Stable Harmony. Nils Kurbis

Similar documents
Propositional Logic. Part I

STABILITY AND PARADOX IN ALGORITHMIC LOGIC

What if current foundations of mathematics are inconsistent? Vladimir Voevodsky September 25, 2010

1.3. Conditional expressions To express case distinctions like

Matching Algorithms. Proof. If a bipartite graph has a perfect matching, then it is easy to see that the right hand side is a necessary condition.

THE FOUNDATIONS OF MATHEMATICS

CSL105: Discrete Mathematical Structures. Ragesh Jaiswal, CSE, IIT Delhi

MC 302 GRAPH THEORY 10/1/13 Solutions to HW #2 50 points + 6 XC points

Lecture 5. Logic I. Statement Logic

Typed Lambda Calculus

Typed Lambda Calculus for Syntacticians

XI International PhD Workshop OWD 2009, October Fuzzy Sets as Metasets

CSC 501 Semantics of Programming Languages

1. Suppose you are given a magic black box that somehow answers the following decision problem in polynomial time:

NP-Hardness. We start by defining types of problem, and then move on to defining the polynomial-time reductions.

3.4 Deduction and Evaluation: Tools Conditional-Equational Logic

2 Introduction to operational semantics

Formal Methods of Software Design, Eric Hehner, segment 1 page 1 out of 5

Elementary Recursive Function Theory

arxiv: v1 [math.ho] 7 Nov 2017

8 NP-complete problem Hard problems: demo

Discrete Optimization. Lecture Notes 2

Handout 9: Imperative Programs and State

The three faces of homotopy type theory. Type theory and category theory. Minicourse plan. Typing judgments. Michael Shulman.

Chapter 3: Propositional Languages

3.7 Denotational Semantics

Chapter 3. Set Theory. 3.1 What is a Set?

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages

Propositional Theories are Strongly Equivalent to Logic Programs

FUZZY BOOLEAN ALGEBRAS AND LUKASIEWICZ LOGIC. Angel Garrido

The Resolution Algorithm

Topic 3: Propositions as types

Finding Strongly Connected Components

Part I Logic programming paradigm

Chapter 8. NP-complete problems

Mathematically Rigorous Software Design Review of mathematical prerequisites

1 The Axiom of Extensionality

The Encoding Complexity of Network Coding

On the Relationships between Zero Forcing Numbers and Certain Graph Coverings

Winning Positions in Simplicial Nim

Logic and its Applications

Mathematical Logic Prof. Arindama Singh Department of Mathematics Indian Institute of Technology, Madras. Lecture - 37 Resolution Rules

Reasoning With Characteristic Models

Lecture slides & distribution files:

1. Logic as a subject matter is the study of logics, understood as models of acceptable inference patterns

Workbook Unit 13: Natural Deduction Proofs (IV)

8. Relational Calculus (Part II)

Module 7. Independent sets, coverings. and matchings. Contents

INDEPENDENT POSTULATES FOR THE "INFORMAL" PART OF PRINCIPIA MATHEMATICA*

CSCI 5454, CU Boulder Samriti Kanwar Lecture April 2013

Software Engineering Lecture Notes

Proofs are Programs. Prof. Clarkson Fall Today s music: Proof by Paul Simon

Constructive Coherent Translation of Propositional Logic

Polynomial SAT-Solver Algorithm Explanation

Small Survey on Perfect Graphs

A step towards the Bermond-Thomassen conjecture about disjoint cycles in digraphs

Matching Theory. Figure 1: Is this graph bipartite?

CHECKING PROOFS IN THE METAMATHEMATICS OF FIRST ORDER LOGIC

Formal Semantics in Modern Type Theories: Is It Model-Theoretic, Proof-Theoretic or Both?

CS161 Handout 07 Summer 2013 July 17, 2013 Guide to Reductions. Thanks to Julie Tibshirani for helping with this handout!

Basic Elements of Computer Algebra in MIZAR

The 4/5 Upper Bound on the Game Total Domination Number

Ch9: Exact Inference: Variable Elimination. Shimi Salant, Barak Sternberg

Distributed minimum spanning tree problem

Common Knowledge in Exchanges

A Reduction of Conway s Thrackle Conjecture

Linear Clause Generation by Tableaux and DAGs

Binary Decision Diagrams

The Mathematical Vernacular

Fundamental mathematical techniques reviewed: Mathematical induction Recursion. Typically taught in courses such as Calculus and Discrete Mathematics.

A Transformational Characterization of Markov Equivalence for Directed Maximal Ancestral Graphs

CS Bootcamp Boolean Logic Autumn 2015 A B A B T T T T F F F T F F F F T T T T F T F T T F F F

The Resolution Width Problem is EXPTIME-Complete

CLAW-FREE 3-CONNECTED P 11 -FREE GRAPHS ARE HAMILTONIAN

The discussion of Chapter 1 will be split into two sessions; the first will cover 1.1 and 1.2; the second will cover 1.3, 1.4, and 1.5.

