. RefeJ"~nces in the Ii terature should be:- made to the _"!'OL·,-,',~;r:gs, as the texts are identical and the Symposia Sl?ries is gcaerally available in libraries. ABSTRACT Compilers for high-level languages aTe generally constructed to give the complete translation of the programs into machme language. As machines merely juggle bit patterns, the concepts of the original language may be lost or at least obscured during this passage. The purpose of a mathematical semantics is to give a correct and meaningful correspondence between programs and mathematical entities in a way that is entirely independent of an implementation. -
Why Mathematical Proof?
Why Mathematical Proof? Dana S. Scott, FBA, FNAS University Professor Emeritus Carnegie Mellon University Visiting Scholar University of California, Berkeley NOTICE! The author has plagiarized text and graphics from innumerable publications and sites, and he has failed to record attributions! But, as this lecture is intended as an entertainment and is not intended for publication, he regards such copying, therefore, as “fair use”. Keep this quiet, and do please forgive him. A Timeline for Geometry Some Greek Geometers Thales of Miletus (ca. 624 – 548 BC). Pythagoras of Samos (ca. 580 – 500 BC). Plato (428 – 347 BC). Archytas (428 – 347 BC). Theaetetus (ca. 417 – 369 BC). Eudoxus of Cnidus (ca. 408 – 347 BC). Aristotle (384 – 322 BC). Euclid (ca. 325 – ca. 265 BC). Archimedes of Syracuse (ca. 287 – ca. 212 BC). Apollonius of Perga (ca. 262 – ca. 190 BC). Claudius Ptolemaeus (Ptolemy)(ca. 90 AD – ca. 168 AD). Diophantus of Alexandria (ca. 200 – 298 AD). Pappus of Alexandria (ca. 290 – ca. 350 AD). Proclus Lycaeus (412 – 485 AD). There is no Royal Road to Geometry Euclid of Alexandria ca. 325 — ca. 265 BC Euclid taught at Alexandria in the time of Ptolemy I Soter, who reigned over Egypt from 323 to 285 BC. He authored the most successful textbook ever produced — and put his sources into obscurity! Moreover, he made us struggle with proofs ever since. Why Has Euclidean Geometry Been So Successful? • Our naive feeling for space is Euclidean. • Its methods have been very useful. • Euclid also shows us a mysterious connection between (visual) intuition and proof. The Pythagorean Theorem Euclid's Elements: Proposition 47 of Book 1 The Pythagorean Theorem Generalized If it holds for one Three triple, Similar it holds Figures for all. -
The Development of Mathematical Logic from Russell to Tarski: 1900–1935
The Development of Mathematical Logic from Russell to Tarski: 1900–1935 Paolo Mancosu Richard Zach Calixto Badesa The Development of Mathematical Logic from Russell to Tarski: 1900–1935 Paolo Mancosu (University of California, Berkeley) Richard Zach (University of Calgary) Calixto Badesa (Universitat de Barcelona) Final Draft—May 2004 To appear in: Leila Haaparanta, ed., The Development of Modern Logic. New York and Oxford: Oxford University Press, 2004 Contents Contents i Introduction 1 1 Itinerary I: Metatheoretical Properties of Axiomatic Systems 3 1.1 Introduction . 3 1.2 Peano’s school on the logical structure of theories . 4 1.3 Hilbert on axiomatization . 8 1.4 Completeness and categoricity in the work of Veblen and Huntington . 10 1.5 Truth in a structure . 12 2 Itinerary II: Bertrand Russell’s Mathematical Logic 15 2.1 From the Paris congress to the Principles of Mathematics 1900–1903 . 15 2.2 Russell and Poincar´e on predicativity . 19 2.3 On Denoting . 21 2.4 Russell’s ramified type theory . 22 2.5 The logic of Principia ......................... 25 2.6 Further developments . 26 3 Itinerary III: Zermelo’s Axiomatization of Set Theory and Re- lated Foundational Issues 29 3.1 The debate on the axiom of choice . 29 3.2 Zermelo’s axiomatization of set theory . 32 3.3 The discussion on the notion of “definit” . 35 3.4 Metatheoretical studies of Zermelo’s axiomatization . 38 4 Itinerary IV: The Theory of Relatives and Lowenheim’s¨ Theorem 41 4.1 Theory of relatives and model theory . 41 4.2 The logic of relatives . -
The Origins of Structural Operational Semantics
The Origins of Structural Operational Semantics Gordon D. Plotkin Laboratory for Foundations of Computer Science, School of Informatics, University of Edinburgh, King’s Buildings, Edinburgh EH9 3JZ, Scotland I am delighted to see my Aarhus notes [59] on SOS, Structural Operational Semantics, published as part of this special issue. The notes already contain some historical remarks, but the reader may be interested to know more of the personal intellectual context in which they arose. I must straightaway admit that at this distance in time I do not claim total accuracy or completeness: what I write should rather be considered as a reconstruction, based on (possibly faulty) memory, papers, old notes and consultations with colleagues. As a postgraduate I learnt the untyped λ-calculus from Rod Burstall. I was further deeply impressed by the work of Peter Landin on the semantics of pro- gramming languages [34–37] which includes his abstract SECD machine. One should also single out John McCarthy’s contributions [45–48], which include his 1962 introduction of abstract syntax, an essential tool, then and since, for all approaches to the semantics of programming languages. The IBM Vienna school [42, 41] were interested in specifying real programming languages, and, in particular, worked on an abstract interpreting machine for PL/I using VDL, their Vienna Definition Language; they were influenced by the ideas of McCarthy, Landin and Elgot [18]. I remember attending a seminar at Edinburgh where the intricacies of their PL/I abstract machine were explained. The states of these machines are tuples of various kinds of complex trees and there is also a stack of environments; the transition rules involve much tree traversal to access syntactical control points, handle jumps, and to manage concurrency. -
Lecture 1: Tarski on Truth Philosophy of Logic and Language — HT 2016-17
Lecture 1: Tarski on Truth Philosophy of Logic and Language — HT 2016-17 Jonny McIntosh [email protected] Alfred Tarski (1901-1983) was a Polish (and later, American) mathematician, logician, and philosopher.1 In the 1930s, he published two classic papers: ‘The Concept of Truth in Formalized Languages’ (1933) and ‘On the Concept of Logical Consequence’ (1936). He gives a definition of truth for formal languages of logic and mathematics in the first paper, and the essentials of the model-theoretic definition of logical consequence in the second. Over the course of the next few lectures, we’ll look at each of these in turn. 1 Background The notion of truth seems to lie at the centre of a range of other notions that are central to theorising about the formal languages of logic and mathematics: e.g. validity, con- sistency, and completeness. But the notion of truth seems to give rise to contradiction: Let sentence (1) = ‘sentence (1) is not true’. Then: 1. ‘sentence (1) is not true’ is true IFF sentence (1) is not true 2. sentence (1) = ‘sentence (1) is not true’ 3. So, sentence (1) is true IFF sentence (1) is not true 4. So, sentence (1) is true and sentence (1) is not true Tarski is worried that, unless the paradox can be resolved, metatheoretical results in- voking the notion of truth or other notions that depend on it will be remain suspect. But how can it be resolved? The second premise is undeniable, and the first premise is an instance of a schema that seems central to the concept of truth, namely the following: ‘S’ is true IFF S, 1The eventful story of Tarski’s life is told by Anita and Solomon Feferman in their wonderful biography, Alfred Tarski: Life and Logic (CUP, 2004).