First-Order Logic in a Nutshell
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Lecture Slides
Program Verification: Lecture 3 Jos´eMeseguer Computer Science Department University of Illinois at Urbana-Champaign 1 Algebras An (unsorted, many-sorted, or order-sorted) signature Σ is just syntax: provides the symbols for a language; but what is that language talking about? what is its semantics? It is obviously talking about algebras, which are the mathematical models in which we interpret the syntax of Σ, giving it concrete meaning. Unsorted algebras are the simplest example: children become familiar with them from the early awakenings of reason. They consist of a set of data elements, and various chosen constants among those elements, and operations on such data. 2 Algebras (II) For example, for Σ the unsorted signature of the module NAT-MIXFIX we can define many different algebras, such as the following: 1. IN, the algebra of natural numbers in whatever notation we wish (Peano, binary, base 10, etc.) with 0 interpreted as the zero element, s interpreted as successor, and + and * interpreted as natural number addition and multiplication. 2. INk, the algebra of residue classes modulo k, for k a nonzero natural number. This is a finite algebra whose set of elements can be represented as the set {0,...,k − 1}. We interpret 0 as 0, and for the other 3 operations we perform them in IN and then take the residue modulo k. For example, in IN7 we have 6+6=5. 3. Z, the algebra of the integers, with 0 interpreted as the zero element, s interpreted as successor, and + and * interpreted as integer addition and multiplication. 4. -
Set-Theoretic Geology, the Ultimate Inner Model, and New Axioms
Set-theoretic Geology, the Ultimate Inner Model, and New Axioms Justin William Henry Cavitt (860) 949-5686 [email protected] Advisor: W. Hugh Woodin Harvard University March 20, 2017 Submitted in partial fulfillment of the requirements for the degree of Bachelor of Arts in Mathematics and Philosophy Contents 1 Introduction 2 1.1 Author’s Note . .4 1.2 Acknowledgements . .4 2 The Independence Problem 5 2.1 Gödelian Independence and Consistency Strength . .5 2.2 Forcing and Natural Independence . .7 2.2.1 Basics of Forcing . .8 2.2.2 Forcing Facts . 11 2.2.3 The Space of All Forcing Extensions: The Generic Multiverse 15 2.3 Recap . 16 3 Approaches to New Axioms 17 3.1 Large Cardinals . 17 3.2 Inner Model Theory . 25 3.2.1 Basic Facts . 26 3.2.2 The Constructible Universe . 30 3.2.3 Other Inner Models . 35 3.2.4 Relative Constructibility . 38 3.3 Recap . 39 4 Ultimate L 40 4.1 The Axiom V = Ultimate L ..................... 41 4.2 Central Features of Ultimate L .................... 42 4.3 Further Philosophical Considerations . 47 4.4 Recap . 51 1 5 Set-theoretic Geology 52 5.1 Preliminaries . 52 5.2 The Downward Directed Grounds Hypothesis . 54 5.2.1 Bukovský’s Theorem . 54 5.2.2 The Main Argument . 61 5.3 Main Results . 65 5.4 Recap . 74 6 Conclusion 74 7 Appendix 75 7.1 Notation . 75 7.2 The ZFC Axioms . 76 7.3 The Ordinals . 77 7.4 The Universe of Sets . 77 7.5 Transitive Models and Absoluteness . -
Pst-Plot — Plotting Functions in “Pure” L ATEX
pst-plot — plotting functions in “pure” LATEX Commonly, one wants to simply plot a function as a part of a LATEX document. Using some postscript tricks, you can make a graph of an arbitrary function in one variable including implicitly defined functions. The commands described on this worksheet require that the following lines appear in your document header (before \begin{document}). \usepackage{pst-plot} \usepackage{pstricks} The full pstricks manual (including pst-plot documentation) is available at:1 http://www.risc.uni-linz.ac.at/institute/systems/documentation/TeX/tex/ A good page for showing the power of what you can do with pstricks is: http://www.pstricks.de/.2 Reverse Polish Notation (postfix notation) Reverse polish notation (RPN) is a modification of polish notation which was a creation of the logician Jan Lukasiewicz (advisor of Alfred Tarski). The advantage of these notations is that they do not require parentheses to control the order of operations in an expression. The advantage of this in a computer is that parsing a RPN expression is trivial, whereas parsing an expression in standard notation can take quite a bit of computation. At the most basic level, an expression such as 1 + 2 becomes 12+. For more complicated expressions, the concept of a stack must be introduced. The stack is just the list of all numbers which have not been used yet. When an operation takes place, the result of that operation is left on the stack. Thus, we could write the sum of all integers from 1 to 10 as either, 12+3+4+5+6+7+8+9+10+ or 12345678910+++++++++ In both cases the result 55 is left on the stack. -
The Consistency of Arithmetic
