Unary negation: T F F T

Similar documents
Truth-Functional Logic

PHIL 422 Advanced Logic Inductive Proof

Deduction by Daniel Bonevac. Chapter 3 Truth Trees

More Propositional Logic Algebra: Expressive Completeness and Completeness of Equivalences. Computability and Logic

Propositional Logic Basics Propositional Equivalences Normal forms Boolean functions and digital circuits. Propositional Logic.

Propositional Logic Truth-functionality Definitions Soundness Completeness Inferences. Modal Logic. Daniel Bonevac.

Example. Logic. Logical Statements. Outline of logic topics. Logical Connectives. Logical Connectives

The statement calculus and logic

Lecture 7. Logic. Section1: Statement Logic.

1 Propositional Logic

Philosophy 220. Truth-Functional Equivalence and Consistency

Lecture 4: Proposition, Connectives and Truth Tables

What is Logic? Introduction to Logic. Simple Statements. Which one is statement?

Tecniche di Verifica. Introduction to Propositional Logic

Symbolic Logic 3. For an inference to be deductively valid it is impossible for the conclusion to be false if the premises are true.

Comp487/587 - Boolean Formulas

Chapter 4: Classical Propositional Semantics

Solutions to Sample Problems for Midterm

Lecture 2. Logic Compound Statements Conditional Statements Valid & Invalid Arguments Digital Logic Circuits. Reading (Epp s textbook)

Introduction Propositional Logic. Discrete Mathematics Andrei Bulatov

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

Logic: Propositional Logic (Part I)

INTRODUCTION TO LOGIC. Propositional Logic. Examples of syntactic claims

CHAPTER 4 CLASSICAL PROPOSITIONAL SEMANTICS

AI Principles, Semester 2, Week 2, Lecture 5 Propositional Logic and Predicate Logic

Tautological equivalence. entence equivalence. Tautological vs logical equivalence

Semantic Metatheory of SL: Mathematical Induction

Section 1.1 Propositional Logic. proposition : true = T (or 1) or false = F (or 0) (binary logic) the moon is made of green cheese

1.1 Statements and Compound Statements

2.2: Logical Equivalence: The Laws of Logic

Discrete Mathematics and Its Applications

KRIPKE S THEORY OF TRUTH 1. INTRODUCTION

02 Propositional Logic

MA103 STATEMENTS, PROOF, LOGIC

Topic 1: Propositional logic

A Little Deductive Logic

Seminaar Abstrakte Wiskunde Seminar in Abstract Mathematics Lecture notes in progress (27 March 2010)

Mathematical Logic Part One

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

CS 486: Lecture 2, Thursday, Jan 22, 2009

Introduction. Applications of discrete mathematics:

Logic. Definition [1] A logic is a formal language that comes with rules for deducing the truth of one proposition from the truth of another.

First Degree Entailment

a. ~p : if p is T, then ~p is F, and vice versa

Exclusive Disjunction

Introduction to Logic

DERIVATIONS AND TRUTH TABLES

We last time we began introducing equivalency laws.

INTRODUCTION TO LOGIC

Chapter 1 Review of Equations and Inequalities

Propositional Calculus: Formula Simplification, Essential Laws, Normal Forms

Compound Propositions

Supplementary Logic Notes CSE 321 Winter 2009

Boolean Algebra and Digital Logic

1 Boolean Algebra Simplification

CSCI3390-Lecture 17: A sampler of NP-complete problems

Introducing Proof 1. hsn.uk.net. Contents

Manual of Logical Style (fresh version 2018)

Logic as a Tool Chapter 1: Understanding Propositional Logic 1.1 Propositions and logical connectives. Truth tables and tautologies

CHAPTER 6 - THINKING ABOUT AND PRACTICING PROPOSITIONAL LOGIC

Section 1.1: Logical Form and Logical Equivalence

Section 1.2 Propositional Equivalences. A tautology is a proposition which is always true. A contradiction is a proposition which is always false.

