DOCSLIB.ORG

The Complete Proof Theory of Hybrid Systems

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Complete Proof Theory of Hybrid Systems c 2012 IEEE. 2012 27th Annual ACM/IEEE Symposium on Logic in Computer Science The Complete Proof Theory of Hybrid Systems Andre´ Platzer Computer Science Department Carnegie Mellon University Pittsburgh, USA [email protected] Abstract—Hybrid systems are a fusion of continuous dynamical prove is true. Soundness should be sine qua non for formal systems and discrete dynamical systems. They freely combine verification, but is so complex for hybrid systems [7], [27] dynamical features from both worlds. For that reason, it has that it is often inadvertently forsaken. In logic, we can simply often been claimed that hybrid systems are more challenging than continuous dynamical systems and than discrete systems. ensure soundness by checking it locally per proof rule. We now show that, proof-theoretically, this is not the case. We More intriguingly, however, our logical setting also enables present a complete proof-theoretical alignment that interreduces us to ask the converse: is the proof calculus complete, i.e., can the discrete dynamics and the continuous dynamics of hybrid it prove all that is true? A corollary to Godel’s¨ incompleteness systems. We give a sound and complete axiomatization of hybrid theorem shows that hybrid systems do not have a sound systems relative to continuous dynamical systems and a sound and complete axiomatization of hybrid systems relative to dis- and complete calculus that is fully effective, because both crete dynamical systems. Thanks to our axiomatization, proving their discrete fragment and their continuous fragment alone properties of hybrid systems is exactly the same as proving are nonaxiomatizable since each can define integer arithmetic properties of continuous dynamical systems and again, exactly the [23, Theorem 2]. But logic can do better. The suitability same as proving properties of discrete dynamical systems. This of an axiomatization can still be established by showing fundamental cornerstone sheds light on the nature of hybridness and enables flexible and provably perfect combinations of discrete completeness relative to a fragment [8], [15]. This relative reasoning with continuous reasoning that lift to all aspects of completeness, in which we assume we were able to prove hybrid systems and their fragments. valid formulas in a fragment and prove that we can then Index Terms—proof theory; hybrid dynamical systems; differ- prove all others, also tells us how subproblems are related ential dynamic logic; axiomatization; completeness computationally. It tells us whether one subproblem dominates the others. Standard relative completeness [8], [15], however, I. INTRODUCTION which works relative to the data logic, is inadequate for hybrid Hybrid systems are dynamical systems that combine dis- systems, whose complexity comes from the dynamics, not the crete dynamics and continuous dynamics. They play an im- data logic, first-order real arithmetic, which is decidable [30]. portant role, e.g., in modeling systems that use computers In this paper, we answer an open problem about hybrid to control physical systems. Hybrid systems feature (iterated) systems proof theory [23]. We prove that differential dynamic difference equations for discrete dynamics and differential logic (dL), which is a logic of hybrid systems, has a sound equations for continuous dynamics. They, further, combine and complete axiomatization relative to its discrete fragment. conditional switching, nondeterminism, and repetition. The This is the first discrete relative completeness result for hybrid theory of hybrid systems concluded that very limited classes systems. of systems are undecidable [4], [6], [16]. Most hybrid systems Together with our previous result of a sound and complete research since focused on practical approaches for efficient axiomatization of hybrid systems relative to the continuous approximate reachability analysis for classes of hybrid systems fragment of dL [23], we obtain a complete alignment of the [3], [7], [13], [27]. Undecidability also did not stop researchers proof theories of hybrid systems, of continuous dynamical in program verification from making impressive progress. systems, and of discrete dynamical systems. Even though these This progress, however, concerned both the practice and the classes of dynamical systems seem to have quite different theory, where logic was the key to studying the theory beyond intuitive expressiveness, their proof theories actually align undecidability [8], [14], [15], [21], [28]. perfectly and make them (provably) interreducible. Our dL We take a logical perspective, with which we study the calculus can prove properties of hybrid systems exactly as logical foundations of hybrid systems and obtain interesting good as properties of continuous systems can be proved, proof-theoretical relationships in spite of undecidability. We