Satisfiability Checking

Total Page:16

File Type:pdf, Size:1020Kb

Satisfiability Checking Satisfiability Checking First-Order Logic Prof. Dr. Erika Ábrahám RWTH Aachen University Informatik 2 LuFG Theory of Hybrid Systems WS 14/15 Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 1 / 38 First-order logic We have seen that natural languages are not well-suited for correct reasoning. Propositional logic is useful but sometimes not expressive enough for modeling. First-order (FO) logic is a framework with the syntactical ingredients: 1 Theory symbols: constants, variables, function symbols 2 Lifting from theory to the logical level: predicate symbols 3 Logical symbols: Logical connectives and quantifiers 3 is fixed Fixing 1 and 2 gives different FO instances Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 2 / 38 Constants, variables, function symbols, terms Theory symbols: constants, variables, function symbols Example: Constants:0 ; 1 Variables: x; y; z;::: Function symbol binary + Terms (theory expressions) are inductively defined by the following rules: 1 All constants and variables are terms. 2 If t1;:::; tn (n > 0) are terms and f an n-ary function symbol then f (t1; :::; tn) is a term. Only strings obtained by finitely many applications of these rules are terms. Example terms:0, x,0 + 1, x + 1, +(x; +(y; 1))( x + (y + 1)) Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 3 / 38 Predicates, constraints Predicates lift terms from the theory to the logical level. Example predicate symbols: binary ≥; >; =; <; ≤ (Theory) constraints are inductively defined by the following rule: 1 If P is an n-ary predicate symbol and t1;:::; tn are terms then P(t1;:::; tn) is a constraint. Only strings obtained by finitely many applications of this rule are constraints. Example constraints: x<x + 1, (x + 1) + y=((x + y) + 1) Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 4 / 38 Logical connectives and quantifiers, formulas Logical connectives: unary :, binary ^; _; !; $;::: Universal quantifier 8 (“for all”), existential quantifier 9 (“exists”) (Well-formed) formulas are inductively defined by the following rules: 1 If c is a constraint then c is a formula (called atomic formula). 2 If ' is a formula then (:') is a formula. 3 If ' and are formulas then (' ^ ) is a formula. 4 Similar rules apply to other binary logical connectives. 5 If ' is a formula and x is a variable, then (8x:') and (9x:') are formulas. Only expressions which can be obtained by finitely many applications of these rules are formulas. Example formulas: x < x + 1 (atomic formula) :(x < 0) x < x + 1 ^ (x + 1) + y =( x + y) + 1 8x:9y: y = x + 1 Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 5 / 38 Example Assume the argumentation: 1 All men are mortal. 2 Socrates is a man. 3 Therefore, Socrates is mortal. We can formalize it by defining Constants: Socrates Variables: x Predicate symbols: unary isMen, isMortal Formalization: 1 8x: isMen(x) ! isMortal(x) 2 isMen(Sokrates) 3 isMortal(Sokrates) Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 6 / 38 Some remarks and notation Constants can also be seen as function symbols of arity0. Sometimes equality (=) is included as a logical symbol. E.g., the logical connectives negation (:) and conjunction (^) and the existential quantifier (9) would be sufficient, the remaining syntax (_; !; $;:::; 8) are syntactic sugar. We omit parenthesis whenever we may restore them through operator precedence: binds stronger : ^ _ ! $ 9 8 Thus, we write: ::a for (:(:a)); 9a: 9b: (a ^ b ! P(a; b)) for 9a: 9b: ((a ^ b) ! P(a; b)) Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 7 / 38 Free and bound variables The free and bound variables of a formula are defined inductively: If ' is an atomic formula then a variable x is free in ' iff x occurs in '. Moreover, there are no bound variables in any atomic formula. A variable x is free in (:') iff x is free in '. Moreover, x is bound in (:') iff x is bound in '. x is free in (' ^ ) iff x is free in either ' or . Moreover, x is bound in (' ^ ) iff x is bound in either ' or . The same rule applies to any other binary connective in place of ^. x is free in (9y:') iff x is free in ' and x is a symbol different from y. Moreover, x is bound in (9y:') iff x is y or x is bound in '. The same rule holds with 8 in place of 9. Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 8 / 38 Free and bound variables Examples: In P(z) _ 8x: 8y: (P(x) ! Q(z)), x and y are bound variables, z is a free variable, and w is neither bound nor free. In Q(z) _ 8z:P(z), z is both bound and free. Being free or bound is for specific occurrences of variables in a formula. In Q(z) _ 8z:P(z), the first occurrence of z is free while the second is bound. Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 9 / 38 Signature Σ, Σ-formula, Σ-sentence A signature Σ fixes the set of non-logical symbols. A Σ-formula is a formula with non-logical symbols from Σ. A Σ-sentence is a Σ-formula without free variables. In the previous example: Σ = (Sokrates; isMen(·); isMortal(·)) with Sokrates a constant and isMen and isMortal unary predicate symbols. The formulas 1 8x: isMen(x) ! isMortal(x) 2 isMen(Sokrates) 3 isMortal(Sokrates) are Σ-sentences (the only variable x is bound). Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 10 / 38 Further examples Σ = f0; 1; +;> g 0; 1 are constant symbols + is a binary function symbol > is a binary predicate symbol Examples of Σ-sentences: 9x: 8y: x > y 8x: 9y: x > y 8x: x + 1 > x 8x: :(x + 0 > x _ x > x + 0) Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 11 / 38 Further examples Σ = f0; 1; +; ∗;<; isPrimeg 0; 1 constant symbols +; ∗ binary function symbols < binary predicate symbol isPrime unary predicate symbol An example Σ-sentence: 8n: (1 < n ! (9p: isPrime(p) ^ n < p < 2 ∗ n)) Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 12 / 38 Example Let Σ = f0; 1; +; =g where0 ; 1 are constants, + is a binary function symbol and = a binary predicate symbol. Let ' = 9x: x + 0 = 1a Σ-formula. Q: Is ' true? A: So far these are only symbols, strings. No meaning yet. Q: What do we need to fix for the semantics? A: We need a domain for the variables. Let’s say N0. Q: Is ' true in N0? A: Depends on the interpretation of ’+’ and ’=’! Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 13 / 38 Structures, satisfiability, validity A Σ-structure is given by: a domain D, an interpretation I of the non-logical symbols in Σ that maps each constant symbol to a domain element, each (free) variable to a domain element, each function symbol of arity n to a function of type Dn ! D, and each predicate symbol of arity n to a predicate of type Dn ! f0; 1g. A Σ-formula is satisfiable if there exists a Σ-structure that satisfies it. A Σ-formula is valid if it is satisfied by all Σ-structures. Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 14 / 38 Semantics Semantics of terms and formulas under a structure (D; I ): constants: [[c]](D;I ) = I (c) variables: [[x]](D;I ) = I (x) functions: [[f (t1;:::; tn)]](D;I ) = I (f )([[t1]](D;I );:::; [[tn]](D;I )) predicates: [[p(t1;:::; tn)]](D;I ) = I (p)([[t1]](D;I );:::; [[tn]](D;I )) logical structure: 1 if [[']](D;I ) = 0 [[:']](D;I ) = 0 if [[']](D;I ) = 1 1 if [[']](D;I ) = 1 and [[ ]](D;I ) = 1 [[' ^ ]](D;I ) = 0 if [[']](D;I ) = 0 or [[ ]](D;I ) = 0 1 if there exists v2D such that [[']](D;I [x7!v]) = 1 [[9x:']](D;I ) = 0 if for all v2D we have [[']](D;I [x7!v]) = 0 Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 15 / 38 Example Σ = f0; 1; +; =g ' = 9x: x + 0 = 1a Σ-formula Q: Is ' satisfiable? A: Yes. Consider the structure S: Domain: N0 Interpretation: 0 and1 are mapped to0 and1 in N0 + means addition = means equality S satisfies '. S is said to be a model of '. Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 16 / 38 Example (cont.) Σ = f0; 1; +; =g ' = 9x: x + 0 = 1a Σ-formula Q: Is ' valid? A: No. Consider the structure S0: Domain: N0 Interpretation: 0 and1 are mapped to0 and1 in N0 + means multiplication = means equality S0 does not satisfy '. Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 17 / 38 Theories T , T -safisfiability and T -validity A Σ-theory T is defined by a set of Σ-sentences. A Σ-formula ' is T -satisfiable if there exists a structure that satisfies both the sentences of T and '. A Σ-formula ' is T -valid if all structures that satisfy the sentences defining T also satisfy '. The number of sentences that are necessary for defining a theory may be large or infinite. Instead, it is common to define a theory through a set of axioms. The theory is defined by these axioms and everything that can be inferred from them by a sound inference system. Satisfiability Checking — Prof. Dr. Erika Ábrahám (RWTH Aachen University) WS 14/15 18 / 38 Examples Σ = f0; 1; +; =g ' = 9x: x + 0 = 1a Σ-formula.