Propositional Logic. Fall () Propositional Logic Fall / 30

Conjunctive Normal Form and SAT

CS 380: ARTIFICIAL INTELLIGENCE PREDICATE LOGICS. Santiago Ontañón

Chapter 1, Section 1.1 Propositional Logic

8. Reductio ad absurdum

Propositional Logic Review

Discrete Mathematics

Natural deduction for truth-functional logic

The Samson-Mills-Quine theorem

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

Logical Agents. Knowledge based agents. Knowledge based agents. Knowledge based agents. The Wumpus World. Knowledge Bases 10/20/14

A Little Deductive Logic

Artificial Intelligence Chapter 7: Logical Agents

ICS141: Discrete Mathematics for Computer Science I

Propositional Logic and Semantics

INTRODUCTION TO LOGIC 3 Formalisation in Propositional Logic

Logic, Sets, and Proofs

Applied Logic. Lecture 3 part 1 - Fuzzy logic. Marcin Szczuka. Institute of Informatics, The University of Warsaw

The Samson-Mills-Quine theorem

Overview. 1. Introduction to Propositional Logic. 2. Operations on Propositions. 3. Truth Tables. 4. Translating Sentences into Logical Expressions

6. Conditional derivations

An Introduction to SAT Solving

8. Reductio ad absurdum

6.080 / Great Ideas in Theoretical Computer Science Spring 2008


PHIL12A Section answers, 16 February 2011

Choosing Logical Connectives

Intermediate Logic. Natural Deduction for TFL

Propositional Logic. Testing, Quality Assurance, and Maintenance Winter Prof. Arie Gurfinkel

Functions. Computers take inputs and produce outputs, just like functions in math! Mathematical functions can be expressed in two ways:

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

PROPOSITIONAL CALCULUS

Propositional logic ( ): Review from Mat 1348

Propositional Logic, Predicates, and Equivalence

Informatics 1 - Computation & Logic: Tutorial 3

A statement is a sentence that is definitely either true or false but not both.

DISCRETE STRUCTURES WEEK5 LECTURE1

2/13/2012. Logic: Truth Tables. CS160 Rosen Chapter 1. Logic?

Transcription:

Unary negation: ϕ 1 ϕ 1 T F F T

Binary (inclusive) or: ϕ 1 ϕ 2 (ϕ 1 ϕ 2 ) T T T T F T F T T F F F Binary (exclusive) or: ϕ 1 ϕ 2 (ϕ 1 ϕ 2 ) T T F T F T F T T F F F

Classical (material) conditional: ϕ 1 ϕ 2 (ϕ 1 ϕ 2 ) T T T T F F F T T F F T Classical (material) biconditional: ϕ 1 ϕ 2 (ϕ 1 ϕ 2 ) T T T T F F F T F F F T

How many (two-valued) binary truth functions are there? ϕ 1 ϕ 2 T T T T T T T T T T F F F F F F F F T F T T T T F F F F T T T T F F F F F T T T F F T T F F T T F F T T F F F F T F T F T F T F T F T F T F T F

How many (two-valued) binary truth functions are there? ϕ 1 ϕ 2 T T T T T T T T T T F F F F F F F F T F T T T T F F F F T T T T F F F F F T T T F F T T F F T T F F T T F F F F T F T F T F T F T F T F T F T F 16 (= 2 (22) ) Which ones are in common use?

How many (two-valued) unary truth functions are there? ϕ 1 T T T F F F T F T F 4 (= 2 (21) )

How many 0-ary connectives are there? T F 2 (= 2 (20) ) What s the difference between, (ϕ 1 ), and (ϕ 1, ϕ 2 )?

Logic textbooks don t usually discuss connectives of higher adicity, although the following one is sometimes mentioned (e.g., by Alonzo Church, 1956). He called it conditional disjunction, although most of the rest of us call it if then else : ϕ 1 ϕ 2 ϕ 3 (ϕ 1, ϕ 2, ϕ 3 ) T T T T T T F T T F T F T F F F F T T T F T F F F F T T F F F F How many ternary connectives are there?

How many ternary connectives are there? 256 (= 2 (23) ) How many quadrinary? 65, 536 (= 2 (24) ) How many truth functions are there all told? So why do logic textbooks stick with just the few common ones???