The Consistency of Arithmetic Timothy Y. Chow July 11, 2018 In 2010, Vladimir Voevodsky, a Fields Medalist and a professor at the Insti- tute for Advanced Study, gave a lecture entitled, “What If Current Foundations of Mathematics Are Inconsistent?” Voevodsky invited the audience to consider seriously the possibility that first-order Peano arithmetic (or PA for short) was inconsistent. He briefly discussed two of the standard proofs of the consistency of PA (about which we will say more later), and explained why he did not find either of them convincing. He then said that he was seriously suspicious that an inconsistency in PA might someday be found. About one year later, Voevodsky might have felt vindicated when Edward Nelson, a professor of mathematics at Princeton University, announced that he had a proof not only that PA was inconsistent, but that a small fragment of primitive recursive arithmetic (PRA)—a system that is widely regarded as implementing a very modest and “safe” subset of mathematical reasoning—was inconsistent [11]. However, a fatal error in his proof was soon detected by Daniel Tausk and (independently) Terence Tao. Nelson withdrew his claim, remarking that the consistency of PA remained “an open problem.” For mathematicians without much training in formal logic, these claims by Voevodsky and Nelson may seem bewildering. While the consistency of some axioms of infinite set theory might be debatable, is the consistency of PA really “an open problem,” as Nelson claimed? Are the existing proofs of the con- sistency of PA suspect, as Voevodsky claimed? If so, does this mean that we cannot be sure that even basic mathematical reasoning is consistent? This article is an expanded version of an answer that I posted on the Math- Overflow website in response to the question, “Is PA consistent? do we know arXiv:1807.05641v1 [math.LO] 16 Jul 2018 it?” Since the question of the consistency of PA seems to come up repeat- edly, and continues to generate confusion, a more extended discussion seems worthwhile. -
Some First-Order Probability Logics
Theoretical Computer Science 247 (2000) 191–212 www.elsevier.com/locate/tcs View metadata, citation and similar papers at core.ac.uk brought to you by CORE Some ÿrst-order probability logics provided by Elsevier - Publisher Connector Zoran Ognjanovic a;∗, Miodrag RaÄskovic b a MatematickiÄ institut, Kneza Mihaila 35, 11000 Beograd, Yugoslavia b Prirodno-matematickiÄ fakultet, R. Domanovicaà 12, 34000 Kragujevac, Yugoslavia Received June 1998 Communicated by M. Nivat Abstract We present some ÿrst-order probability logics. The logics allow making statements such as P¿s , with the intended meaning “the probability of truthfulness of is greater than or equal to s”. We describe the corresponding probability models. We give a sound and complete inÿnitary axiomatic system for the most general of our logics, while for some restrictions of this logic we provide ÿnitary axiomatic systems. We study the decidability of our logics. We discuss some of the related papers. c 2000 Elsevier Science B.V. All rights reserved. Keywords: First order logic; Probability; Possible worlds; Completeness 1. Introduction In recent years there is a growing interest in uncertainty reasoning. A part of inves- tigation concerns its formal framework – probability logics [1, 2, 5, 6, 9, 13, 15, 17–20]. Probability languages are obtained by adding probability operators of the form (in our notation) P¿s to classical languages. The probability logics allow making formulas such as P¿s , with the intended meaning “the probability of truthfulness of is greater than or equal to s”. Probability models similar to Kripke models are used to give semantics to the probability formulas so that interpreted formulas are either true or false. -
Open-Logic-Enderton Rev: C8c9782 (2021-09-28) by OLP/ CC–BY
An Open Mathematical Introduction to Logic Example OLP Text `ala Enderton Open Logic Project open-logic-enderton rev: c8c9782 (2021-09-28) by OLP/ CC{BY Contents I Propositional Logic7 1 Syntax and Semantics9 1.0 Introduction............................ 9 1.1 Propositional Wffs......................... 10 1.2 Preliminaries............................ 11 1.3 Valuations and Satisfaction.................... 12 1.4 Semantic Notions ......................... 14 Problems ................................. 14 2 Derivation Systems 17 2.0 Introduction............................ 17 2.1 The Sequent Calculus....................... 18 2.2 Natural Deduction......................... 19 2.3 Tableaux.............................. 20 2.4 Axiomatic Derivations ...................... 22 3 Axiomatic Derivations 25 3.0 Rules and Derivations....................... 25 3.1 Axiom and Rules for the Propositional Connectives . 26 3.2 Examples of Derivations ..................... 27 3.3 Proof-Theoretic Notions ..................... 29 3.4 The Deduction Theorem ..................... 30 1 Contents 3.5 Derivability and Consistency................... 32 3.6 Derivability and the Propositional Connectives......... 32 3.7 Soundness ............................. 33 Problems ................................. 34 4 The Completeness Theorem 35 4.0 Introduction............................ 35 4.1 Outline of the Proof........................ 36 4.2 Complete Consistent Sets of Sentences ............. 37 4.3 Lindenbaum's Lemma....................... 38 4.4 Construction of a Model -
Arxiv:1311.3168V22 [Math.LO] 24 May 2021 the Foundations Of
