Propositional Calculus - Soundness & Completeness of H

Similar documents
Propositional Calculus - Deductive Systems

Propositional Calculus - Natural deduction Moonzoo Kim CS Dept. KAIST

Propositional Calculus - Hilbert system H Moonzoo Kim CS Dept. KAIST

Propositional Calculus - Hilbert system H Moonzoo Kim CS Division of EECS Dept. KAIST

Propositional Calculus - Semantics (3/3) Moonzoo Kim CS Dept. KAIST

Predicate Calculus - Semantic Tableau (2/2) Moonzoo Kim CS Division of EECS Dept. KAIST

Chapter 3: Propositional Calculus: Deductive Systems. September 19, 2008

Propositional Logic: Deductive Proof & Natural Deduction Part 1

02 Propositional Logic

Deductive Systems. Lecture - 3

Propositional Logic: Gentzen System, G

3 Propositional Logic

Predicate Calculus - Semantics 1/4

Chapter 11: Automated Proof Systems

Propositional Logic: Part II - Syntax & Proofs 0-0

Chapter 11: Automated Proof Systems (1)

Overview. I Review of natural deduction. I Soundness and completeness. I Semantics of propositional formulas. I Soundness proof. I Completeness proof.

Propositional Logic Language

Applied Logic. Lecture 1 - Propositional logic. Marcin Szczuka. Institute of Informatics, The University of Warsaw

Computational Logic. Davide Martinenghi. Spring Free University of Bozen-Bolzano. Computational Logic Davide Martinenghi (1/30)

Part 1: Propositional Logic

Propositional and Predicate Logic - IV

Natural Deduction for Propositional Logic

Fundamentals of Logic

Predicate Logic - Deductive Systems

Classical Propositional Logic

Learning Goals of CS245 Logic and Computation

Advanced Topics in LP and FP

1. Propositional Calculus

Propositional and Predicate Logic - V

CHAPTER 10. Gentzen Style Proof Systems for Classical Logic

Temporal Logic - Soundness and Completeness of L

Natural Deduction. Formal Methods in Verification of Computer Systems Jeremy Johnson

On the Complexity of the Reflected Logic of Proofs

Version January Please send comments and corrections to

Přednáška 12. Důkazové kalkuly Kalkul Hilbertova typu. 11/29/2006 Hilbertův kalkul 1

A Resolution Method for Modal Logic S5

Systems of modal logic

Part 1: Propositional Logic

Propositional Resolution

Equivalents of Mingle and Positive Paradox

Modal and temporal logic

Automated Support for the Investigation of Paraconsistent and Other Logics

1. Propositional Calculus

Propositional Resolution Introduction

Propositional Resolution Part 1. Short Review Professor Anita Wasilewska CSE 352 Artificial Intelligence

AN EXTENSION OF THE PROBABILITY LOGIC LP P 2. Tatjana Stojanović 1, Ana Kaplarević-Mališić 1 and Zoran Ognjanović 2

A Weak Post s Theorem and the Deduction Theorem Retold

Introduction to Metalogic

Hypersequent Calculi for some Intermediate Logics with Bounded Kripke Models

Classical Propositional Logic

COMP219: Artificial Intelligence. Lecture 19: Logic for KR

Lecture Notes on Sequent Calculus

COMP219: Artificial Intelligence. Lecture 19: Logic for KR

Formal (natural) deduction in propositional logic

Semantics and Pragmatics of NLP

Intelligent Systems. Propositional Logic. Dieter Fensel and Dumitru Roman. Copyright 2008 STI INNSBRUCK

First-Order Logic First-Order Theories. Roopsha Samanta. Partly based on slides by Aaron Bradley and Isil Dillig

KE/Tableaux. What is it for?