A combination of: These connectives correspond (sort of) to natural language connectives, and they are functionally complete [also known as expressively complete ] This means that all of the infinite number of truth functions can be expressed as some combination of the ones they chose. How do we prove that??

By showing how to construct a formula that corresponds to any arbitrary truth table: Here s an example truth function (represented by this arbitrary truth table. (I m just using a ternary truth function, so as to be able to fit it on the page... the method works with any adicity). ϕ 1 ϕ 2 ϕ 3 Ψ T T T T T T F F T F T F T F F F F T T T F T F F F F T T F F F F Step 1: For each line of the truth table where Ψ is T, write a formula that is true in that row and in no other row.

ϕ 1 ϕ 2 ϕ 3 Ψ T T T T (ϕ 1 ϕ 2 ϕ 3 ) T T F F T F T F T F F F F T T T ( ϕ 1 ϕ 2 ϕ 3 ) F T F F F F T T ( ϕ 1 ϕ 2 ϕ 3 ) F F F F Note that each of those formulas is true in just exactly their one corresponding row of the truth table. Now, put a between each of those formulas: ((ϕ 1 ϕ 2 ϕ 3 ) ( ϕ 1 ϕ 2 ϕ 3 ) ( ϕ 1 ϕ 2 ϕ 3 )) This is true in one of the three rows, but not in any other row. Thus it characterizes Ψ.

Did it really work?? Did the demonstration cover all the cases?? What do you think?

The formula thus generated is in what is called Disjunctive Normal Form (DNF): (a) The main connectives are disjunctions ( ). (b) Each of the disjuncts is a conjunction, so their main connectives are (c) Each of the items thus conjoined is a literal either a sentence letter or the negation of a sentence letter. Every formula can be put into DNF (and there is a corresponding CNF). This shows that every one of the infinite number of truth functions can be written using only the unary and the binary and. Alternatively put: The set of connectives, {,, } is functionally complete.

Who remembers the DeMorgan Laws?? So this means that each of {, } and {, } are functionally complete, since using just the connectives in one of these sets, one can define everything in an already-known-to-be-functionally-complete set (namely {,, }) And we all know the definition of p q as either (p q) or p q. So {, } is also functionally complete.

Maybe you also know that the connectives and are each functionally complete by themselves? Their truth tables are: ϕ 1 ϕ 2 (ϕ 1 ϕ 2 ) (ϕ 1 ϕ 2 ) T T F F T F F T F T F T F F T T Can you show how to use one of these to define the connectives of an already-known-to-be-functionally-complete set of connectives? Exercise: Show that {, } is functionally complete. Show that {,, } is functionally complete.

Ok that s all well and good. But WHY: How come {, }, {, }, {, }, {,, }, {, }, { }, and { } are functionally complete, but nonetheless {, } is not functionally complete?? {,, } is not functionally complete?? The answer is given in Post s Functional Completeness Theorem (Emil Post, 1941). The most AMAZING and AWE-INSPIRING thing ever proved about two-valued truth-functional logic!!

But first some preliminaries. We need to define five abstract properties of truth functions: 1. Closed under T 2. Closed under F 3. Self-Dual 4. Monotonic (requires preliminary notion of truth-vector order) 5. Counting (requires preliminary notions of truth-vector order and of dummy variable) Easy definitions: A truth-function is closed under T iff it has a T in its first row (i.e., if all its arguments are T then so is its resulting value) A truth-function is closed under F iff it has a F in its last row (i.e., if all its arguments are F then so is its resulting value) So:,,,,, are all closed under T.,,,, are all closed under F.

SELF-DUALITY: Let p be the name of the truth value that is the opposite of the value of p. A truth function f is self-dual iff for each input vector (line of truth table) < p 1,, p n >, f (< p 1,, p n >) = f (< p 1,, p n >) So therefore a function f is not self-dual iff there is a row of the truth table, < q 1,, q n > such that f (< p 1,, p n >) = f (< p 1,, p n >) For example,,, are self-dual.,,,,, are not.