which, in turn, our calculus can do exactly as good as discrete have developed a logic and proof calculus for hybrid systems systems can be proved. Exactly as good as any one of those [23], [25] in which it becomes meaningful to investigate subquestions can be solved, dL can solve all others. Relative to concepts like “what is true for a hybrid system” and “what the fragment for either system class, our dL calculus can prove can be proved about a hybrid system” and investigate how all valid properties for the others. It lifts any approximation for they are related. Our proof calculus is sound, i.e., all it can the fragment perfectly to all hybrid systems. This also defines ANDRE´ PLATZER THE COMPLETE PROOF THEORY OF HYBRID SYSTEMS 2 a relative decision procedure for dL sentences, because our [25]. We, e.g., write x0 = θ for the unrestricted differential completeness proofs are constructive. equation x0 = θ & true. We allow differential equation sys- On top of its theoretical value and the full provability tems and use vectorial notation. Vectorial assignments are alignment that our new result shows, our discrete complete- definable from scalar assignments (and ;). ness result is significant in that—in computer science and A state ν is a mapping from variables to R. Hence ν(x) 2 R verification—programs are closer to being understood than dif- is the value of variable x in state ν. The set of states is denoted ferential equations. Well-established and (partially) automated S. We denote the value of term θ in ν by [[θ]]ν . Each HP α is machinery exists for classical program verification, which, interpreted semantically as a binary reachability relation ρ(α) according to our result, has unexpected direct applications over states, defined inductively by: in hybrid systems. Completeness relative to discrete systems • ρ(x := θ) = f(ν; !): ! = ν except that [[x]]! = [[θ]]ν g increases the confidence that discrete computers can solve • ρ(?χ) = f(ν; ν): ν j= χg 0 0 hybrid systems questions at all. Conversely, control theory • ρ(x = θ & χ) = f('(0);'(r)) : '(t) j= x = θ and provides valuable tools for understanding continuous systems. '(t) j= χ for all 0 ≤ t ≤ r for a solution ' : [0; r] !S Previously, it had been just as hard to generalize discrete 0 def d'(ζ)(x) of any duration rg; i.e., with '(t)(x ) = dζ (t), computer science techniques to continuous questions as it has ' solves the differential equation and satisfies χ at all been to generalize continuous control approaches to discrete times [23] phenomena, let alone to the mixed case of hybrid systems. • ρ(α [ β) = ρ(α) [ ρ(β) Overall, our results provide a perfect link between both • ρ(α; β) = ρ(β) ◦ ρ(α) worlds and allow—in a sound and complete, and constructive ∗ [ n n+1 n 0 • ρ(α ) = ρ(α ) with α ≡ α ; α and α ≡ ?true. way—to combine the best of both worlds. dL allows discrete n2N reasoning as well as continuous reasoning within one single We refer to our book [25] for a comprehensive background. logic and proof system. The dL calculus links and transfers We also refer to [25] for an elaboration how the case r = one side of reasoning in a provably perfect (that is sound and 0 (in which the only condition is '(0) j= χ) is captured by complete) way to the other side. For whatever question about the above definition. To avoid technicalities, we consider only a hybrid system (or its fragments) a discrete approach is more polynomial differential equations, which are all smooth. natural or promising, dL lifts this reasoning in a perfect way to continuous systems, and to hybrid systems, and vice versa B. dL Formulas for any part where a continuous approach is more useful. The formulas of differential dynamic logic (dL) are defined This complete alignment of the proof theories is a funda- by the grammar (where φ, are dL formulas, θ1; θ2 terms, x mental cornerstone for understanding hybridness and relations a variable, α a HP): between discrete and continuous dynamics. In a nutshell, we show that we can proof-theoretically equate: φ, ::= θ1 ≥ θ2 j :φ j φ ^ j 8x φ j [α]φ “hybrid = continuous = discrete” The satisfaction relation ν j= φ is as usual in first-order logic (of real arithmetic) with the addition that ν j= [α]φ iff ! j= φ II. DIFFERENTIAL DYNAMIC LOGIC for all ! with (ν; !) 2 ρ(α). The operator hαi dual to [α] A. Regular Hybrid Programs is defined by hαiφ ≡ :[α]:φ. Consequently, ν j= hαiφ iff We use (regular) hybrid programs (HP) [23] as hybrid ! j= φ for some ! with (ν; !) 2 ρ(α). Operators =; >; ≤; <; system models. HPs form a Kleene algebra with tests [19]. _; !; $; 9x can be defined as usual in first-order logic. A dL The atomic HPs are instantaneous discrete jump assignments formula φ is valid, written φ, iff ν j= φ for all states ν. 1 x := θ, tests ?χ of a first-order formula χ of real arithmetic, C. Axiomatization and differential equation (systems) x0 = θ & χ for a continuous dL evolution restricted to the domain of evolution described by Our axiomatization of is shown in Fig.
