Logic I - Session 10 Thursday, October 15,

Similar documents
Logic I - Session 22. Meta-theory for predicate logic

Natural deduction for truth-functional logic

Truth-Functional Logic

Semantic Metatheory of SL: Mathematical Induction

PHIL12A Section answers, 28 Feb 2011

1 Tautologies, contradictions and contingencies

CS280, Spring 2004: Final

Introduction to Metalogic

Mathematics 114L Spring 2018 D.A. Martin. Mathematical Logic

Completeness in the Monadic Predicate Calculus. We have a system of eight rules of proof. Let's list them:

Formal (natural) deduction in propositional logic

Logic: Propositional Logic (Part I)

Introducing Proof 1. hsn.uk.net. Contents

Chapter 1: The Logic of Compound Statements. January 7, 2008

First Order Logic: Syntax and Semantics

CHAPTER 6 - THINKING ABOUT AND PRACTICING PROPOSITIONAL LOGIC

COMP219: Artificial Intelligence. Lecture 19: Logic for KR

CS1800: Strong Induction. Professor Kevin Gold

Philosophy 220. Truth-Functional Equivalence and Consistency

Automated Program Verification and Testing 15414/15614 Fall 2016 Lecture 2: Propositional Logic

Propositional Logic Review

PHIL 422 Advanced Logic Inductive Proof

Announcements For Methods of Proof for Boolean Logic Proof by Contradiction. Outline. The Big Picture Where is Today? William Starr

COMP219: Artificial Intelligence. Lecture 19: Logic for KR

Logic. Propositional Logic: Syntax

Learning Goals of CS245 Logic and Computation

Supplementary exercises in propositional logic

Logic As Algebra COMP1600 / COMP6260. Dirk Pattinson Australian National University. Semester 2, 2017

Description Logics. Foundations of Propositional Logic. franconi. Enrico Franconi

Propositional Logic: Semantics and an Example

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

CS 380: ARTIFICIAL INTELLIGENCE PREDICATE LOGICS. Santiago Ontañón

Logical Agents. Outline

Deductive Systems. Lecture - 3

PUZZLE. You meet A, B, and C in the land of knights and knaves. A says Either B and I are both knights or we are both knaves.

A Little Deductive Logic

cis32-ai lecture # 18 mon-3-apr-2006

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

The semantics of propositional logic

Classical Propositional Logic

Advanced Topics in LP and FP

CS 771 Artificial Intelligence. Propositional Logic

Logic: Bottom-up & Top-down proof procedures

Logic, Sets, and Proofs

Section 1.1: Logical Form and Logical Equivalence

First-Degree Entailment

Proof Methods for Propositional Logic

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

Natural Deduction for Propositional Logic

Logic. Propositional Logic: Syntax. Wffs

Lecture 7. Logic. Section1: Statement Logic.

Peano Arithmetic. by replacing the schematic letter R with a formula, then prefixing universal quantifiers to bind

PEANO AXIOMS FOR THE NATURAL NUMBERS AND PROOFS BY INDUCTION. The Peano axioms

Title: Logical Agents AIMA: Chapter 7 (Sections 7.4 and 7.5)

Combinatorics. But there are some standard techniques. That s what we ll be studying.

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

Inference Methods In Propositional Logic

Handout: Proof of the completeness theorem

KB Agents and Propositional Logic

A Little Deductive Logic

Proseminar on Semantic Theory Fall 2013 Ling 720 Proving the Soundness and Completeness of Propositional Logic: Some Highlights 1

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

Arguments and Proofs. 1. A set of sentences (the premises) 2. A sentence (the conclusion)

Announcements. CS243: Discrete Structures. Propositional Logic II. Review. Operator Precedence. Operator Precedence, cont. Operator Precedence Example

Knowledge base (KB) = set of sentences in a formal language Declarative approach to building an agent (or other system):

Induction 1 = 1(1+1) = 2(2+1) = 3(3+1) 2

COMP9414: Artificial Intelligence Propositional Logic: Automated Reasoning

Artificial Intelligence Chapter 7: Logical Agents

Deduction by Daniel Bonevac. Chapter 3 Truth Trees

EE562 ARTIFICIAL INTELLIGENCE FOR ENGINEERS

Logic and Philosophical Logic. 1 Inferentialism. Inferentialism and Meaning Underdetermination

