The Method of Proof Analysis: Background, Developments, New Directions
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Admissible Rules and Their Complexity
Admissible rules and their complexity Emil Jeřábek [email protected] http://math.cas.cz/~jerabek/ Institute of Mathematics of the Czech Academy of Sciences, Prague 24th Applications of Logic in Philosophy and the Foundations of Mathematics Szklarska Poręba, May 2019 Outline of the talks 1 Logics and admissibility 2 Transitive modal logics 3 Toy model: logics of bounded depth 4 Projective formulas 5 Admissibility in clx logics 6 Problems and complexity classes 7 Complexity of derivability 8 Complexity of admissibility Logics and admissibility 1 Logics and admissibility 2 Transitive modal logics 3 Toy model: logics of bounded depth 4 Projective formulas 5 Admissibility in clx logics 6 Problems and complexity classes 7 Complexity of derivability 8 Complexity of admissibility Propositional logics Propositional logic L: Language: formulas built from atoms x0; x1; x2;::: using a fixed set of finitary connectives Consequence relation: a relation Γ `L ' between sets of formulas and formulas s.t. I ' `L ' I Γ `L ' implies Γ; ∆ `L ' I Γ; ∆ `L ' and 8 2 ∆ Γ `L imply Γ `L ' I Γ `L ' implies σ(Γ) `L σ(') for every substitution σ Emil Jeřábek Admissible rules and their complexity ALPFM 2019, Szklarska Poręba 1:78 Unifiers and admissible rules Γ; ∆: finite sets of formulas L-unifier of Γ: substitution σ s.t. `L σ(') for all ' 2 Γ Single-conclusion rule: Γ = ' Multiple-conclusion rule: Γ = ∆ I Γ = ∆ is L-derivable (or valid) if Γ `L δ for some δ 2 ∆ I Γ = ∆ is L-admissible (written as Γ ∼L ∆) if every L-unifier of Γ also unifies some δ 2 ∆ NB: Γ is L-unifiable -
The L-Framework Structural Proof Theory in Rewriting Logic
The L-Framework Structural Proof Theory in Rewriting Logic Carlos Olarte Joint work with Elaine Pimentel and Camilo Rocha. Avispa 25 Años Logical Frameworks Consider the following inference rule (tensor in Linear Logic): Γ ⊢ ∆ ⊢ F G i Γ; ∆ ⊢ F i G R Horn Clauses (Prolog) prove Upsilon (F tensor G) :- split Upsilon Gamma Delta, prove Gamma F, prove Delta G . Rewriting Logic (Maude) rl [tensorR] : Gamma, Delta |- F x G => (Gamma |- F) , (Delta |-G) . Gap between what is represented and its representation Rewriting Logic can rightfully be said to have “-representational distance” as a semantic and logical framework. (José Meseguer) Carlos Olarte, Joint work with Elaine Pimentel and Camilo Rocha. 2 Where is the Magic ? Rewriting logic: Equational theory + rewriting rules a Propositional logic op empty : -> Context [ctor] . op _,_ : Context Context -> Context [assoc comm id: empty] . eq F:Formula, F:Formula = F:Formula . --- idempotency a Linear Logic (no weakening / contraction) op _,_ : Context Context -> Context [assoc comm id: empty] . a Lambek’s logics without exchange op _,_ : Context Context -> Context . The general point is that, by choosing the right equations , we can capture any desired structural axiom. (José Meseguer) Carlos Olarte, Joint work with Elaine Pimentel and Camilo Rocha. 3 Determinism vs Non-Determinism Back to the tensor rule: Γ ⊢ F ∆ ⊢ G Γ; F1; F2 ⊢ G iR iL Γ; ∆ ⊢ F i G Γ; F1 i F2 ⊢ G Equations Deterministic (invertible) rules that can be eagerly applied. eq [tensorL] : Gamma, F1 * F2 |- G = Gamma, F1 , F2 |- G . Rules Non-deterministic (non-invertible) rules where backtracking is needed. -
Notes on Proof Theory
