Rules Build Arguments Rules Building Arguments

Similar documents
Sec$on Summary. Valid Arguments Inference Rules for Propositional Logic. Inference Rules for Quantified Statements. Building Arguments

Review. Propositions, propositional operators, truth tables. Logical Equivalences. Tautologies & contradictions

Sec$on Summary. Valid Arguments Inference Rules for Propositional Logic. Inference Rules for Quantified Statements. Building Arguments

CSCI-2200 FOUNDATIONS OF COMPUTER SCIENCE

CS 2336 Discrete Mathematics

Rules of Inference. Arguments and Validity

3/29/2017. Logic. Propositions and logical operations. Main concepts: propositions truth values propositional variables logical operations

The Foundations: Logic and Proofs. Chapter 1, Part III: Proofs

With Question/Answer Animations. Chapter 1, Part III: Proofs

KS MATEMATIKA DISKRIT (DISCRETE MATHEMATICS ) RULES OF INFERENCE. Discrete Math Team

Methods of Proof. 1.6 Rules of Inference. Argument and inference 12/8/2015. CSE2023 Discrete Computational Structures

Review: Potential stumbling blocks

Introduction Logic Inference. Discrete Mathematics Andrei Bulatov

Mathacle. PSet ---- Algebra, Logic. Level Number Name: Date: I. BASICS OF PROPOSITIONAL LOGIC

Discrete Structures of Computer Science Propositional Logic III Rules of Inference

software design & management Gachon University Chulyun Kim

Discrete Structures for Computer Science

Readings: Conjecture. Theorem. Rosen Section 1.5

Chapter 1, Logic and Proofs (3) 1.6. Rules of Inference

Discrete Mathematics Logics and Proofs. Liangfeng Zhang School of Information Science and Technology ShanghaiTech University

Outline. Rules of Inferences Discrete Mathematics I MATH/COSC 1056E. Example: Existence of Superman. Outline

CS100: DISCRETE STRUCTURES. Lecture 5: Logic (Ch1)

Intro to Logic and Proofs

Math.3336: Discrete Mathematics. Nested Quantifiers/Rules of Inference

Logic Overview, I. and T T T T F F F T F F F F

Predicate Logic. Andreas Klappenecker

ECOM Discrete Mathematics

Proofs. Example of an axiom in this system: Given two distinct points, there is exactly one line that contains them.

Why Learning Logic? Logic. Propositional Logic. Compound Propositions

3. The Logic of Quantified Statements Summary. Aaron Tan August 2017

ICS141: Discrete Mathematics for Computer Science I

Discrete Mathematics. Propositional & Predicate Logic. Lecture Notes

Sample Problems for all sections of CMSC250, Midterm 1 Fall 2014

Agenda. Introduction to Proofs Dr Patrick Chan School of Computer Science and Engineering South China University of Technology

DISCRETE MATHEMATICS BA202

CSCE 222 Discrete Structures for Computing. Predicate Logic. Dr. Hyunyoung Lee. !!!!! Based on slides by Andreas Klappenecker

COMP 182 Algorithmic Thinking. Proofs. Luay Nakhleh Computer Science Rice University

Propositional Logic Not Enough

n logical not (negation) n logical or (disjunction) n logical and (conjunction) n logical exclusive or n logical implication (conditional)

1 The Foundation: Logic and Proofs

Logic - recap. So far, we have seen that: Logic is a language which can be used to describe:

CS 2740 Knowledge Representation. Lecture 4. Propositional logic. CS 2740 Knowledge Representation. Administration

Natural Deduction is a method for deriving the conclusion of valid arguments expressed in the symbolism of propositional logic.

Boolean Algebra and Proof. Notes. Proving Propositions. Propositional Equivalences. Notes. Notes. Notes. Notes. March 5, 2012

1 The Foundation: Logic and Proofs

Solutions to Exercises (Sections )

CS0441 Discrete Structures Recitation 3. Xiang Xiao

Chapter 4, Logic using Propositional Calculus Handout

Rules of Inference. Agenda. Rules of Inference Dr Patrick Chan. p r. p q. q r. Rules of Inference for Quantifiers. Hypothetical Syllogism

Math 3336: Discrete Mathematics Practice Problems for Exam I

PROPOSITIONAL CALCULUS

(Refer Slide Time: 02:20)

PHI Propositional Logic Lecture 2. Truth Tables

Predicate Logic & Quantification

Review for Midterm 1. Andreas Klappenecker

2. Use quantifiers to express the associative law for multiplication of real numbers.

Analyzing Arguments with Truth Tables

Manual of Logical Style

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.

Do not start until you are given the green signal

2/2/2018. CS 103 Discrete Structures. Chapter 1. Propositional Logic. Chapter 1.1. Propositional Logic

A. Propositional Logic

Discrete Mathematics

Logic and Proof. Aiichiro Nakano

n Empty Set:, or { }, subset of all sets n Cardinality: V = {a, e, i, o, u}, so V = 5 n Subset: A B, all elements in A are in B

Predicate Logic. Example. Statements in Predicate Logic. Some statements cannot be expressed in propositional logic, such as: Predicate Logic

CSE 20 DISCRETE MATH. Winter

THE LOGIC OF COMPOUND STATEMENTS

Reexam in Discrete Mathematics

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

CHAPTER 1 - LOGIC OF COMPOUND STATEMENTS

Test 1 Solutions(COT3100) (1) Prove that the following Absorption Law is correct. I.e, prove this is a tautology:

CPSC 121: Models of Computation. Module 6: Rewriting predicate logic statements

MACM 101 Discrete Mathematics I. Exercises on Propositional Logic. Due: Tuesday, September 29th (at the beginning of the class)

SRI VENKATESWARA COLLEGE OF ENGINEERING AND TECHNOLOGY MA DISCRETE MATHEMATICS

CSE 20 DISCRETE MATH. Fall

CSC 125 :: Final Exam May 3 & 5, 2010

Knowledge Representation. Propositional logic

Unit I LOGIC AND PROOFS. B. Thilaka Applied Mathematics

Logic. Logic is a discipline that studies the principles and methods used in correct reasoning. It includes:

Mat 243 Exam 1 Review

CPSC 121: Models of Computation

Review. p q ~p v q Contrapositive: ~q ~p Inverse: ~p ~q Converse: q p

Knowledge Representation. Propositional logic.