Revisiting Kalmar completeness metaproof

CSCI.6962/4962 Software Verification Fundamental Proof Methods in Computer Science (Arkoudas and Musser) Sections p.

/633 Introduction to Algorithms Lecturer: Michael Dinitz Topic: Approximation algorithms Date: 11/27/18

Tidying up the Mess around the Subsumption Theorem in Inductive Logic Programming Shan-Hwei Nienhuys-Cheng Ronald de Wolf bidewolf

HANDBOOK OF LOGIC IN ARTIFICIAL INTELLIGENCE AND LOGIC PROGRAMMING

An Improved Upper Bound for the Sum-free Subset Constant

Semantic Forcing in Disjunctive Logic Programs

On the Efficiency of Negligence Rule

Vertex Deletion games with Parity rules

Propositional Logic Formal Syntax and Semantics. Computability and Logic

Module 6. Knowledge Representation and Logic (First Order Logic) Version 2 CSE IIT, Kharagpur

(a) (4 pts) Prove that if a and b are rational, then ab is rational. Since a and b are rational they can be written as the ratio of integers a 1

ROUGH MEMBERSHIP FUNCTIONS: A TOOL FOR REASONING WITH UNCERTAINTY

Towards a Logical Reconstruction of Relational Database Theory

GRAPHS WITH 1-FACTORS

Resolution (14A) Young W. Lim 6/14/14

The External Network Problem

These notes present some properties of chordal graphs, a set of undirected graphs that are important for undirected graphical models.

PROPOSITIONAL LOGIC (2)

CS 3EA3: Sheet 9 Optional Assignment - The Importance of Algebraic Properties

The Inverse of a Schema Mapping

Theoretical Computer Science

Logical Verification Course Notes. Femke van Raamsdonk Vrije Universiteit Amsterdam

Lecture 2: NP-Completeness

Crown-free highly arc-transitive digraphs

Transcription:

Stable Harmony Nils Kurbis 1. Gentzen s Thesis and Two Notions of Harmony Gentzen observed that there is a remarkable systematic in the inference patterns for symbols of the calculus of natural deduction. This lead him to put forward what might be called Gentzen s Thesis: The introductions constitute, so to speak, the definitions of the symbols concerned, and the eliminations are in the end only consequences thereof, which could be expressed thus: In the elimination of a symbol, the formula in question, whose outer symbol it concerns, may only be used as that which it means on the basis of the introduction of this symbol. 1 Gentzen s Thesis invites fleshing out in a comprehensive theory, which is what Michael Dummett and Dag Prawitz aim to provide with their prooftheoretic justification of deduction. 2 They employ the following a version of Gentzen s Thesis: The meaning of a logical constant may be defined by its introduction and elimination rules if they are in harmony. What is harmony? Two explanations of this notion can be extracted from the literature which lead to two different definitions: 1.) Harmony is a feature detectable in the rules governing a logical constant: Introduction and elimination rules for a constant are in harmony if the latter can somehow be read off the former. 2.) Harmony holds if the rules for a constant yield a suitable induction clause for a normalisation theorem: Introduction and elimination rules for a constant are in harmony if maximal formulas may be removed from deductions. 1 Gentzen (1935), p.189. 2 See Dummett (1993) and Prawitz (1965). 1

Notice the difference between these two notions of harmony: Harmony 1 is a feature of rules independently of a logic they are part of. It is a common feature all rules of a certain kind are supposed to exhibit. It is to do with a special uniformity in the form of these rules. Contrary to that, harmony 2 is a feature rules can only have relative to a logic they are part of. There is no suggestion that it is a common feature of all rules which occur in logics which normalise. An important relation seems to hold between the two notions of harmony: It has not been spelled out what harmony 1 consists in, what it means to read off introduction from elemination rules and vice versa. But it is perfectly clear what harmony 2 is. Thus harmony 2 suggests itself as a formally precise replacement of harmony 1. 2. The Problem with Stability I have oversimplified Dummett s and Prawitz version of Gentzen s Thesis. It should read: The meaning of a logical constant may be defined by introduction and elimination rules if they are stable. Stability is explained in terms of harmony 1 : Stability holds if harmony 1 holds and the introduction rules for a constant can somehow be read off its elimination rules. 3 This creates a problem: It is stability, hence harmony 1, which is crucial to Dummett s and Prawitz programme. But the notion of reading off introduction from elimination rules and vice versa has not been made precise. Rather, as noted at the very end of the last section, if anything, harmony 1 has been replaced by harmony 2. Thus we have no formally precise explanation of stability, and hence no formally precise foundation for Dummett s and Prawitz proof-theoretic justification of deduction! 3. Making Harmony 1 Precise The aim of the rest of this paper is to meet the challenge the last section poses and to define a formally precise notion of harmony 1. Gentzen provides the clue to the answer I shall propose: By making these thoughts [about the remarkable systematic in the inference patterns] more precise it should be possible to establish on the basis of certain requirements that the elimination rules are functions of the corresponding introduction rules. 4 My strategy is 3 Cf. Dummett (1993), p.287. 4 Gentzen (1935), p.189. 2