Warm-Up Problem. Write a Resolution Proof for. Res 1/32

Section 1.2: Propositional Logic

The Importance of Being Formal. Martin Henz. February 5, Propositional Logic

Truth Tables for Propositions

Semantics and Pragmatics of NLP

Discrete Mathematics for CS Fall 2003 Wagner Lecture 3. Strong induction

The predicate calculus is complete

PHIL12A Section answers, 16 February 2011

Logical Inference. Artificial Intelligence. Topic 12. Reading: Russell and Norvig, Chapter 7, Section 5

6.080 / Great Ideas in Theoretical Computer Science Spring 2008

Introduction to Metalogic 1

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

ALGEBRAIC STRUCTURE AND DEGREE REDUCTION

Inference Methods In Propositional Logic

Propositional Logic: Syntax

AI Programming CS S-09 Knowledge Representation

Artificial Intelligence. Propositional logic

Proof Techniques (Review of Math 271)

CS 380: ARTIFICIAL INTELLIGENCE

Why Learning Logic? Logic. Propositional Logic. Compound Propositions

Logical Agents: Propositional Logic. Chapter 7

Intelligent Agents. Pınar Yolum Utrecht University

a. See the textbook for examples of proving logical equivalence using truth tables. b. There is a real number x for which f (x) < 0. (x 1) 2 > 0.

Propositional Logic: Bottom-Up Proofs

FORMAL PROOFS DONU ARAPURA

Intermediate Logic. Natural Deduction for TFL

Before we show how languages can be proven not regular, first, how would we show a language is regular?

Propositional Logic: Syntax

CHAPTER 2 INTRODUCTION TO CLASSICAL PROPOSITIONAL LOGIC

Transcription:

Logic I - Session 10 Thursday, October 15, 2009 1

Plan Re: course feedback Review of course structure Recap of truth-functional completeness? Soundness of SD Thursday, October 15, 2009 2

The course structure Basics of arguments and logical notions (deductive validity and soundness, logical truth, falsity, consistency, indeterminacy, equivalence SL: syntax and semantics Derivation system SD (and SD+) Meta-logic: proofs about SL and SD / SD+ PL: syntax and semantics Derivation system PD (and PD+, PDE) Meta-logic: proofs about PL and PD / PD+ / PDE Thursday, October 15, 2009 3

Last time Mathematical induction Strategy: (1) Insert relevant definitions in the claim you want to prove. (2) Arrange a sequence for the induction. (3) Formulate basis clause and inductive hypothesis. (4) Prove basis clause. (5) Prove inductive hypothesis by assuming its antecedent (n case) and deducing its consequent (n+1 case). Truth-functional completeness Thursday, October 15, 2009 4

Truth-functional completeness Truth-function: a mapping, for some positive integer n, from each combination of TVs n sentences can have to a TV. E.g. for two sentences: {T,F}X{T,F} {T,F}. More generally: {T,F} n {T,F} SL is truth-functionally complete iff for every truth-function f, there is an SL sentence P that expresses f. P expresses f iff P s truth-table is the characteristic truthtable for for f Thursday, October 15, 2009 5

Truth-functional completeness We can state this more formally than in TLB: An truth-function f is a set of ordered pairs like this: {<<T,T>, T>, <<T,F>, F>, <<F,T>, F>, <<F,F>, F>} P expresses f iff for any i that is a member of f, when the atomic components of P are assigned the TVs in the 1st member of i, P receives the TV that s the 2nd member of i. Thursday, October 15, 2009 6

Truth-functional completeness Why care? We want to use SL and truth-tables to test for TFtruth, validity, consistency, etc. Suppose we couldn t express some TF in SL, e.g. neither/nor. Then we would have no sentence of SL that expressed the same truth-function as Neither Alice nor Bill can swim. But then SL wouldn t let us use a TT to show that the sentence is TF-entailed by {`Alice can swim if and only if Bill can swim, `If Alice can swim, then Carol can t swim, `Carol can swim }. Similar points apply to other truth-functions and tests for truth-functional properties and relations Thursday, October 15, 2009 7