Background notion: truth-vector ordering. This is an ordering of the input rows of a truth table (technically, it s a partial order not every pair of rows can be compared). Maybe clearest with an example. Suppose a truth vector of three elements. Here s an ordering: < T, T, T > < F, T, T > < T, F, T > < T, T, F > < F, F, T > < F, T, F > < T, F, F > < F, F, F > T is considered greater than F. And so each member of each level is greater than any of the ones in the next level lower. Being greater than is transitive, so that following a pathway downward always will yield cases of greater than.

MONOTONIC TRUTH FUNCTION: The n-ary truth function f n is monotonic iff whenever n-ary truth vectors are such that V 1 V 2, f n (V 1 ) f n (V 2 ) So, a (two-valued) truth function f is not monotonic iff there is a row of the truth table where the value of f at that row is F while the value of f at some row that is less than the first is T. For example:,,, are monotonic.,,,, are not.

DUMMY POSITION (for a truth function f ): A position in a truth function s input values is dummy iff it never makes a difference in evaluating the truth function. That is: The i th position of < x 1,, x n > is a dummy position iff f n (x 1,, x i 1, F, x i+1,, x n ) = f n (x 1,, x i 1, T, x i+1,, x n ) for every combination of values of all the other positions. Consider this imaginary binary connective (p q): ϕ 1 ϕ 2 (ϕ 1 ϕ 2 ) T T F T F T F T F F F T Obviously, this is really just ϕ 2. ϕ 1 never makes a difference.

COUNTING FUNCTIONS: A function f is a counting function iff (i) No position is dummy. (ii) if V i and V j are immediately adjacent in the truth-vector ordering, then f (V i ) f (V j ) Or in other words, if two input vectors differ in only one value, but the values of the function applied to those two rows are the same, then the function is not counting (well, unless that position is dummy).

ϕ 1 ϕ 1 T F F T Negation is self-dual and counting (but not the other properties) ϕ 1 ϕ 2 T T T T T T F T F T F F T F F T T F F T F T T F T T F F F F F T T F T F, are T-preserving, F-preserving, monotonic is T-preserving is T-preserving and counting is F-preserving and counting is T-preserving, monotonic and counting is F-preserving, monotonic and counting And of course, atomic sentences themselves (or, if you prefer, the identity function on truth values) are T-preserving, F-preserving, self-dual, monotonic, and counting.

POST S FUNCTIONAL COMPLETENESS THEOREM The most amazing theorem about two-valued truth functions ever!!

A set of (2-valued) truth functions, X, is functionally complete iff for each of: closed under T closed under F monotonic self-dual counting, there is a member of X which does not manifest that property.

HOW TO PROVE THIS BEAUTIFUL THEOREM?? Well, it s an iff theorem, so let s try proving each of the two directions separately: (i) If X is a functionally complete set, then for each of the properties, some member of X does not manifest it. (ii) Given any set of connectives X such that, for each of the properties, there is at least one connective in X lacks that property, then X is functionally complete. Let s prove (ii) first. We ll do it by construction that is, I will assume I have connectives that lack the five properties, and will then show you how to define a set of connectives that you already know to be functionally complete (such as {, } or {, }, etc.) Afterwards I ll show you how a proof of (i) proceeds, but won t actually present the detailed proof.

ϕ 1 ϕ 2 ϕ 3 f 1 f 2 f 3 f 4 f 5 f 6 T T T F T T F T F T T F T F F 1 T 1 T T T F T T F F T 1 F F T F F T T F T T 1 F F T T T T T T T 1 F F T F F T T 1 F F T F F T T T T F F T F F F F T F T F T f 1 and f 2 are non-t- and non-f-preserving (respectively). f 3 is non-monotonic (as proved by the 1 s). f 4 is non-counting [no dummies: rows 1 & 5 for 1st position, 6 & 8 for 2nd position, 1 & 2 for 3rd position]. The 1 s show where it isn t counting. f 5 is non-self-dual, as shown by the 1 s. f 6 is a function that is non-t-and-non-f-preserving, non-monotonic, non-self-dual, non-counting.