Recommended publications
  • “The Church-Turing “Thesis” As a Special Corollary of Gödel's
    “The Church-Turing “Thesis” as a Special Corollary of Gödel’s Completeness Theorem,” in Computability: Turing, Gödel, Church, and Beyond, B. J. Copeland, C. Posy, and O. Shagrir (eds.), MIT Press (Cambridge), 2013, pp. 77-104. Saul A. Kripke This is the published version of the book chapter indicated above, which can be obtained from the publisher at https://mitpress.mit.edu/books/computability. It is reproduced here by permission of the publisher who holds the copyright. © The MIT Press The Church-Turing “ Thesis ” as a Special Corollary of G ö del ’ s 4 Completeness Theorem 1 Saul A. Kripke Traditionally, many writers, following Kleene (1952) , thought of the Church-Turing thesis as unprovable by its nature but having various strong arguments in its favor, including Turing ’ s analysis of human computation. More recently, the beauty, power, and obvious fundamental importance of this analysis — what Turing (1936) calls “ argument I ” — has led some writers to give an almost exclusive emphasis on this argument as the unique justification for the Church-Turing thesis. In this chapter I advocate an alternative justification, essentially presupposed by Turing himself in what he calls “ argument II. ” The idea is that computation is a special form of math- ematical deduction. Assuming the steps of the deduction can be stated in a first- order language, the Church-Turing thesis follows as a special case of G ö del ’ s completeness theorem (first-order algorithm theorem). I propose this idea as an alternative foundation for the Church-Turing thesis, both for human and machine computation. Clearly the relevant assumptions are justified for computations pres- ently known.
    [Show full text]
  • Axiomatic Set Teory P.D.Welch
    Axiomatic Set Teory P.D.Welch. August 16, 2020 Contents Page 1 Axioms and Formal Systems 1 1.1 Introduction 1 1.2 Preliminaries: axioms and formal systems. 3 1.2.1 The formal language of ZF set theory; terms 4 1.2.2 The Zermelo-Fraenkel Axioms 7 1.3 Transfinite Recursion 9 1.4 Relativisation of terms and formulae 11 2 Initial segments of the Universe 17 2.1 Singular ordinals: cofinality 17 2.1.1 Cofinality 17 2.1.2 Normal Functions and closed and unbounded classes 19 2.1.3 Stationary Sets 22 2.2 Some further cardinal arithmetic 24 2.3 Transitive Models 25 2.4 The H sets 27 2.4.1 H - the hereditarily finite sets 28 2.4.2 H - the hereditarily countable sets 29 2.5 The Montague-Levy Reflection theorem 30 2.5.1 Absoluteness 30 2.5.2 Reflection Theorems 32 2.6 Inaccessible Cardinals 34 2.6.1 Inaccessible cardinals 35 2.6.2 A menagerie of other large cardinals 36 3 Formalising semantics within ZF 39 3.1 Definite terms and formulae 39 3.1.1 The non-finite axiomatisability of ZF 44 3.2 Formalising syntax 45 3.3 Formalising the satisfaction relation 46 3.4 Formalising definability: the function Def. 47 3.5 More on correctness and consistency 48 ii iii 3.5.1 Incompleteness and Consistency Arguments 50 4 The Constructible Hierarchy 53 4.1 The L -hierarchy 53 4.2 The Axiom of Choice in L 56 4.3 The Axiom of Constructibility 57 4.4 The Generalised Continuum Hypothesis in L.