Notes on Proof Theory Master 1 “Informatique”, Univ. Paris 13 Master 2 “Logique Mathématique et Fondements de l’Informatique”, Univ. Paris 7 Damiano Mazza November 2016 1Last edit: March 29, 2021 Contents 1 Propositional Classical Logic 5 1.1 Formulas and truth semantics . 5 1.2 Atomic negation . 8 2 Sequent Calculus 10 2.1 Two-sided formulation . 10 2.2 One-sided formulation . 13 3 First-order Quantification 16 3.1 Formulas and truth semantics . 16 3.2 Sequent calculus . 19 3.3 Ultrafilters . 21 4 Completeness 24 4.1 Exhaustive search . 25 4.2 The completeness proof . 30 5 Undecidability and Incompleteness 33 5.1 Informal computability . 33 5.2 Incompleteness: a road map . 35 5.3 Logical theories . 38 5.4 Arithmetical theories . 40 5.5 The incompleteness theorems . 44 6 Cut Elimination 47 7 Intuitionistic Logic 53 7.1 Sequent calculus . 55 7.2 The relationship between intuitionistic and classical logic . 60 7.3 Minimal logic . 65 8 Natural Deduction 67 8.1 Sequent presentation . 68 8.2 Natural deduction and sequent calculus . 70 8.3 Proof tree presentation . 73 8.3.1 Minimal natural deduction . 73 8.3.2 Intuitionistic natural deduction . 75 1 8.3.3 Classical natural deduction . 75 8.4 Normalization (cut-elimination in natural deduction) . 76 9 The Curry-Howard Correspondence 80 9.1 The simply typed l-calculus . 80 9.2 Product and sum types . 81 10 System F 83 10.1 Intuitionistic second-order propositional logic . 83 10.2 Polymorphic types . 84 10.3 Programming in system F ...................... 85 10.3.1 Free structures . -
Bunched Hypersequent Calculi for Distributive Substructural Logics
EPiC Series in Computing Volume 46, 2017, Pages 417{434 LPAR-21. 21st International Conference on Logic for Programming, Artificial Intelligence and Reasoning Bunched Hypersequent Calculi for Distributive Substructural Logics Agata Ciabattoni and Revantha Ramanayake Technische Universit¨atWien, Austria fagata,[email protected]∗ Abstract We introduce a new proof-theoretic framework which enhances the expressive power of bunched sequents by extending them with a hypersequent structure. A general cut- elimination theorem that applies to bunched hypersequent calculi satisfying general rule conditions is then proved. We adapt the methods of transforming axioms into rules to provide cutfree bunched hypersequent calculi for a large class of logics extending the dis- tributive commutative Full Lambek calculus DFLe and Bunched Implication logic BI. The methodology is then used to formulate new logics equipped with a cutfree calculus in the vicinity of Boolean BI. 1 Introduction The wide applicability of logical methods and their use in new subject areas has resulted in an explosion of new logics. The usefulness of these logics often depends on the availability of an analytic proof calculus (formal proof system), as this provides a natural starting point for investi- gating metalogical properties such as decidability, complexity, interpolation and conservativity, for developing automated deduction procedures, and for establishing semantic properties like standard completeness [26]. A calculus is analytic when every derivation (formal proof) in the calculus has the property that every formula occurring in the derivation is a subformula of the formula that is ultimately proved (i.e. the subformula property). The use of an analytic proof calculus tremendously restricts the set of possible derivations of a given statement to deriva- tions with a discernible structure (in certain cases this set may even be finite). -
Bounded-Analytic Sequent Calculi and Embeddings for Hypersequent Logics?