Truth-functional completeness So we want to know that we can express every truth-function We know this because we can set out an algorithm that, for any truth-function f, generates a sentence that expresses f. We can do this by focusing on each row of the TT that represents f, finding characteristic sentences for each Thursday, October 15, 2009 8

Truth-functional completeness Look at each value left of the vertical line in row i. (We re going to pick a sentence for each value.) If the first value is T, we pick A. If it s F, we pick A. If the second value is T, we pick B. If it s F, we pick B, etc. Form the iterated conjunction of all these sentences. This is the CS for row i. Thursday, October 15, 2009 9

Truth-functional completeness Repeat the procedure for other rows until you have a CS for each row Now find a sentence P that expresses the TF represented by the whole TT. Look at the values right of the vert line. If there are no Ts, P is any contradiction, e.g. A& A. If there is just one T, on row i, P is the CS for row i. If there are Ts on multiple rows, P is the iterated disjunction of the CSs for those rows. Thursday, October 15, 2009 10

Ex: Find a sentence that expresses the TF for this TT schema: T T T F T T F F T F T T T F F F F T T T F T F F F F T T F F F F (A&B)&C (A&B)& C (A& B)&C (A& B)& C ( A&B)&C ( A&B)& C ( A& B)&C ( A& B)& C There are Ts right of the vertical line on rows 3, 5, and 7. So we want an interated disjunction of the CSs for those rows. (((A& B)&C) v (( A&B)&C)) v (( A& B)&C) Thursday, October 15, 2009 11

SD is sound iff if Γ P in SD, then Γ P. Why do we care about soundness of SD? In doing logic, we care about truth. E.g.: If the sentences in Γ are true, must P be true? That is, are derivations always truth-preserving? If we want to use a derivation in SD of P from Γ to help us tell whether the truth of a given sentence follows from the truth of some other sentences, then derivations in SD better be a guide to truth-functional entailment! I.e. it better be that if Γ P in SD, then Γ P. Thursday, October 15, 2009 12

So how do we prove that if Γ P in SD, then Γ P? Mathematical induction of course! Let s start with a reminder of the definitions for and `. Γ P iff every TVA that makes all members of Γ true also makes P true. Γ P (in SD) iff there is a derivation (in SD) in which all the primary assumptions are members of Γ and P occurs in the scope of only those assumptions. Thursday, October 15, 2009 13

Let s think now about the sequence on which we ll use MI. A natural sequence to use is derivation length. We could try: Basis clause: For any 1-line derivation (in SD) in which all the primary assumptions are members of Γ and P occurs in the scope of only those assumption, Γ P. Thursday, October 15, 2009 14

Then our inductive hypothesis would be: IH: If (A) For any n-line derivation in which all the primary assumptions are members of Γ* and Q occurs in the scope of only those assumption, Γ* Q, then (B) for any n+1-line derivation in which all the primary assumptions are members of Γ^ and R occurs in the scope of only those assumption, Γ^ R. Thursday, October 15, 2009 15

But this won t work! Exploring why will help us understand why the proof in the book goes the way it does. Suppose we ve assumed (A), the n-line case. Now we re working on (B), the n+1-line case. In this situation, we d like to be able to know that the nth line of the n+1-line derivation is OK Then we d just have to show that adding the n+1st line doesn t get us into trouble. So we d like to use our assumption (A)... But this is where the proof hits trouble... Thursday, October 15, 2009 16

(A) doesn t guarantee anything about the nth line in an n+1 line derivation! (Why?) (A) For any n-line derivation in which all the primary assumptions are members of Γ* and Q occurs in the scope of only those assumption, Γ* Q. (A) only applies if the sentence on the nth line is only in the scope of primary assumptions! And in an n+1 line derivation, the nth line might not be only in the scope of primary assumptions. So (A) doesn t guarantee that in our n+1-line derivation we didn t already go wrong in getting to line n. Thursday, October 15, 2009 17