Load more

Recommended publications
  • Deep Inference
    Deep Inference Alessio Guglielmi University of Bath [email protected] 1 Introduction Deep inference could succinctly be described as an extreme form of linear logic [12]. It is a methodology for designing proof formalisms that generalise Gentzen formalisms, i.e. the sequent calculus and natural deduction [11]. In a sense, deep inference is obtained by applying some of the main concepts behind linear logic to the formalisms, i.e., to the rules by which proof systems are designed. By do- ing so, we obtain a better proof theory than the traditional one due to Gentzen. In fact, in deep inference we can provide proof systems for more logics, in a more regular and modular way, with smaller proofs, less syntax, less bureau- cracy and we have a chance to make substantial progress towards a solution to the century-old problem of the identity of proofs. The first manuscript on deep inference appeared in 1999 and the first refereed papers in 2001 [6, 19]. So far, two formalisms have been designed and developed in deep inference: the calcu- lus of structures [15] and open deduction [17]. A third one, nested sequents [5], introduces deep inference features into a more traditional Gentzen formalism. Essentially, deep inference tries to understand proof composition and proof normalisation (in a very liberal sense including cut elimination [11]) in the most logic-agnostic way. Thanks to it we obtain a deeper understanding of the nature of normalisation. It seems that normalisation is a primitive, simple phenomenon that manifests itself in more or less complicated ways that depend more on the choice of representation for proofs rather than their true mathematical na- ture.
    [Show full text]
  • Facilitating Meta-Theory Reasoning (Invited Paper)
    Facilitating Meta-Theory Reasoning (Invited Paper) Giselle Reis Carnegie Mellon University in Qatar [email protected] Structural proof theory is praised for being a symbolic approach to reasoning and proofs, in which one can define schemas for reasoning steps and manipulate proofs as a mathematical structure. For this to be possible, proof systems must be designed as a set of rules such that proofs using those rules are correct by construction. Therefore, one must consider all ways these rules can interact and prove that they satisfy certain properties which makes them “well-behaved”. This is called the meta-theory of a proof system. Meta-theory proofs typically involve many cases on structures with lots of symbols. The majority of cases are usually quite similar, and when a proof fails, it might be because of a sub-case on a very specific configuration of rules. Developing these proofs by hand is tedious and error-prone, and their combinatorial nature suggests they could be automated. There are various approaches on how to automate, either partially or completely, meta-theory proofs. In this paper, I will present some techniques that I have been involved in for facilitating meta-theory reasoning. 1 Introduction Structural proof theory is a branch of logic that studies proofs as mathematical objects, understanding the kinds of operations and transformations that can be done with them. To do that, one must represent proofs using a regular and unambiguous structure, which is constructed from a fixed set of rules. This set of rules is called a proof calculus, and there are many different ones.