Bounded-analytic sequent calculi and embeddings for hypersequent logics? Agata Ciabattoni1, Timo Lang1, and Revantha Ramanayake2;3 1 Institute of Logic and Computation, Technische Universit¨atWien, A-1040 Vienna, Austria. fagata,[email protected] 2 Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 4, NL-9747 AG Groningen, Netherlands. [email protected] 3 CogniGron (Groningen Cognitive Systems and Materials Center), University of Groningen, Nijenborgh 4, NL-9747 AG Groningen, Netherlands. Abstract. A sequent calculus with the subformula property has long been recognised as a highly favourable starting point for the proof the- oretic investigation of a logic. However, most logics of interest cannot be presented using a sequent calculus with the subformula property. In response, many formalisms more intricate than the sequent calculus have been formulated. In this work we identify an alternative: retain the se- quent calculus but generalise the subformula property to permit specific axiom substitutions and their subformulas. Our investigation leads to a classification of generalised subformula properties and is applied to infinitely many substructural, intermediate and modal logics (specifi- cally: those with a cut-free hypersequent calculus). We also develop a complementary perspective on the generalised subformula properties in terms of logical embeddings. This yields new complexity upper bounds for contractive-mingle substructural logics and situates isolated results on the so-called simple substitution property within a general theory. 1 Introduction What concepts are essential to the proof of a given statement? This is a funda- mental and long-debated question in logic since the time of Leibniz, Kant and Frege, when a broad notion of analytic proof was formulated to mean truth by conceptual containments, or purity of method in mathematical arguments. -
An Overview of Structural Proof Theory and Computing
An overview of Structural Proof Theory and Computing Dale Miller INRIA-Saclay & LIX, Ecole´ Polytechnique Palaiseau, France Madison, Wisconsin, 2 April 2012 Part of the Special Session in Structural Proof Theory and Computing 2012 ASL annual meeting Outline Setting the stage Overview of sequent calculus Focused proof systems This special session Alexis Saurin, University of Paris 7 Proof search and the logic of interaction David Baelde, ITU Copenhagen A proof theoretical journey from programming to model checking and theorem proving Stefan Hetzl, Vienna University of Technology Which proofs can be computed by cut-elimination? Marco Gaboardi, University of Pennsylvania Light Logics for Polynomial Time Computations Some themes within proof theory • Ordinal analysis of consistency proofs (Gentzen, Sch¨utte, Pohlers, etc) • Reverse mathematics (Friedman, Simpson, etc) • Proof complexity (Cook, Buss, Kraj´ıˇcek,Pudl´ak,etc) • Structural Proof Theory (Gentzen, Girard, Prawitz, etc) • Focus on the combinatorial and structural properties of proof. • Proofs and their constituent are elements of computation Proof normalization. Programs are proofs and computation is proof normalization (λ-conversion, cut-elimination). A foundations for functional programming. Curry-Howard Isomorphism. Proof search. Programs are theories and computation is the search for sequent proofs. A foundations for logic programming, model checking, and theorem proving. Many roles of logic in computation Computation-as-model: Computations happens, i.e., states change, communications occur, etc. Logic is used to make statements about computation. E.g., Hoare triples, modal logics. Computation-as-deduction: Elements of logic are used to model elements of computation directly. Many roles of logic in computation Computation-as-model: Computations happens, i.e., states change, communications occur, etc. -
On Analyticity in Deep Inference
On Analyticity in Deep Inference Paola Bruscoli and Alessio Guglielmi∗ University of Bath Abstract In this note, we discuss the notion of analyticity in deep inference and propose a formal definition for it. The idea is to obtain a notion that would guarantee the same properties that analyticity in Gentzen theory guarantees, in particular, some reasonable starting point for algorithmic proof search. Given that deep inference generalises Gentzen proof theory, the notion of analyticity discussed here could be useful in general, and we offer some reasons why this might be the case. This note is dedicated to Dale Miller on the occasion of his 60th birthday. Dale has been for both of us a friend and one of our main sources of scientific inspiration. Happy birthday Dale! 1 