to specify the function Gentzen speaks of. Gentzen does not give the function which maps introduction to elimination rules and to my knowledge neither does anyone else. This is remarkable, given there seems to exist a certain consensus amongst workers in the field of how to read off elimination rules from introduction rules. 5 Dummett tries to capture some of Gentzen s idea with his notions of harmony and stability, but what he has to say on that issue is a long way from the envisaged mathematical precision of Gentzen s remark. Previous discussions have, to my knowledge, always assumed that there is one kind of rule of inference and then the meanings of the connectives are given either uniformly by introduction rules or by elimination rules, the others being determined relative to them by some principle of harmony. Maybe this is why the function Gentzen speaks of has not been found. In contrast, I shall give two kinds of general forms of rules of inference, one where an introduction rule for a connective is given and one where an elimination rule is given, and I shall specify for each kind a general method of reading off the elimination and introduction rules for the connective in such a way that the rules may be said to be in harmony. Moreover, the process can be reversed, which corresponds to Dummett s notion of stability. I shall, however, define a notion of stability which is different from Dummett s, although arguably it captures what Dummett intended to capture with his notion. Although I won t go into any further details, let me note here that there are also general forms of reduction procedures for removing maximal formulas and any logic with only connectives governed by stable rules of either of the two types fulfils the requirements of the proof-theoretic justification of deduction, in particular, deductions normalise. Logics containing only connectives governed by rules of either of the two types may be called quasiintuitionist. The results can be extended to cover also quasi-classical logics by allowing rules of another type, namely versions of the negation rule consequentia mirabilis. It is interesting that proving normalisation theorems for quasi-classical logics, which I can demonstrate for classical and relevance logic, cannot proceed in as general a fashion as in the case of quasi-intuitionist logics. 6 5 As demonstrated by, for instance, Dummett (1993), p.294ff. Zucker and Tragesser have to admit that their method for determining elimination rules from introduction rules allows there to be cases where a set of introduction rules is given but there does not seem to be a suitable set of [elimination] rules ((1978), p.506). 6 All this is covered in my Ph.D. thesis. 3

4. Formal Preliminaries I shall presuppose familiarity with the framework of substructural logics and restrict myself here to explaining what is idiosyncratic to my formalism. Lists are collections of formulas structured by punctuation marks. For instance, ((A, B); C), (A; B) is a list. Workers in the field in general restrict consideration to two kinds of punctuation marks, as e.g. in some substructural formulations of the relevant logic R. I allow logics to have any number of punctuation marks. Θ(X 1... X n ) is used to designate a list with immediate sublists X 1... X n which are combined in a specific way by punctuation marks. Θ designates the structure of the list, which may be represented by using variables ranging over lists. For example, let Θ be (ξ 1 ; ξ 2 ), ξ 3. Then Θ((A, B) C (D; B)) is the result of replacing ξ 1 by (A, B), ξ 2 by C and ξ 3 by (D; B), i.e. (((A, B); C), (D; B)). The following makes no mentioning of structural rules that allow the replacement of lists with other lists in the antecedents of consecutions X A. Only the general structure of lists is taken into account. 5. The General Forms of Rules of Inference I shall now give the general forms of two kinds of rules of inference and the function that maps introduction/elimination rules to elimination/introduction rules. 5.a The General Form of Rules of Type One If a connective Ξ is governed by rules of type one, it has one introduction rule of the form: Φ(X A 1... A h ) B 1... Ψ(X A k... A l ) B p X Ξ x A 1... A l B 1... B p where there are no formulas on X containing the variables on the sequence x = x 1... x v free. To be governed by harmonious rules of inference, Ξ is required to have p elimination rules, i.e. one for each premises and each of the form: 4

Z Ξ x A 1... A l B 1... B p Y i A i [x/t]... Y j A j [x/t] Θ(Z Y i... Y j ) B o [x/t] where t is free for x. I leave it to the reader to figure out how the process of reading off the elimination rules for Ξ from its introduction rule can be reversed so that the introduction rule is read off the collection of elimination rules. Examples of Rules of Type One 5.a.i Verum is governed by an introduction rule with no premises: X Hence has no elimination rule. 5.a.ii Conjunction is governed by an introduction rule with two premises and no discharged hypotheses: X A X B X A B Hence has two elimination rules without minor premises: X A B X A X A B X B 5