    [Show full text]
  • Propositional Logic: Deductive Proof & Natural Deduction Part 1
    Propositional Logic: Deductive Proof & Natural Deduction Part 1 CS402, Spring 2016 Shin Yoo Shin Yoo Propositional Logic: Deductive Proof & Natural Deduction Part 1 Deductive Proof In propositional logic, a valid formula is a tautology. So far, we could show the validity of a formula φ in the following ways: Through the truth table for φ Obtain φ as a substitution instance of a formula known to be valid. That is, q ! (p ! q) is valid, therefore r ^ s ! (p _ q ! r ^ s) is also valid. Obtain φ through interchange of equivalent formulas. That is, if φ ≡ and φ is a subformula of a valid formula χ, χ0 obtained by replacing all occurrences of φ in χ with is also valid. Shin Yoo Propositional Logic: Deductive Proof & Natural Deduction Part 1 Deductive Proof Goals of logic: (given U), is φ valid? Theorem 1 (2.38, Ben-Ari) U j= φ iff j= A1 ^ ::: ^ An ! φ when U = fA1;:::; Ang. However, there are problems in semantic approach. Set of axioms may be infinite: for example, Peano and ZFC (Zermelo-Fraenkel set theory) theories cannot be finitely axiomatised. Hilbert system, H, uses axiom schema, which in turn generates an infinite number of axioms. We cannot write truth tables for these. The truth table itself is not always there! Very few logical systems have decision procedures for validity. For example, predicate logic does not have any such decision procedure. Shin Yoo Propositional Logic: Deductive Proof & Natural Deduction Part 1 Semantic vs. Syntax j= φ vs. ` φ Truth Tools Semantics Syntax Validity Proof All Interpretations Finite Proof Trees Undecidable Manual Heuristics (except propositional logic) Shin Yoo Propositional Logic: Deductive Proof & Natural Deduction Part 1 Deductive Proof A deductive proof system relies on a set of proof rules (also inference rules), which are in themselves syntactic transformations following specific patterns.
    [Show full text]
  • Agent-Based Blackboard Architecture for a Higher-Order Theorem Prover
    Masterarbeit am Institut für Informatik der Freien Universität Berlin, Arbeitsgruppe Künstliche Intelligenz Agent-based Blackboard Architecture for a Higher-Order Theorem Prover Max Wisniewski [email protected] Betreuer: PD. Dr. Christoph Benzmüller Berlin, den 17. Oktober 2014 Abstract The automated theorem prover Leo was one of the first systems able to prove theorems in higher-order logic. Since the first version of Leo many other systems emerged and outperformed Leo and its successor Leo-II. The Leo-III project’s aim is to develop a new prover reclaiming the lead in the area of higher-order theorem proving. Current competitive theorem provers sequentially manipulate sets of formulas in a global loop to obtain a proof. Nowadays in almost every area in computer science, concurrent and parallel approaches are increas- ingly used. Although some research towards parallel theorem proving has been done and even some systems were implemented, most modern the- orem provers do not use any form of parallelism. In this thesis we present an architecture for Leo-III that use paral- lelism in its very core. To this end an agent-based blackboard architecture is employed. Agents denote independent programs which can act on their own. In comparison to classical theorem prover architectures, the global loop is broken down to a set of tasks that can be computed in paral- lel. The results produced by all agents will be stored in a blackboard, a globally shared datastructure, thus visible to all other agents. For a proof of concept example agents are given demonstrating an agent-based approach can be used to implemented a higher-order theorem prover.
    [Show full text]
  • Unifying Theories of Logics with Undefinedness
    Unifying Theories of Logics with Undefinedness Victor Petru Bandur PhD University of York Computer Science September, 2014 Abstract A relational approach to the question of how different logics relate formally is described. We consider three three-valued logics, as well as classical and semi-classical logic. A fundamental representation of three-valued predicates is developed in the Unifying Theories of Programming (UTP) framework of Hoare and He. On this foundation, the five logics are encoded semantically as UTP theories. Several fundamental relationships are revealed using theory linking mechanisms, which corroborate results found in the literature, and which have direct applicability to the sound mixing of logics in order to prove facts. The initial development of the fundamental three-valued predicate model, on which the theories are based, is then applied to the novel systems-of-systems specification language CML, in order to reveal proof obligations which bridge a gap that exists between the semantics of CML and the existing semantics of one of its sub-languages, VDM. Finally, a detailed account is given of an envisioned model theory for our proposed structuring, which aims to lift the sentences of the five logics encoded to the second order, allowing them to range over elements of existing UTP theories of computation, such as designs and CSP processes. We explain how this would form a complete treatment of logic interplay that is expressed entirely inside UTP. ii Contents Abstract ii Contents iii List of Figures vi Acknowledgments vii Author's Declaration viii 1 Background 1 1.1 Introduction . .1 1.2 Logic in Software Rationale .