    [Show full text]
  • Propositional Logic
    Propositional Logic - Review Predicate Logic - Review Propositional Logic: formalisation of reasoning involving propositions Predicate (First-order) Logic: formalisation of reasoning involving predicates. • Proposition: a statement that can be either true or false. • Propositional variable: variable intended to represent the most • Predicate (sometimes called parameterized proposition): primitive propositions relevant to our purposes a Boolean-valued function. • Given a set S of propositional variables, the set F of propositional formulas is defined recursively as: • Domain: the set of possible values for a predicate’s Basis: any propositional variable in S is in F arguments. Induction step: if p and q are in F, then so are ⌐p, (p /\ q), (p \/ q), (p → q) and (p ↔ q) 1 2 Predicate Logic – Review cont’ Predicate Logic - Review cont’ Given a first-order language L, the set F of predicate (first-order) •A first-order language consists of: formulas is constructed inductively as follows: - an infinite set of variables Basis: any atomic formula in L is in F - a set of predicate symbols Inductive step: if e and f are in F and x is a variable in L, - a set of constant symbols then so are the following: ⌐e, (e /\ f), (e \/ f), (e → f), (e ↔ f), ∀ x e, ∃ s e. •A term is a variable or a constant symbol • An occurrence of a variable x is free in a formula f if and only •An atomic formula is an expression of the form p(t1,…,tn), if it does not occur within a subformula e of f of the form ∀ x e where p is a n-ary predicate symbol and each ti is a term.
    [Show full text]
  • First Order Logic and Nonstandard Analysis
    First Order Logic and Nonstandard Analysis Julian Hartman September 4, 2010 Abstract This paper is intended as an exploration of nonstandard analysis, and the rigorous use of infinitesimals and infinite elements to explore properties of the real numbers. I first define and explore first order logic, and model theory. Then, I prove the compact- ness theorem, and use this to form a nonstandard structure of the real numbers. Using this nonstandard structure, it it easy to to various proofs without the use of limits that would otherwise require their use. Contents 1 Introduction 2 2 An Introduction to First Order Logic 2 2.1 Propositional Logic . 2 2.2 Logical Symbols . 2 2.3 Predicates, Constants and Functions . 2 2.4 Well-Formed Formulas . 3 3 Models 3 3.1 Structure . 3 3.2 Truth . 4 3.2.1 Satisfaction . 5 4 The Compactness Theorem 6 4.1 Soundness and Completeness . 6 5 Nonstandard Analysis 7 5.1 Making a Nonstandard Structure . 7 5.2 Applications of a Nonstandard Structure . 9 6 Sources 10 1 1 Introduction The founders of modern calculus had a less than perfect understanding of the nuts and bolts of what made it work. Both Newton and Leibniz used the notion of infinitesimal, without a rigorous understanding of what they were. Infinitely small real numbers that were still not zero was a hard thing for mathematicians to accept, and with the rigorous development of limits by the likes of Cauchy and Weierstrass, the discussion of infinitesimals subsided. Now, using first order logic for nonstandard analysis, it is possible to create a model of the real numbers that has the same properties as the traditional conception of the real numbers, but also has rigorously defined infinite and infinitesimal elements.
    [Show full text]
  • Logic and Proof
    Logic and Proof Computer Science Tripos Part IB Lent Term Lawrence C Paulson Computer Laboratory University of Cambridge [email protected] Copyright c 2018 by Lawrence C. Paulson I Logic and Proof 101 Introduction to Logic Logic concerns statements in some language. The language can be natural (English, Latin, . ) or formal. Some statements are true, others false or meaningless. Logic concerns relationships between statements: consistency, entailment, . Logical proofs model human reasoning (supposedly). Lawrence C. Paulson University of Cambridge I Logic and Proof 102 Statements Statements are declarative assertions: Black is the colour of my true love’s hair. They are not greetings, questions or commands: What is the colour of my true love’s hair? I wish my true love had hair. Get a haircut! Lawrence C. Paulson University of Cambridge I Logic and Proof 103 Schematic Statements Now let the variables X, Y, Z, . range over ‘real’ objects Black is the colour of X’s hair. Black is the colour of Y. Z is the colour of Y. Schematic statements can even express questions: What things are black? Lawrence C. Paulson University of Cambridge I Logic and Proof 104 Interpretations and Validity An interpretation maps variables to real objects: The interpretation Y 7 coal satisfies the statement Black is the colour→ of Y. but the interpretation Y 7 strawberries does not! A statement A is valid if→ all interpretations satisfy A. Lawrence C. Paulson University of Cambridge I Logic and Proof 105 Consistency, or Satisfiability A set S of statements is consistent if some interpretation satisfies all elements of S at the same time.