Introduction It seems that the notion of analyticity in proof theory has been defined by Smullyan, firstly for natural-deduction systems in [Smu65], then for tableaux systems in [Smu66]. Afterwards, analyticity appeared as a core concept in his influential textbook [Smu68b]. There is no widely accepted and general definition of analytic proof system; however, the commonest view today is that such a system is a collection of inference rules that all possess some form of the subfor- mula property, as Smullyan argued. Since analyticity in the sequent calculus is conceptually equivalent to analyticity in the other Gentzen formalisms, in this note we focus on the sequent calculus as a representative of all formalisms of traditional proof theory [Gen69]. Normally, the only rule in a sequent system that does not possess the subformula property is the cut rule. -
Negri, S and Von Plato, J Structural Proof Theory
Sequent Calculus for Intuitionistic Logic We present a system of sequent calculus for intuitionistic propositional logic. In later chapters we obtain stronger systems by adding rules to this basic system, and we therefore go through its proof-theoretical properties in detail, in particular the admissibility of structural rules and the basic consequences of cut elimination. Many of these properties can then be verified in a routine fashion for extensions of the system. We begin with a discussion of the significance of constructive reasoning. 2.1. CONSTRUCTIVE REASONING Intuitionistic logic, and intuitionism more generally, used to be philosophically motivated, but today the grounds for using intuitionistic logic can be completely neutral philosophically. Intuitionistic or constructive reasoning, which are the same thing, systematically supports computability: If the initial data in a problem or theorem are computable and if one reasons constructively, logic will never make one committed to an infinite computation. Classical logic, instead, does not make the distinction between the computable and the noncomputable. We illustrate these phenomena by an example: A mathematical colleague comes with an algorithm for generating a decimal expansion O.aia2a3..., and also gives a proof that if none of the decimals at is greater than zero, a contradiction follows. Then you are asked to find the first decimal ak such that ak > 0. But you are out of luck in this task, for several hours and days of computation bring forth only 0's Given two real numbers a and b, if it happens to be true that they are equal, a and b would have to be computed to infinite precision to verify a = b. -
On the Rules of Intermediate Logics
On the rules of intermediate logics Rosalie Iemhoff ∗ Institute for Discrete Mathematics and Geometry E104, Vienna University of Technology Wiedner Hauptstrasse 8-10, 1040 Vienna, Austria [email protected] http://www.logic.at/people/iemhoff Abstract If the Visser rules are admissible for an intermediate logic, they form a basis for the admissible rules of the logic. How to characterize the admissible rules of intermediate logics for which not all of the Visser rules are admissible is not known. In this paper we give a brief overview of results on admisisble rules in the context of intermediate logics. We apply these results to some well-known intermediate logics. We provide natural examples of logics for which the Visser rule are derivable, admissible but nonderivable, or not admissible. Keywords: Intermediate logics, admissible rules, realizability, Rieger-Nishimura for- mulas, Medvedev logic, Independence of Premise. 1 Introduction Admissible rules, the rules under which a theory is closed, form one of the most intriguing aspects of intermediate logics. A rule A/ B is admissible for a theory if B is provable in it whenever A is. The rule A/ B is said to be derivable if the theory proves that A B. Classical propositional logic CPC does not have any non-derivable admissible→ rules, because in this case A/ B is admissible if and only if A B is derivable, but for example intuitionistic propositional logic IPC has m→any admissible rules that are not derivable in the theory itself. For example, the Independence of Premise rule IPR A B C / ( A B) ( A C) ¬ → ∨ ¬ → ∨ ¬ → is not derivable as an implication within the system, but it is an admissible rule of it. -
Admissible Rules of Lukasiewicz Logic