So what do we do? We need (A) to be stronger, so that it applies to the nth line of an n+1-line derivation. (Compare our proof last time of 6.1E (1a).) So we make the inductive hypothesis stronger, and make the basis clause stronger accordingly. That s why the proof in the book is as complex as it is! New basis clause: In any derivation, if Γ1 is the set of open assumptions with scope over sentence P1 on line 1, then Γ1 P1. Importantly, we re NOT requiring that the assumptions in Γ1 be primary assumptions. Thursday, October 15, 2009 18

New inductive hypothesis: If (A) then (B). (A) In any derivation, for every line i n, if Γi is the set of open assumptions with scope over sentence Pi on line i, then Γi Pi. (B) In any derivation, if Γn+1 is the set of open assumptions with scope over sentence Pn+1 on line n+1, then Γn+1 Pn+1. Thursday, October 15, 2009 19

Now, prove the basis clause: In any derivation, if Γ1 is the set of open assumptions with scope over sentence P1 on line 1, then Γ1 P1. Since P1 is on line 1, P1 must be an assumption. And since every assumption is in its own scope, and there aren t any other sentences before P1, the set of open assumptions with scope over P1 is just {P1}. So Γ1 = {P1}. Trivially, {P1} P1, so since Γ1 = {P1}, Γ1 P1. Thursday, October 15, 2009 20

Now let s prove the inductive hypothesis by assuming (A). (A) In any derivation, for every line i n, if Γi is the set of open assumptions with scope over sentence Pi on line i, then Γi Pi. Now suppose Γn+1 is the set of open assumptions with scope over sentence Pn+1 on line n+1. We need to show that Γn+1 Pn+1. (A) entails that Γn Pn, and similarly for every earlier line. So we only need to show that we didn t go wrong in the step to line n+1. Thursday, October 15, 2009 21

Pn+1 on line n+1 had to be justified by one SD s rules. So we can proceed by showing that whichever rule justified Pn+1, the result is that Γn+1 Pn+1. I ll just go a couple of the rules. (The other cases are in TLB.) Suppose Pn+1 is justified by conjunction elimination applied to a conjunction Pn+1&R (or R&Pn+1) on line j. We know that since j < n+1, Γj Pn+1&R (or R&Pn+1) So Γj Pn+1. Thursday, October 15, 2009 22

Now, if Pn+1 is justified by the sentence on line j, then all the assumptions open at j must still be open at n+1. That means that Γj Γn+1. So we can show that Γn+1 Pn+1 if we can prove the following: (*) If Γj Pn+1 and Γj Γn+1, then Γn+1 Pn+1. We can prove that easily: if a TVA me.m. Γn+1 true and Γj Γn+1, then it me.m. Γj is true. So if every TVA that me.m. Γj true also makes Pn+1 true, then every TVA that me.m. Γn+1 true me.m. Γj true, and hence makes Pn+1 true. So (*) is true. And we know its antecedent is true: Γj Pn+1 and Γj Γn+1. So it follows that Γn+1 Pn+1. Thursday, October 15, 2009 23

Now we ve made some progress on establishing (B) given (A). (B) In any derivation, if Γn+1 is the set of open assumptions in whose scope is a sentence Pn+1 on line n+1, then Γn+1 Pn+1. For we ve shown that given (A), (B) holds whenever Pn+1 is justified by conjunction elimination. If we check all the other rules, then we ll have proven given (A) that however we got Pn+1 from earlier lines, Γn+1 Pn+1. So we ll have proven (B) given (A). So we ll have proven the inductive hypothesis and finished our MI proof. Thursday, October 15, 2009 24

Suppose Pn+1 is justified by applying I to lines h-k n+1. Then Pn+1 is of the form Q, line h is Q, and lines j k and k contain some contradictory R and R. Since j and k n+1, we know that (A) applies to lines j and k, so Γj R and Γk R. For I to apply, we can t have closed any assumptions between h and n+1 except Q. So we know that Γj-Q and Γk-Q are subsets of Γn+1. So Γj Γn+1 {Q} and Γk Γn+1 {Q}. But that means Γn+1 {Q} R and Γn+1 {Q} R. So Γn+1 Q. Thursday, October 15, 2009 25

MIT OpenCourseWare http://ocw.mit.edu 24.241 Logic I Fall 2009 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 1