Mathematical (Formal) Logic and Mathematical Theories
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The Deduction Rule and Linear and Near-Linear Proof Simulations
The Deduction Rule and Linear and Near-linear Proof Simulations Maria Luisa Bonet¤ Department of Mathematics University of California, San Diego Samuel R. Buss¤ Department of Mathematics University of California, San Diego Abstract We introduce new proof systems for propositional logic, simple deduction Frege systems, general deduction Frege systems and nested deduction Frege systems, which augment Frege systems with variants of the deduction rule. We give upper bounds on the lengths of proofs in Frege proof systems compared to lengths in these new systems. As applications we give near-linear simulations of the propositional Gentzen sequent calculus and the natural deduction calculus by Frege proofs. The length of a proof is the number of lines (or formulas) in the proof. A general deduction Frege proof system provides at most quadratic speedup over Frege proof systems. A nested deduction Frege proof system provides at most a nearly linear speedup over Frege system where by \nearly linear" is meant the ratio of proof lengths is O(®(n)) where ® is the inverse Ackermann function. A nested deduction Frege system can linearly simulate the propositional sequent calculus, the tree-like general deduction Frege calculus, and the natural deduction calculus. Hence a Frege proof system can simulate all those proof systems with proof lengths bounded by O(n ¢ ®(n)). Also we show ¤Supported in part by NSF Grant DMS-8902480. 1 that a Frege proof of n lines can be transformed into a tree-like Frege proof of O(n log n) lines and of height O(log n). As a corollary of this fact we can prove that natural deduction and sequent calculus tree-like systems simulate Frege systems with proof lengths bounded by O(n log n). -
Chapter 9: Initial Theorems About Axiom System
Initial Theorems about Axiom 9 System AS1 1. Theorems in Axiom Systems versus Theorems about Axiom Systems ..................................2 2. Proofs about Axiom Systems ................................................................................................3 3. Initial Examples of Proofs in the Metalanguage about AS1 ..................................................4 4. The Deduction Theorem.......................................................................................................7 5. Using Mathematical Induction to do Proofs about Derivations .............................................8 6. Setting up the Proof of the Deduction Theorem.....................................................................9 7. Informal Proof of the Deduction Theorem..........................................................................10 8. The Lemmas Supporting the Deduction Theorem................................................................11 9. Rules R1 and R2 are Required for any DT-MP-Logic........................................................12 10. The Converse of the Deduction Theorem and Modus Ponens .............................................14 11. Some General Theorems About ......................................................................................15 12. Further Theorems About AS1.............................................................................................16 13. Appendix: Summary of Theorems about AS1.....................................................................18 2 Hardegree, -
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. -
5 Deduction in First-Order Logic
5 Deduction in First-Order Logic The system FOLC. Let C be a set of constant symbols. FOLC is a system of deduction for # the language LC . Axioms: The following are axioms of FOLC. (1) All tautologies. (2) Identity Axioms: (a) t = t for all terms t; (b) t1 = t2 ! (A(x; t1) ! A(x; t2)) for all terms t1 and t2, all variables x, and all formulas A such that there is no variable y occurring in t1 or t2 such that there is free occurrence of x in A in a subformula of A of the form 8yB. (3) Quantifier Axioms: 8xA ! A(x; t) for all formulas A, variables x, and terms t such that there is no variable y occurring in t such that there is a free occurrence of x in A in a subformula of A of the form 8yB. Rules of Inference: A; (A ! B) Modus Ponens (MP) B (A ! B) Quantifier Rule (QR) (A ! 8xB) provided the variable x does not occur free in A. Discussion of the axioms and rules. (1) We would have gotten an equivalent system of deduction if instead of taking all tautologies as axioms we had taken as axioms all instances (in # LC ) of the three schemas on page 16. All instances of these schemas are tautologies, so the change would have not have increased what we could 52 deduce. In the other direction, we can apply the proof of the Completeness Theorem for SL by thinking of all sententially atomic formulas as sentence # letters. The proof so construed shows that every tautology in LC is deducible using MP and schemas (1){(3). -
Knowing-How and the Deduction Theorem
Knowing-How and the Deduction Theorem Vladimir Krupski ∗1 and Andrei Rodin y2 1Department of Mechanics and Mathematics, Moscow State University 2Institute of Philosophy, Russian Academy of Sciences and Department of Liberal Arts and Sciences, Saint-Petersburg State University July 25, 2017 Abstract: In his seminal address delivered in 1945 to the Royal Society Gilbert Ryle considers a special case of knowing-how, viz., knowing how to reason according to logical rules. He argues that knowing how to use logical rules cannot be reduced to a propositional knowledge. We evaluate this argument in the context of two different types of formal systems capable to represent knowledge and support logical reasoning: Hilbert-style systems, which mainly rely on axioms, and Gentzen-style systems, which mainly rely on rules. We build a canonical syntactic translation between appropriate classes of such systems and demonstrate the crucial role of Deduction Theorem in this construction. This analysis suggests that one’s knowledge of axioms and one’s knowledge of rules ∗[email protected] [email protected] ; the author thanks Jean Paul Van Bendegem, Bart Van Kerkhove, Brendan Larvor, Juha Raikka and Colin Rittberg for their valuable comments and discussions and the Russian Foundation for Basic Research for a financial support (research grant 16-03-00364). 1 under appropriate conditions are also mutually translatable. However our further analysis shows that the epistemic status of logical knowing- how ultimately depends on one’s conception of logical consequence: if one construes the logical consequence after Tarski in model-theoretic terms then the reduction of knowing-how to knowing-that is in a certain sense possible but if one thinks about the logical consequence after Prawitz in proof-theoretic terms then the logical knowledge- how gets an independent status. -