    [Show full text]
  • Proof Terms for Classical Derivations Greg Restall* Philosophy Department the University of Melbourne [email protected] June 13, 2017 Version 0.922
    proof terms for classical derivations Greg Restall* Philosophy Department The University of Melbourne [email protected] june 13, 2017 Version 0.922 Abstract: I give an account of proof terms for derivations in a sequent calculus for classical propositional logic. The term for a derivation δ of a sequent Σ ∆ encodes how the premises Σ and conclusions ∆ are related in δ. This encoding is many–to–one in the sense that different derivations can have the same proof term, since different derivations may be different ways of representing the same underlying connection between premises and conclusions. However, not all proof terms for a sequent Σ ∆ are the same. There may be different ways to connect those premises and conclusions. Proof terms can be simplified in a process corresponding to the elimination of cut inferences in se- quentderivations. However, unlikecuteliminationinthesequentcalculus, eachproofterm hasa unique normal form (fromwhichallcutshavebeeneliminated)anditisstraightforwardtoshowthattermreduc- tion is strongly normalising—every reduction process terminates in that unique normal form. Further- more, proof terms are invariants for sequent derivations in a strong sense—two derivations δ1 and δ2 have the same proofterm if and only if some permutation of derivation steps sends δ1 to δ2 (given a rela- tively natural class of permutations of derivations in the sequent calculus). Since not every derivation of a sequent can be permuted into every other derivation of that sequent, proof terms provide a non-trivial account of the identity of proofs, independent of the syntactic representation of those proofs. outline This paper has six sections: Section 1 motivates the problem of proof identity, and reviews the reasons that the question of proof identity for classical propositional logic is difficult.
    [Show full text]
  • Introducing Substitution in Proof Theory
    Introducing Substitution in Proof Theory Alessio Guglielmi University of Bath 20 July 2014 This talk is available at http://cs.bath.ac.uk/ag/t/ISPT.pdf It is an abridged version of this other talk: http://cs.bath.ac.uk/ag/t/PCMTGI.pdf Deep inference web site: http://alessio.guglielmi.name/res/cos/ Outline Problem: compressing proofs. Solution: proof composition mechanisms beyond Gentzen. Open deduction: composition by connectives and inference, smaller analytic proofs than in Gentzen. Atomic flows: geometry is enough to normalise. Composition by substitution: more geometry, more efficiency, more naturality. ON THE PROOF COMPLEXITY OF DEEP INFERENCE 21 SKSg can analogously be extended, but there is no need to create a special rule; we only need to broaden the criterion by which we recognize a proof. Definition 5.3. An extended SKSg proof of α is an SKSg derivation with conclusion α ¯ ¯ ¯ ¯ ¯ ¯ and premiss [A1 β1] [β1 A1] [Ah βh ] [βh Ah ],whereA1, A1,...,Ah , Ah ∨ ∧ ∨ ∧ ···∧ ∨ ∧ ∨ are mutually distinct and A1 / β1,α and ... and Ah / β1,...,βh ,α.WedenotebyxSKSg the proof system whose proofs∈ are extended SKSg proofs.∈ Theorem 5.4. For every xFrege proof of length l and size n there exists an xSKSg proof of 2 the same formula and whose length and size are, respectively, O(l ) and O(n ). Proof. Consider an xFrege proof as in Definition 5.1. By Remark 5.2 and Theorem 4.6, there exists the following xSKSg proof, whose length and size are yielded by 4.6: ¯ ¯ ¯ ¯ [A1 β1] [β1 A1] [Ah βh ] [βh Ah ] ∨ ∧ ∨ ∧ ···∧ ∨ ∧ ∨ $ SKSg .