    [Show full text]
  • Satisfiability 6 the Decision Problem 7
    Satisfiability Difficult Problems Dealing with SAT Implementation Klaus Sutner Carnegie Mellon University 2020/02/04 Finding Hard Problems 2 Entscheidungsproblem to the Rescue 3 The Entscheidungsproblem is solved when one knows a pro- cedure by which one can decide in a finite number of operations So where would be look for hard problems, something that is eminently whether a given logical expression is generally valid or is satis- decidable but appears to be outside of P? And, we’d like the problem to fiable. The solution of the Entscheidungsproblem is of funda- be practical, not some monster from CRT. mental importance for the theory of all fields, the theorems of which are at all capable of logical development from finitely many The Circuit Value Problem is a good indicator for the right direction: axioms. evaluating Boolean expressions is polynomial time, but relatively difficult D. Hilbert within P. So can we push CVP a little bit to force it outside of P, just a little bit? In a sense, [the Entscheidungsproblem] is the most general Say, up into EXP1? problem of mathematics. J. Herbrand Exploiting Difficulty 4 Scaling Back 5 Herbrand is right, of course. As a research project this sounds like a Taking a clue from CVP, how about asking questions about Boolean fiasco. formulae, rather than first-order? But: we can turn adversity into an asset, and use (some version of) the Probably the most natural question that comes to mind here is Entscheidungsproblem as the epitome of a hard problem. Is ϕ(x1, . , xn) a tautology? The original Entscheidungsproblem would presumable have included arbitrary first-order questions about number theory.
    [Show full text]
  • Solving the Boolean Satisfiability Problem Using the Parallel Paradigm Jury Composition
    Philosophæ doctor thesis Hoessen Benoît Solving the Boolean Satisfiability problem using the parallel paradigm Jury composition: PhD director Audemard Gilles Professor at Universit´ed'Artois PhD co-director Jabbour Sa¨ıd Assistant Professor at Universit´ed'Artois PhD co-director Piette C´edric Assistant Professor at Universit´ed'Artois Examiner Simon Laurent Professor at University of Bordeaux Examiner Dequen Gilles Professor at University of Picardie Jules Vernes Katsirelos George Charg´ede recherche at Institut national de la recherche agronomique, Toulouse Abstract This thesis presents different technique to solve the Boolean satisfiability problem using parallel and distributed architec- tures. In order to provide a complete explanation, a careful presentation of the CDCL algorithm is made, followed by the state of the art in this domain. Once presented, two proposi- tions are made. The first one is an improvement on a portfo- lio algorithm, allowing to exchange more data without loosing efficiency. The second is a complete library with its API al- lowing to easily create distributed SAT solver. Keywords: SAT, parallelism, distributed, solver, logic R´esum´e Cette th`ese pr´esente diff´erentes techniques permettant de r´esoudre le probl`eme de satisfaction de formule bool´eenes utilisant le parall´elismeet du calcul distribu´e. Dans le but de fournir une explication la plus compl`ete possible, une pr´esentation d´etaill´ee de l'algorithme CDCL est effectu´ee, suivi d'un ´etatde l'art. De ce point de d´epart,deux pistes sont explor´ees. La premi`ereest une am´eliorationd'un algorithme de type portfolio, permettant d'´echanger plus d'informations sans perte d’efficacit´e.
    [Show full text]
  • Self-Similarity in the Foundations
    Self-similarity in the Foundations Paul K. Gorbow Thesis submitted for the degree of Ph.D. in Logic, defended on June 14, 2018. Supervisors: Ali Enayat (primary) Peter LeFanu Lumsdaine (secondary) Zachiri McKenzie (secondary) University of Gothenburg Department of Philosophy, Linguistics, and Theory of Science Box 200, 405 30 GOTEBORG,¨ Sweden arXiv:1806.11310v1 [math.LO] 29 Jun 2018 2 Contents 1 Introduction 5 1.1 Introductiontoageneralaudience . ..... 5 1.2 Introduction for logicians . .. 7 2 Tour of the theories considered 11 2.1 PowerKripke-Plateksettheory . .... 11 2.2 Stratifiedsettheory ................................ .. 13 2.3 Categorical semantics and algebraic set theory . ....... 17 3 Motivation 19 3.1 Motivation behind research on embeddings between models of set theory. 19 3.2 Motivation behind stratified algebraic set theory . ...... 20 4 Logic, set theory and non-standard models 23 4.1 Basiclogicandmodeltheory ............................ 23 4.2 Ordertheoryandcategorytheory. ...... 26 4.3 PowerKripke-Plateksettheory . .... 28 4.4 First-order logic and partial satisfaction relations internal to KPP ........ 32 4.5 Zermelo-Fraenkel set theory and G¨odel-Bernays class theory............ 36 4.6 Non-standardmodelsofsettheory . ..... 38 5 Embeddings between models of set theory 47 5.1 Iterated ultrapowers with special self-embeddings . ......... 47 5.2 Embeddingsbetweenmodelsofsettheory . ..... 57 5.3 Characterizations.................................. .. 66 6 Stratified set theory and categorical semantics 73 6.1 Stratifiedsettheoryandclasstheory . ...... 73 6.2 Categoricalsemantics ............................... .. 77 7 Stratified algebraic set theory 85 7.1 Stratifiedcategoriesofclasses . ..... 85 7.2 Interpretation of the Set-theories in the Cat-theories ................ 90 7.3 ThesubtoposofstronglyCantorianobjects . ....... 99 8 Where to go from here? 103 8.1 Category theoretic approach to embeddings between models of settheory .