An Integration of Resolution and Natural Deduction Theorem Proving
From: AAAI-86 Proceedings. Copyright ©1986, AAAI (www.aaai.org). All rights reserved. An Integration of Resolution and Natural Deduction Theorem Proving Dale Miller and Amy Felty Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 Abstract: We present a high-level approach to the integra- ble of translating between them. In order to achieve this goal, tion of such different theorem proving technologies as resolution we have designed a programming language which permits proof and natural deduction. This system represents natural deduc- structures as values and types. This approach builds on and ex- tion proofs as X-terms and resolution refutations as the types of tends the LCF approach to natural deduction theorem provers such X-terms. These type structures, called ezpansion trees, are by replacing the LCF notion of a uakfation with explicit term essentially formulas in which substitution terms are attached to representation of proofs. The terms which represent proofs are quantifiers. As such, this approach to proofs and their types ex- given types which generalize the formulas-as-type notion found tends the formulas-as-type notion found in proof theory. The in proof theory [Howard, 19691. Resolution refutations are seen LCF notion of tactics and tacticals can also be extended to in- aa specifying the type of a natural deduction proofs. This high corporate proofs as typed X-terms. Such extended tacticals can level view of proofs as typed terms can be easily combined with be used to program different interactive and automatic natural more standard aspects of LCF to yield the integration for which Explicit representation of proofs deduction theorem provers. -
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. -
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. -
CHAPTER 8 Hilbert Proof Systems, Formal Proofs, Deduction Theorem
CHAPTER 8 Hilbert Proof Systems, Formal Proofs, Deduction Theorem The Hilbert proof systems are systems based on a language with implication and contain a Modus Ponens rule as a rule of inference. They are usually called Hilbert style formalizations. We will call them here Hilbert style proof systems, or Hilbert systems, for short. Modus Ponens is probably the oldest of all known rules of inference as it was already known to the Stoics (3rd century B.C.). It is also considered as the most "natural" to our intuitive thinking and the proof systems containing it as the inference rule play a special role in logic. The Hilbert proof systems put major emphasis on logical axioms, keeping the rules of inference to minimum, often in propositional case, admitting only Modus Ponens, as the sole inference rule. 1 Hilbert System H1 Hilbert proof system H1 is a simple proof system based on a language with implication as the only connective, with two axioms (axiom schemas) which characterize the implication, and with Modus Ponens as a sole rule of inference. We de¯ne H1 as follows. H1 = ( Lf)g; F fA1;A2g MP ) (1) where A1;A2 are axioms of the system, MP is its rule of inference, called Modus Ponens, de¯ned as follows: A1 (A ) (B ) A)); A2 ((A ) (B ) C)) ) ((A ) B) ) (A ) C))); MP A ;(A ) B) (MP ) ; B 1 and A; B; C are any formulas of the propositional language Lf)g. Finding formal proofs in this system requires some ingenuity. Let's construct, as an example, the formal proof of such a simple formula as A ) A. -
Propositional Logic: Part II - Syntax & Proofs
Propositional Logic: Part II - Syntax & Proofs 0-0 Outline ² Syntax of Propositional Formulas ² Motivating Proofs ² Syntactic Entailment ` and Proofs ² Proof Rules for Natural Deduction ² Axioms, theories and theorems ² Consistency & completeness 1 Language of Propositional Calculus Def: A propositional formula is constructed inductively from the symbols for ² propositional variables: p; q; r; : : : or p1; p2; : : : ² connectives: :; ^; _; !; $ ² parentheses: (; ) ² constants: >; ? by the following rules: 1. A propositional variable or constant symbol (>; ?) is a formula. 2. If Á and à are formulas, then so are: (:Á); (Á ^ Ã); (Á _ Ã); (Á ! Ã); (Á $ Ã) Note: Removing >; ? from the above provides the def. of sentential formulas in Rubin. Semantically > = T; 2 ? = F though often T; F are used directly in formulas when engineers abuse notation. 3 WFF Smackdown Propositional formulas are also called propositional sentences or well formed formulas (WFF). When we use precedence of logical connectives and associativity of ^; _; $ to drop (smackdown!) parentheses it is understood that this is shorthand for the fully parenthesized expressions. Note: To further reduce use of (; ) some def's of formula use order of precedence: ! ^ ! :; ^; _; instead of :; ; $ _ $ As we will see, PVS uses the 1st order of precedence. 4 BNF and Parse Trees The Backus Naur form (BNF) for the de¯nition of a propositional formula is: Á ::= pj?j>j(:Á)j(Á ^ Á)j(Á _ Á)j(Á ! Á) Here p denotes any propositional variable and each occurrence of Á to the right of ::= represents any formula constructed thus far. We can apply this inductive de¯nition in reverse to construct a formula's parse tree. -
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. -
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 .