5.a.iii Universal Quantification x is governed by an introduction rule with one main premises and no discharged hypotheses, where x does not occur free in any formula on X: X F x X xf x Hence x has one elimination rule, where t is free for x in F x: 5.a.iv Implication Y xf x Y F t is governed by an introduction rule with one premise and one discharged hypothesis: X, A B X A B Hence it has one elimination rule with one minor premise: Y A B Z A Y, Z B Whether is material or relevant depends on the structural rules that hold for the comma in the logic. 6

5.a.v Biconditional is governed by an introduction rule with two premises and one discharged hypothesis for each premise: X, A B X, B A X A B Hence it has two elimination rules, each with one minor premise: X A B Y A X, Y B X A B Y B X, Y A Whether is material or relevant depends on the structural rules that hold for the comma in the logic. 5.b The General Form of Rules of Type Two If a connective Ξ is governed by rules of type two, it has one elimination rule of the form: Z Ξ x D 1... D n Y (Φ(D 1... D i )) E... Y (Ψ(D l... D n )) E Y (Z) E where none of the variables on the sequence x = x 1... x v is free in E and any formula on Y. To be governed by harmonious rules of inference, Ξ is required to have p introduction rules, where p is the number of minor premises, one for each, and each of the form: X j D j [x / t ]... X k D k [x / t ] Θ(X j... X k ) Ξ x D 1... D n where t is free for x I leave it to the reader to figure out how the process of reading off the introduction rules for Ξ from its elimination rule can be reversed so that the elimination rule is read off the collection of introduction rules. 7

Examples of Rules of Type Two 5.b.i Falsum is governed by an elimination rule without minor premises: X Y (X) A Hence has no introduction rule. 5.b.ii Existential Quantification x is governed by an elimination rule with one minor premise and one discharged hypothesis, where y is not free in C and any formula on Y : X xf x Y (F x) C Y (X) C Hence it has one introduction rule, where t is free for x in F x: Z F t Z xf x 5.b.iii Truth Constant t for the Empty List t is governed by an elimination rule with one minor premise where the empty list 0 occurs in the place of a discharged assumption: X t Y (0) C Y (X) C Hence t has one introduction rule with no premises: 0 t 8

5.b.iv Fusion The constant is governed by an elimination rule with one minor premise and two discharged hypotheses: X A B Y (A; B) C Y (X) C Hence it has one introduction rule with two premises: 5.b.v Disjunction X A Y B X; Y A B The constant is governed by an elimination rule with two minor premises, each with one discharged assumption: X A B Y (A) C Y (B) C Y (X) C Hence has two introduction rules with one premise each: X A X A B X B X A B 6. Formally Precise Definitions of Harmony and Stability We can now give a formally precise definition of harmony: introduction and elimination rules for a constant Ξ are in harmony or a logical constant Ξ is governed by harmonious rules iff either (i) Ξ is governed by an introduction rule of type one and elimination rules determined from it by the method of section 5.a, or (ii) Ξ is governed by an elimination rule of type two and introduction rules determined from it by the method of section 5.b. This definition allows restrictions on the lists occurring in the rules such as the restrictions on the elimination rule for quantum disjunction or the 9

introduction rule for S4-necessity. Let s call such restrictions list restrictions. List restrictions do not affect whether the rules are of a specific form, hence do not affect harmoniousness. However, we can exclude rules with list restrictions from being stable by adopting the following definition: introduction and elimination rules for a constant Ξ are stable or a logical constant Ξ is governed by stable rules iff the rules for Ξ are harmonious and without lists restrictions. Notice that these definitions sever the ties between harmony and normalisation that is prevalent in the literature. It is not the case that deductions in any logic containing only constants governed by harmonious rules normalise. For instance, this is not the case for deductions in a logic containing only implication and S4-necessity. However, if all rules are stable, then normalisation is guaranteed. Department of Philosophy University College London Gower Street London WC1E 6BT email: n.kurbis@ucl.ac.uk References: Dummett, M. (1993). The Logical Basis of Metaphysics. Cambridge, Mass.: Harvard University Press. Gentzen, G. (1935). Untersuchungen über das logische Schliessen. I. Mathematische Zeitschrift 39, 176-210, 405-431. Prawitz, D. (1965). Natural Deduction. A Proof Theoretical Study. (Acta Universitatis Stockholmiensis, Stockholm Studies in Philosophy 3). Stockholm, Gteborg, Uppsala: Almquist & Wiksell. Zucker & Tragesser (1978). The Adequacy Problem for Inferential Logic. Journal of Philosophical Logic 7, 501-516. 10