    [Show full text]
  • Monadic Decomposition
    Monadic Decomposition Margus Veanes1, Nikolaj Bjørner1, Lev Nachmanson1, and Sergey Bereg2 1 Microsoft Research {margus,nbjorner,levnach}@microsoft.com 2 The University of Texas at Dallas [email protected] Abstract. Monadic predicates play a prominent role in many decid- able cases, including decision procedures for symbolic automata. We are here interested in discovering whether a formula can be rewritten into a Boolean combination of monadic predicates. Our setting is quantifier- free formulas over a decidable background theory, such as arithmetic and we here develop a semi-decision procedure for extracting a monadic decomposition of a formula when it exists. 1 Introduction Classical decidability results of fragments of logic [7] are based on careful sys- tematic study of restricted cases either by limiting allowed symbols of the lan- guage, limiting the syntax of the formulas, fixing the background theory, or by using combinations of such restrictions. Many decidable classes of problems, such as monadic first-order logic or the L¨owenheim class [29], the L¨ob-Gurevich class [28], monadic second-order logic with one successor (S1S) [8], and monadic second-order logic with two successors (S2S) [35] impose at some level restric- tions to monadic or unary predicates to achieve decidability. Here we propose and study an orthogonal problem of whether and how we can transform a formula that uses multiple free variables into a simpler equivalent formula, but where the formula is not a priori syntactically or semantically restricted to any fixed fragment of logic. Simpler in this context means that we have eliminated all theory specific dependencies between the variables and have transformed the formula into an equivalent Boolean combination of predicates that are “essentially” unary.
    [Show full text]
  • Foundations of Mathematics
    Foundations of Mathematics November 27, 2017 ii Contents 1 Introduction 1 2 Foundations of Geometry 3 2.1 Introduction . .3 2.2 Axioms of plane geometry . .3 2.3 Non-Euclidean models . .4 2.4 Finite geometries . .5 2.5 Exercises . .6 3 Propositional Logic 9 3.1 The basic definitions . .9 3.2 Disjunctive Normal Form Theorem . 11 3.3 Proofs . 12 3.4 The Soundness Theorem . 19 3.5 The Completeness Theorem . 20 3.6 Completeness, Consistency and Independence . 22 4 Predicate Logic 25 4.1 The Language of Predicate Logic . 25 4.2 Models and Interpretations . 27 4.3 The Deductive Calculus . 29 4.4 Soundness Theorem for Predicate Logic . 33 5 Models for Predicate Logic 37 5.1 Models . 37 5.2 The Completeness Theorem for Predicate Logic . 37 5.3 Consequences of the completeness theorem . 41 5.4 Isomorphism and elementary equivalence . 43 5.5 Axioms and Theories . 47 5.6 Exercises . 48 6 Computability Theory 51 6.1 Introduction and Examples . 51 6.2 Finite State Automata . 52 6.3 Exercises . 55 6.4 Turing Machines . 56 6.5 Recursive Functions . 61 6.6 Exercises . 67 iii iv CONTENTS 7 Decidable and Undecidable Theories 69 7.1 Introduction . 69 7.1.1 Gödel numbering . 69 7.2 Decidable vs. Undecidable Logical Systems . 70 7.3 Decidable Theories . 71 7.4 Gödel’s Incompleteness Theorems . 74 7.5 Exercises . 81 8 Computable Mathematics 83 8.1 Computable Combinatorics . 83 8.2 Computable Analysis . 85 8.2.1 Computable Real Numbers . 85 8.2.2 Computable Real Functions .