    [Show full text]
  • Logic Three Tutorials at Tbilisi
    Logic Three tutorials at Tbilisi Wilfrid Hodges 1.1 Queen Mary, University of London [email protected] FIRST TUTORIAL October 2003 Entailments Revised 17 October 2003. For copyright reasons some of the photos are not included. 1 2 1.2 1.3 Two sentences: If p and q are sentences, the expression (a) Noam Chomsky is very clever. p |= q (b) Noam Chomsky is clever. is called a sequent. If (a) is true then (b) must be true too. If it is true, i.e. if p does entail q, We express this by saying that (a) entails (b), in symbols then we say it is a valid sequent, NC is very clever. |= NC is clever. or for short an entailment. 3 4 1.5 Typical problem Imagine a situation with two Noam Chomskys, 1.4 Provisional definition Logic is the study of entailments. Butit’sabadideatogotoadefinition so early. one very clever and one very unintelligent. 5 6 1.7 1.6 Remedy Conversation: A situation (for a set of sentences) consists of the information needed to fix who the named people are, what the date is, • “Noam Chomsky is very clever.” and anything else relevant to the truth of the sentences. • “Yes, but on the other hand Noam Chomsky Then we can revise our notion of entailment: is not clever at all.” ‘(a) entails (b)’ means We can understand this conversation. In every situation, if (a) is true then (b) is true. (David Lewis, ‘Scorekeeping in a language game’.) This remedy is not waterproof, but it will allow us to continue building up logic.
    [Show full text]
  • A Mechanised Proof of Gödel's Incompleteness Theorems Using
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Apollo A Mechanised Proof of G¨odel'sIncompleteness Theorems using Nominal Isabelle Lawrence C. Paulson Abstract An Isabelle/HOL formalisation of G¨odel'stwo incompleteness theorems is presented. The work follows Swierczkowski's´ detailed proof of the theorems using hered- itarily finite (HF) set theory [32]. Avoiding the usual arithmetical encodings of syntax eliminates the necessity to formalise elementary number theory within an embedded logical calculus. The Isabelle formalisation uses two separate treatments of variable binding: the nominal package [34] is shown to scale to a development of this complex- ity, while de Bruijn indices [3] turn out to be ideal for coding syntax. Critical details of the Isabelle proof are described, in particular gaps and errors found in the literature. 1 Introduction This paper describes mechanised proofs of G¨odel'sincompleteness theorems [8], includ- ing the first mechanised proof of the second incompleteness theorem. Very informally, these results can be stated as follows: Theorem 1 (First Incompleteness Theorem) If L is a consistent theory capable of formalising a sufficient amount of elementary mathematics, then there is a sentence δ such that neither δ nor :δ is a theorem of L, and moreover, δ is true.1 Theorem 2 (Second Incompleteness Theorem) If L is as above and Con(L) is a sentence stating that L is consistent, then Con(L) is not a theorem of L. Both of these will be presented formally below. Let us start to examine what these theorems actually assert.