The Foundations of Mathematics in the Physical Reality Doeko H. Homan May 24, 2021 It is well-known that the concept set can be used as the foundations for mathematics, and the Peano axioms for the set of all natural numbers should be ‘considered as the fountainhead of all mathematical knowledge’ (Halmos [1974] page 47). However, natural numbers should be defined, thus ‘what is a natural number’, not ‘what is the set of all natural numbers’. Set theory should be as intuitive as possible. Thus there is no ‘empty set’, and a single shoe is not a singleton set but an individual, a pair of shoes is a set. In this article we present an axiomatic definition of sets with individuals. Natural numbers and ordinals are defined. Limit ordinals are ‘first numbers’, that is a first number of the Peano axioms. For every natural number m we define ‘ωm-numbers with a first number’. Every ordinal is an ordinal with a first ω0-number. Ordinals with a first number satisfy the Peano axioms. First ωω-numbers are defined. 0 is a first ωω-number. Then we prove a first ωω-number notequal 0 belonging to ordinal γ is an impassable barrier for counting down γ to 0 in a finite number of steps. 1 What is a set? At an early age you develop the idea of ‘what is a set’. You belong to a arXiv:1311.3168v22 [math.LO] 24 May 2021 family or a clan, you belong to the inhabitants of a village or a particular region. Experience shows there are objects that constitute a set. -
The Continuum Hypothesis, Part I, Volume 48, Number 6
fea-woodin.qxp 6/6/01 4:39 PM Page 567 The Continuum Hypothesis, Part I W. Hugh Woodin Introduction of these axioms and related issues, see [Kanamori, Arguably the most famous formally unsolvable 1994]. problem of mathematics is Hilbert’s first prob- The independence of a proposition φ from the axioms of set theory is the arithmetic statement: lem: ZFC does not prove φ, and ZFC does not prove φ. ¬ Cantor’s Continuum Hypothesis: Suppose that Of course, if ZFC is inconsistent, then ZFC proves X R is an uncountable set. Then there exists a bi- anything, so independence can be established only ⊆ jection π : X R. by assuming at the very least that ZFC is consis- → tent. Sometimes, as we shall see, even stronger as- This problem belongs to an ever-increasing list sumptions are necessary. of problems known to be unsolvable from the The first result concerning the Continuum (usual) axioms of set theory. Hypothesis, CH, was obtained by Gödel. However, some of these problems have now Theorem (Gödel). Assume ZFC is consistent. Then been solved. But what does this actually mean? so is ZFC + CH. Could the Continuum Hypothesis be similarly solved? These questions are the subject of this ar- The modern era of set theory began with Cohen’s ticle, and the discussion will involve ingredients discovery of the method of forcing and his appli- from many of the current areas of set theoretical cation of this new method to show: investigation. Most notably, both Large Cardinal Theorem (Cohen). Assume ZFC is consistent. Then Axioms and Determinacy Axioms play central roles. -
Symmetric Approximations of Pseudo-Boolean Functions with Applications to Influence Indexes
SYMMETRIC APPROXIMATIONS OF PSEUDO-BOOLEAN FUNCTIONS WITH APPLICATIONS TO INFLUENCE INDEXES JEAN-LUC MARICHAL AND PIERRE MATHONET Abstract. We introduce an index for measuring the influence of the kth smallest variable on a pseudo-Boolean function. This index is defined from a weighted least squares approximation of the function by linear combinations of order statistic functions. We give explicit expressions for both the index and the approximation and discuss some properties of the index. Finally, we show that this index subsumes the concept of system signature in engineering reliability and that of cardinality index in decision making. 1. Introduction Boolean and pseudo-Boolean functions play a central role in various areas of applied mathematics such as cooperative game theory, engineering reliability, and decision making (where fuzzy measures and fuzzy integrals are often used). In these areas indexes have been introduced to measure the importance of a variable or its influence on the function under consideration (see, e.g., [3, 7]). For instance, the concept of importance of a player in a cooperative game has been studied in various papers on values and power indexes starting from the pioneering works by Shapley [13] and Banzhaf [1]. These power indexes were rediscovered later in system reliability theory as Barlow-Proschan and Birnbaum measures of importance (see, e.g., [10]). In general there are many possible influence/importance indexes and they are rather simple and natural. For instance, a cooperative game on a finite set n = 1,...,n of players is a set function v∶ 2[n] → R with v ∅ = 0, which associates[ ] with{ any}coalition of players S ⊆ n its worth v S . -
Building the Signature of Set Theory Using the Mathsem Program