Admissible rules ofLukasiewicz logic Emil Jeˇr´abek∗ Institute of Mathematics of the Academy of Sciences Zitn´a25,ˇ 115 67 Praha 1, Czech Republic, email: [email protected] October 13, 2009 Abstract We investigate admissible rules ofLukasiewicz multi-valued propositional logic. We show that admissibility of multiple-conclusion rules inLukasiewicz logic, as well as validity of universal sentences in free MV -algebras, is decidable (in PSPACE). 1 Introduction Investigation of nonclassical logics usually revolves around provability of formulas. When we generalize the problem from formulas to inference rules, there arises an important distinction between derivable and admissible rules, introduced by Lorenzen [16]. A rule ϕ1, . , ϕn / ψ is derivable if it belongs to the consequence relation of the logic (defined semantically, or by a proof system using a set of axioms and rules); and it is admissible if the set of theorems of the logic is closed under the rule. These two notions coincide for the standard consequence relation of classical logic, but nonclassical logics often admit rules which are not derivable. (A logic whose admissible rules are all derivable is called structurally complete.) For example, all superintuitionistic (si) logics admit the Kreisel–Putnam rule ¬p → q ∨ r / (¬p → q) ∨ (¬p → r) (Prucnal [21]), whereas many of these logics (such as IPC itself) do not derive this rule. The research of admissible rules was stimulated by a question of H. Friedman [5], asking whether admissibility of rules in IPC is decidable. The problem was extensively investigated in a series of papers by Rybakov, who has shown that admissibility is decidable for a large class of modal and si logics, found semantic criteria for admissibility, and obtained other results on various aspects of admissibility. -
Admissible Rules in Logic and Algebra
Admissible Rules in Logic and Algebra George Metcalfe Mathematics Institute University of Bern Pisa Summer Workshop on Proof Theory 12-15 June 2012, Pisa George Metcalfe (University of Bern) Admissible Rules in Logic and Algebra June2012,Pisa 1/107 Derivability vs Admissibility Consider a system defined by two rules: Nat(0) and Nat(x) ⊲ Nat(s(x)). The following rule is derivable: Nat(x) ⊲ Nat(s(s(x))). However, this rule is only admissible: Nat(s(x)) ⊲ Nat(x). But what if we add to the system: Nat(s(−1)) ??? George Metcalfe (University of Bern) Admissible Rules in Logic and Algebra June2012,Pisa 2/107 Derivability vs Admissibility Consider a system defined by two rules: Nat(0) and Nat(x) ⊲ Nat(s(x)). The following rule is derivable: Nat(x) ⊲ Nat(s(s(x))). However, this rule is only admissible: Nat(s(x)) ⊲ Nat(x). But what if we add to the system: Nat(s(−1)) ??? George Metcalfe (University of Bern) Admissible Rules in Logic and Algebra June2012,Pisa 2/107 Derivability vs Admissibility Consider a system defined by two rules: Nat(0) and Nat(x) ⊲ Nat(s(x)). The following rule is derivable: Nat(x) ⊲ Nat(s(s(x))). However, this rule is only admissible: Nat(s(x)) ⊲ Nat(x). But what if we add to the system: Nat(s(−1)) ??? George Metcalfe (University of Bern) Admissible Rules in Logic and Algebra June2012,Pisa 2/107 Derivability vs Admissibility Consider a system defined by two rules: Nat(0) and Nat(x) ⊲ Nat(s(x)). The following rule is derivable: Nat(x) ⊲ Nat(s(s(x))). -
Designing a Theorem Prover
Designing a Theorem Prover Lawrence C Paulson Computer Laboratory University of Cambridge May 1990 CONTENTS 2 Contents 1 Folderol: a simple theorem prover 3 1.1 Representation of rules :::::::::::::::::::::::::: 4 1.2 Propositional logic :::::::::::::::::::::::::::: 5 1.3 Quanti¯ers and uni¯cation :::::::::::::::::::::::: 7 1.4 Parameters in quanti¯er rules :::::::::::::::::::::: 7 2 Basic data structures and operations 10 2.1 Terms ::::::::::::::::::::::::::::::::::: 10 2.2 Formulae :::::::::::::::::::::::::::::::::: 11 2.3 Abstraction and substitution ::::::::::::::::::::::: 11 2.4 Parsing and printing ::::::::::::::::::::::::::: 13 3 Uni¯cation 14 3.1 Examples ::::::::::::::::::::::::::::::::: 15 3.2 Parameter dependencies in uni¯cation :::::::::::::::::: 16 3.3 The environment ::::::::::::::::::::::::::::: 17 3.4 The ML code for uni¯cation ::::::::::::::::::::::: 17 3.5 Extensions and omissions ::::::::::::::::::::::::: 18 3.6 Instantiation by the environment :::::::::::::::::::: 19 4 Inference in Folderol 20 4.1 Solving a goal ::::::::::::::::::::::::::::::: 21 4.2 Selecting a rule :::::::::::::::::::::::::::::: 22 4.3 Constructing the subgoals :::::::::::::::::::::::: 23 4.4 Goal selection ::::::::::::::::::::::::::::::: 25 5 Folderol in action 27 5.1 Propositional examples :::::::::::::::::::::::::: 27 5.2 Quanti¯er examples :::::::::::::::::::::::::::: 30 5.3 Beyond Folderol: advanced automatic methods ::::::::::::: 37 6 Interactive theorem proving 39 6.1 The Boyer/Moore theorem prover :::::::::::::::::::: 40 6.2 The Automath languages