    [Show full text]
  • Satisfiability Calculus: an Abstract Formulation of Semantic Proof Systems
    Fundamenta Informaticae XX (2019) 1–50 1 DOI 10.3233/FI-2016-0000 IOS Press Satisfiability Calculus: An Abstract Formulation of Semantic Proof Systems Carlos G. Lopez Pombo∗ Pablo F. Castroy CONICET-Universidad de Buenos Aires. CONICET- Department of Computer Science Instituto de Investigacion´ en Ciencias de la Universidad Nacional de R´ıo Cuarto Computacion´ (ICC) R´ıo Cuarto, Argentina [email protected] [email protected] Nazareno M. Aguirrez Thomas S.E. Maibaumx CONICET- Department of Computer Science Department of Computing & Software Universidad Nacional de R´ıo Cuarto McMaster University, Canada R´ıo Cuarto, Argentina [email protected] [email protected] Abstract. The theory of institutions, introduced by Goguen and Burstall in 1984, can be thought of as an abstract formulation of model theory. This theory has been shown to be particularly useful in computer science, as a mathematical foundation for formal approaches to software con- struction. Institution theory was extended by a number of researchers, Jose´ Meseguer among them, who, in 1989, presented General Logics, wherein the model theoretical view of institutions Address for correspondence: Department of Computer Science, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina ∗Carlos G. Lopez Pombo wants to thank the founding from Agencia Nacional de Promocion´ Cient´ıfica y Tecnologica´ (ANPCyT) through grant PICT 2013 No. 2129, from Universidad de Buenos Aires through grant UBACyT 2014-2017 No. 20020130200092BA and from Consejo Nacional de Investigaciones Cient´ıficas y Tecnologicas´ (CONICET) through grant PIP 2014-2016 No. 11220130100148CO. yPablo Castro wants to thank the founding from Agencia Nacional de Promocion´ Cient´ıfica y Tecnologica´ (ANPCyT) through grant PICT 2013 No.
    [Show full text]
  • Constructive Completeness Proofs and Delimited Control Danko Ilik
    Constructive Completeness Proofs and Delimited Control Danko Ilik To cite this version: Danko Ilik. Constructive Completeness Proofs and Delimited Control. Software Engineering [cs.SE]. Ecole Polytechnique X, 2010. English. tel-00529021 HAL Id: tel-00529021 https://pastel.archives-ouvertes.fr/tel-00529021 Submitted on 24 Oct 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Thèse présentée pour obtenir le grade de Docteur de l’Ecole Polytechnique Spécialité : Informatique par Danko ILIK´ Titre de la thèse : Preuves constructives de complétude et contrôle délimité Soutenue le 22 octobre 2010 devant le jury composé de : M. Hugo HERBELIN Directeur de thèse M. Ulrich BERGER Rapporteur M. Thierry COQUAND Rapporteur M. Olivier DANVY Rapporteur M. Gilles DOWEK Examinateur M. Paul-André MELLIÈS Examinateur M. Alexandre MIQUEL Examinateur M. Wim VELDMAN Examinateur Constructive Completeness Proofs and Delimited Control Danko ILIK´ ABSTRACT. Motivated by facilitating reasoning with logical meta-theory in- side the Coq proof assistant, we investigate the constructive versions of some completeness theorems. We start by analysing the proofs of Krivine and Berardi-Valentini, that classical logic is constructively complete with respect to (relaxed) Boolean models, and the algorithm behind the proof.
    [Show full text]
  • HISTORY of LOGICAL CONSEQUENCE 1. Introduction
    HISTORY OF LOGICAL CONSEQUENCE CONRAD ASMUS AND GREG RESTALL 1. Introduction Consequence is a, if not the, core subject matter of logic. Aristotle's study of the syllogism instigated the task of categorising arguments into the logically good and the logically bad; the task remains an essential element of the study of logic. In a logically good argument, the conclusion follows validly from the premises; thus, the study of consequence and the study of validity are the same. In what follows, we will engage with a variety of approaches to consequence. The following neutral framework will enhance the discussion of this wide range of approaches. Consequences are conclusions of valid arguments. Arguments have two parts: a conclusion and a collection of premises. The conclusion and the premises are all entities of the same sort. We will call the conclusion and premises of an argument the argument's components and will refer to anything that can be an argument component as a proposition. The class of propositions is defined func- tionally (they are the entities which play the functional role of argument compo- nents); thus, the label should be interpreted as metaphysically neutral. Given the platonistic baggage often associated with the label \proposition", this may seem a strange choice but the label is already used for the argument components of many of the approaches below (discussions of Aristotlean and Medieval logic are two ex- amples). A consequence relation is a relation between collections of premises and conclusions; a collection of premises is related to a conclusion if and only if the latter is a consequence of the former.
    [Show full text]