The Syntax of First-Order Logic. Marc Hoyois

Introduction to Logic in Computer Science: Autumn 2006

Propositional and Predicate Logic. jean/gbooks/logic.html

Gödel s Completeness Theorem

2. The Logic of Compound Statements Summary. Aaron Tan August 2017

Overview. Knowledge-Based Agents. Introduction. COMP219: Artificial Intelligence. Lecture 19: Logic for KR

Mathematical Logic Propositional Logic - Tableaux*

SE 212: Logic and Computation. se212 Nancy Day

Propositional Dynamic Logic

Semantics for Propositional Logic

PHI 120 Introductory Logic Consistency of the Calculus

Linear Logic Pages. Yves Lafont (last correction in 2017)

03 Propositional Logic II

Semantics of intuitionistic propositional logic

6. Logical Inference

Marie Duží

Propositional Logic: Syntax

Predicate Logic - Semantic Tableau

A brief introduction to Logic. (slides from

7. Propositional Logic. Wolfram Burgard and Bernhard Nebel

2.5.2 Basic CNF/DNF Transformation

CS206 Lecture 03. Propositional Logic Proofs. Plan for Lecture 03. Axioms. Normal Forms

Two sources of explosion

Predicate Calculus - Undecidability of predicate calculus Moonzoo Kim CS Dept. KAIST

Warm-Up Problem. Is the following true or false? 1/35

Knowledge representation DATA INFORMATION KNOWLEDGE WISDOM. Figure Relation ship between data, information knowledge and wisdom.

A NEW FOUNDATION OF A COMPLETE BOOLEAN EQUATIONAL LOGIC

Intelligent Agents. First Order Logic. Ute Schmid. Cognitive Systems, Applied Computer Science, Bamberg University. last change: 19.

Artificial Intelligence Knowledge Representation I

Propositional logic. First order logic. Alexander Clark. Autumn 2014

3.17 Semantic Tableaux for First-Order Logic

Informal Statement Calculus

Canonical models for normal logics (Completeness via canonicity)

Description Logics. Deduction in Propositional Logic. franconi. Enrico Franconi

Modal Logic XX. Yanjing Wang

Outline. Overview. Syntax Semantics. Introduction Hilbert Calculus Natural Deduction. 1 Introduction. 2 Language: Syntax and Semantics

Propositional Reasoning

Basic Algebraic Logic

Artificial Intelligence. Propositional Logic. Copyright 2011 Dieter Fensel and Florian Fischer

Chapter 4: Classical Propositional Semantics

CS156: The Calculus of Computation

Transcription:

Propositional Calculus - Soundness & Completeness of H Moonzoo Kim CS Dept. KAIST moonzoo@cs.kaist.ac.kr 1

Review Goal of logic To check whether given a formula Á is valid To prove a given formula Á ` Á Syntactic method (proof system G, H, natural deduction,etc) Sound & Complete ² Á Semantic method (truth table, etc) Semantic tableau 2

Roadmap of the today s class ` Á Hilbert system H Sound & Complete ² Á Semantic method proof system G Semantic tableau 3

Thm 3.34 H is sound, that is ` A then ² A Proof is by structural induction We show that 1. the all three axioms are valid and that Soundness of H (1/2) 2. if the premises of MP are valid, so is the conclusion Task 1: to prove ² Axiom1, ² Axiom2, and ² Axiom3 By showing the semantic tableau of the negated axiom is closed 4

Task 2: proof by RAA ( 귀류법 ) Suppose that MP were not sound. Soundness of H (2/2) Then there would be a set of formulas {A, A! B, B} such that A and A! B are valid, but B is not valid `A `A!B `B If B is not valid, there is an interpretation v such that v(b) = F. Since A and A! B are valid, for any interpretation, in particular for v, v(a) = v(a! B) = T. From this we deduce that v(b) = T contradicting the choice of v 5

Completeness of H(1/5) Thm 3.35 H is complete, that is, if ² A then ` A Any valid formula can be proved in G (thm 3.8). We will show how a proof in G can be mechanically transformed into a proof in H The exact correspondence is that if the set of formulas U is provable in G then the single formula ÇU is provable in H A problem is that We can show that { :p, p} is an axiom in G then ` p Ç :p in H since this is simply Thm 3.10 ( ` A! A) Note that A Ç B is an abbreviation for : A! B Similarly A Æ B is an abbreviation for : (A! : B) But if the axiom in G is {q, : p, r, p, s}, we cannot immediately conclude that ` q Ç : p Ç r Ç p Ç s 6