    [Show full text]
  • Mathematical Logic
    Copyright c 1998–2005 by Stephen G. Simpson Mathematical Logic Stephen G. Simpson December 15, 2005 Department of Mathematics The Pennsylvania State University University Park, State College PA 16802 http://www.math.psu.edu/simpson/ This is a set of lecture notes for introductory courses in mathematical logic offered at the Pennsylvania State University. Contents Contents 1 1 Propositional Calculus 3 1.1 Formulas ............................... 3 1.2 Assignments and Satisfiability . 6 1.3 LogicalEquivalence. 10 1.4 TheTableauMethod......................... 12 1.5 TheCompletenessTheorem . 18 1.6 TreesandK¨onig’sLemma . 20 1.7 TheCompactnessTheorem . 21 1.8 CombinatorialApplications . 22 2 Predicate Calculus 24 2.1 FormulasandSentences . 24 2.2 StructuresandSatisfiability . 26 2.3 TheTableauMethod......................... 31 2.4 LogicalEquivalence. 37 2.5 TheCompletenessTheorem . 40 2.6 TheCompactnessTheorem . 46 2.7 SatisfiabilityinaDomain . 47 3 Proof Systems for Predicate Calculus 50 3.1 IntroductiontoProofSystems. 50 3.2 TheCompanionTheorem . 51 3.3 Hilbert-StyleProofSystems . 56 3.4 Gentzen-StyleProofSystems . 61 3.5 TheInterpolationTheorem . 66 4 Extensions of Predicate Calculus 71 4.1 PredicateCalculuswithIdentity . 71 4.2 TheSpectrumProblem . .. .. .. .. .. .. .. .. .. 75 4.3 PredicateCalculusWithOperations . 78 4.4 Predicate Calculus with Identity and Operations . ... 82 4.5 Many-SortedPredicateCalculus . 84 1 5 Theories, Models, Definability 87 5.1 TheoriesandModels ......................... 87 5.2 MathematicalTheories. 89 5.3 DefinabilityoveraModel . 97 5.4 DefinitionalExtensionsofTheories . 100 5.5 FoundationalTheories . 103 5.6 AxiomaticSetTheory . 106 5.7 Interpretability . 111 5.8 Beth’sDefinabilityTheorem. 112 6 Arithmetization of Predicate Calculus 114 6.1 Primitive Recursive Arithmetic . 114 6.2 Interpretability of PRA in Z1 ....................114 6.3 G¨odelNumbers ............................ 114 6.4 UndefinabilityofTruth. 117 6.5 TheProvabilityPredicate .
    [Show full text]
  • Turing-Computable Functions  • Let F :(Σ−{ })∗ → Σ∗
    Turing-Computable Functions • Let f :(Σ−{ })∗ → Σ∗. – Optimization problems, root finding problems, etc. • Let M be a TM with alphabet Σ. • M computes f if for any string x ∈ (Σ −{ })∗, M(x)=f(x). • We call f a recursive functiona if such an M exists. aKurt G¨odel (1931, 1934). c 2017 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 60 Kurt G¨odela (1906–1978) Quine (1978), “this the- orem [···] sealed his im- mortality.” aThis photo was taken by Alfred Eisenstaedt (1898–1995). c 2017 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 61 Church’s Thesis or the Church-Turing Thesis • What is computable is Turing-computable; TMs are algorithms.a • No “intuitively computable” problems have been shown not to be Turing-computable, yet.b aChurch (1935); Kleene (1953). bQuantum computer of Manin (1980) and Feynman (1982) and DNA computer of Adleman (1994). c 2017 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 62 Church’s Thesis or the Church-Turing Thesis (concluded) • Many other computation models have been proposed. – Recursive function (G¨odel), λ calculus (Church), formal language (Post), assembly language-like RAM (Shepherdson & Sturgis), boolean circuits (Shannon), extensions of the Turing machine (more strings, two-dimensional strings, and so on), etc. • All have been proved to be equivalent. c 2017 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 63 Alonso Church (1903–1995) c 2017 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 64 Extended Church’s Thesisa • All “reasonably succinct encodings” of problems are polynomially related (e.g., n2 vs. n6). – Representations of a graph as an adjacency matrix and as a linked list are both succinct.
    [Show full text]