Building the Signature of Set Theory Using the MathSem Program Luxemburg Andrey UMCA Technologies, Moscow, Russia [email protected] Abstract. Knowledge representation is a popular research field in IT. As mathematical knowledge is most formalized, its representation is important and interesting. Mathematical knowledge consists of various mathematical theories. In this paper we consider a deductive system that derives mathematical notions, axioms and theorems. All these notions, axioms and theorems can be considered a small mathematical theory. This theory will be represented as a semantic net. We start with the signature <Set; > where Set is the support set, is the membership predicate. Using the MathSem program we build the signature <Set; where is set intersection, is set union, -is the Cartesian product of sets, and is the subset relation. Keywords: Semantic network · semantic net· mathematical logic · set theory · axiomatic systems · formal systems · semantic web · prover · ontology · knowledge representation · knowledge engineering · automated reasoning 1 Introduction The term "knowledge representation" usually means representations of knowledge aimed to enable automatic processing of the knowledge base on modern computers, in particular, representations that consist of explicit objects and assertions or statements about them. We are particularly interested in the following formalisms for knowledge representation: 1. First order predicate logic; 2. Deductive (production) systems. In such a system there is a set of initial objects, rules of inference to build new objects from initial ones or ones that are already build, and the whole of initial and constructed objects. 3. Semantic net. In the most general case a semantic net is an entity-relationship model, i.e., a graph, whose vertices correspond to objects (notions) of the theory and edges correspond to relations between them. -
Embedding and Learning with Signatures
Embedding and learning with signatures Adeline Fermaniana aSorbonne Universit´e,CNRS, Laboratoire de Probabilit´es,Statistique et Mod´elisation,4 place Jussieu, 75005 Paris, France, [email protected] Abstract Sequential and temporal data arise in many fields of research, such as quantitative fi- nance, medicine, or computer vision. A novel approach for sequential learning, called the signature method and rooted in rough path theory, is considered. Its basic prin- ciple is to represent multidimensional paths by a graded feature set of their iterated integrals, called the signature. This approach relies critically on an embedding prin- ciple, which consists in representing discretely sampled data as paths, i.e., functions from [0, 1] to Rd. After a survey of machine learning methodologies for signatures, the influence of embeddings on prediction accuracy is investigated with an in-depth study of three recent and challenging datasets. It is shown that a specific embedding, called lead-lag, is systematically the strongest performer across all datasets and algorithms considered. Moreover, an empirical study reveals that computing signatures over the whole path domain does not lead to a loss of local information. It is concluded that, with a good embedding, combining signatures with other simple algorithms achieves results competitive with state-of-the-art, domain-specific approaches. Keywords: Sequential data, time series classification, functional data, signature. 1. Introduction Sequential or temporal data are arising in many fields of research, due to an in- crease in storage capacity and to the rise of machine learning techniques. An illustra- tion of this vitality is the recent relaunch of the Time Series Classification repository (Bagnall et al., 2018), with more than a hundred new datasets. -
Axiomatic Systems for Geometry∗
Axiomatic Systems for Geometry∗ George Francisy composed 6jan10, adapted 27jan15 1 Basic Concepts An axiomatic system contains a set of primitives and axioms. The primitives are Adaptation to the object names, but the objects they name are left undefined. This is why the primi- current course is in tives are also called undefined terms. The axioms are sentences that make assertions the margins. about the primitives. Such assertions are not provided with any justification, they are neither true nor false. However, every subsequent assertion about the primitives, called a theorem, must be a rigorously logical consequence of the axioms and previ- ously proved theorems. There are also formal definitions in an axiomatic system, but these serve only to simplify things. They establish new object names for complex combinations of primitives and previously defined terms. This kind of definition does not impart any `meaning', not yet, anyway. See Lesson A6 for an elaboration. If, however, a definite meaning is assigned to the primitives of the axiomatic system, called an interpretation, then the theorems become meaningful assertions which might be true or false. If for a given interpretation all the axioms are true, then everything asserted by the theorems also becomes true. Such an interpretation is called a model for the axiomatic system. In common speech, `model' is often used to mean an example of a class of things. In geometry, a model of an axiomatic system is an interpretation of its primitives for ∗From textitPost-Euclidean Geometry: Class Notes and Workbook, UpClose Printing & Copies, Champaign, IL 1995, 2004 yProf. George K.