Completeness of H(2/5) Lem 3.36 If U µu and ` ÇU (in H) then ` ÇU (in H) The proof is by induction using Thm 3.31 through 3.33 Suppose we have a proof of Ç U. By repeated application of Thm 3.31, we can transform this into a proof of Ç U, where U is a permutation of the elements of U. Thm 3.31 Weakening ` A! A Ç B and ` B! A Ç B Now by repeated applications of the commutativity and associativity of disjunction, we can move the elements of U to their proper places Thm 3.32 Commutativity rule: ` A Ç B $ B Ç A Thm 3.33 Associativity rule : ` A Ç (B Ç C) $ (A Ç B) Ç C 7

Completeness of H(3/5) Completeness proof by induction on the structure of the proof in G We are transforming a proof in G to a proof in H Task 1: If U is an axiom, it contains a pair of complementary literals and ` :p Ç p can be proved in H. BY Lem 3.36, this may be transformed into a proof of Ç U. Lem 3.36 If U µu and ` ÇU (in H) then ` ÇU (in H) 8

Task 2: Completeness of H(4/5) The last step in the proof of U in G is the application of an or rule. Case 1: An rule was used in G to infer `U 1 [fa 1 ;A 2 g `U 1 [fa 1 _A 2 g By the inductive hypothesis, ` (ÇU 1 Ç A 1 ) Ç A 2 in H from which we infer ` Ç U 1 Ç (A 1 Ç A 2 ) by associativity Case 2: An rule was used in G to infer `U 1 [fa 1 g `U 2 [fa 2 g `U 1 [U 2 [fa 1^A 2 g By the inductive hypothesis, ` Ç U 1 Ç A 1 and ` Ç U 2 Ç A 2 in H. From these, we can find a proof of ` ÇU 1 Ç ÇU 2 Ç (A 1 Æ A 2 ) 9

Completeness of H(5/5) From ` Ç U 1 Ç A 1 and ` Ç U 2 Ç A 2 in H, we can find a proof of ` ÇU 1 Ç ÇU 2 Ç (A 1 Æ A 2 ) as follows: 10

Consistency Def 3.38 A set of formulas U is inconsistent iff for some formula A, U ` A and U ` : A. U is consistent iff it is not inconsistent Thm 3.39 U is inconsistent iff for all A, U ` A Proof: Let A be an arbitrary formula. Since U is incosistent, for some formula B, U ` B and U ` : B. By Thm 3.21 ` B! ( : B! A). Using MP twice, U ` A. Corollary 3.40 U is consistent iff for some A, U 0 A Thm 3.41 U ` A iff U [ {: A} is inconsistent 11

Variants of H (1/2) Variant Hilbert systems almost invariably have MP as the only rule. They differ in the choice of primitive operators and axioms H replace Axiom 3 by Axiom 3 ` (: B! : A)! (( : B! A)! B) Thm 3.44 H and H are equivalent A proof of Axiom 3 in H The other direction? 12

Variants of H (2/2) H has the same MP rule but a set of axioms as Axiom 1 ` A Ç A! A Axiom 2 ` A! A Ç B Axiom 3 ` A Ç B! B Ç A Axiom 4 ` B! C! (A Ç B!A ÇC) Note that it is also possible to consider Ç as the primitive binary operator. Then,! is defined by : A Ç B. Yet another variant of Hilbert system H has only one axiom with MP Meredith s axiom ({[A! B)! ( : C! : D)]! C}! E)! [(E! A)! (D! A)] 13

Subformula property Def 3.48 A deductive system has the subformula property if any formula appearing in a proof of A is either a subformula of A or the negation of a subformula of A G has the subformula property while H obviously does not since MP erase formulas That is why a proof in H is harder than a proof in G If a deductive system has the subformula property, then mechanical proof may be possible since there exists only there exist only a finite number of subformulas for a finite formula Á there exist only a finite number of inference rules A proof of (p Ç q)! (q Ç p) in G 14

Automated proof One desirable property of a deductive system is to generate an automated/mechanical proof We have decision procedure to check validity of a propositional formula automatically (i.e., truth table and semantic tableau) Note that decision procedure requires knowledge on all interpretations (i.e., infinite number of interpretations in general) which is not feasible except propositional logic A deductive proof requires only a finite set of sets of formulas, because a deductive proof system analyzes the target formula only, not its interpretations. Many research works to develop (semi)automated theorem prover No obvious connection between the formula and its proof in H makes a proof in H difficult ( no mechanical proof) A human being has to rely on his/her brain to select proper axioms 15