CSE Discrete Structures

Propositional Logic. Argument Forms. Ioan Despi. University of New England. July 19, 2013

Packet #2: Set Theory & Predicate Calculus. Applied Discrete Mathematics

Anna University, Chennai, November/December 2012

Full file at Chapter 1

Inference and Proofs (1.6 & 1.7)

MAT 243 Test 1 SOLUTIONS, FORM A

Advanced Topics in LP and FP

Propositional Logic. Spring Propositional Logic Spring / 32

It rains now. (true) The followings are not propositions.

Today s Lecture 2/25/10. Truth Tables Continued Introduction to Proofs (the implicational rules of inference)

Steinhardt School of Culture, Education, and Human Development Department of Teaching and Learning. Mathematical Proof and Proving (MPP)

[Ch 3, 4] Logic and Proofs (2) 1. Valid and Invalid Arguments ( 2.3, 3.4) 400 lecture note #2. 1) Basics

Section 1.1 Propositions

Mat2345 Week 2. Chap 1.5, 1.6. Fall Mat2345 Week 2. Chap 1.5, 1.6. Week2. Negation. 1.5 Inference. Modus Ponens. Modus Tollens. Rules.

III. Elementary Logic

Transcription:

Section 1.6 1

Section Summary Valid Arguments Inference Rules for Propositional Logic Using Rules of Inference to Build Arguments Rules of Inference for Quantified Statements Building Arguments for Quantified Statements 2

Valid Arguments 1. Valid argument in Propositional Logic Inference Rules 2. Valid argument in Predicate Logic Inference rules for propositional logic plus additional inference rules to handle variables and quantifiers. The rules of inference are the essential building block in the construction of valid arguments. 3

Arguments in Propositional Logic An argument in propositional logic is a sequence of propositions. All but the final proposition are called premises. The last statement is the conclusion. Premises are true statements. The argument is valid if the premises imply the conclusion. An argument form is an argument that is valid no matter what propositions are substituted into its propositional variables. If the premises are p 1,p 2,,p n and the conclusion is q then (p 1 p 2 p n ) q is a tautology. Inference rules are all simple argument forms that will be used to construct more complex argument forms. 4

Rules of Inference for Propositional Logic: Modus Ponens Corresponding Tautology: (p (p q)) q Why premises imply the conclusion? Truth table Example: Let p be It is snowing. Let q be I will study discrete math. If it is snowing, then I will study discrete math. It is snowing. Therefore, I will study discrete math. 5

Modus Tollens Corresponding Tautology: ( q (p q)) p Why premises imply the conclusion? Truth table Example: Let p be it is snowing. Let q be I will study discrete math. If it is snowing, then I will study discrete math. I will not study discrete math. Therefore, it is not snowing. 6

Hypothetical Syllogism Corresponding Tautology: ((p q) (q r)) (p r) Example: Let p be it snows. Let q be I will study discrete math. Let r be I will get an A. If it snows, then I will study discrete math. If I study discrete math, I will get an A. Therefore, If it snows, I will get an A. 7

Disjunctive Syllogism Corresponding Tautology: ( p (p q)) q Example: Let p be I will study discrete math. Let q be I will study English literature. I will study discrete math or I will study English literature. I will not study discrete math. Therefore, I will study English literature. 8

Addition Corresponding Tautology: p (p q) Example: Let p be I will study discrete math. Let q be I will visit Las Vegas. I will study discrete math. Therefore, I will study discrete math or I will visit Las Vegas. 9

Simplification Corresponding Tautology: (p q) p Example: Let p be I will study discrete math. Let q be I will study English literature. I will study discrete math and English literature Therefore, I will study discrete math. 10

Conjunction Corresponding Tautology: ((p) (q)) (p q) Example: Let p be I will study discrete math. Let q be I will study English literature. I will study discrete math. I will study English literature. Therefore, I will study discrete math and I will study English literature. 11

Resolution Resolution plays an important role in AI and is used in Prolog. Corresponding Tautology: (( p r ) (p q)) (q r) Example: Let p be I will study discrete math. Let r be I will study English literature. Let q be I will study databases. I will not study discrete math or I will study English literature. I will study discrete math or I will study databases. Therefore, I will study databases or I will English literature. 12

Using the Rules of Inference to Build Valid Arguments A valid argument is a sequence of statements. Each statement is either a premise or follows from previous statements by rules of inference. The last statement is called conclusion. A valid argument takes the following form: S 1 S 2... S n C 13

Valid Arguments Example 1: From the single proposition Show that q is a conclusion. Solution: Simplification 14

Valid Arguments Example 2: With these hypotheses: It is not sunny this afternoon and it is colder than yesterday. We will go swimming only if it is sunny. If we do not go swimming, then we will take a canoe trip. If we take a canoe trip, then we will be home by sunset. Using the inference rules, construct a valid argument for the conclusion: We will be home by sunset. Solution: 1. Choose propositional variables: p : It is sunny this afternoon. r : We will go swimming. t : We will be home by sunset. q : It is colder than yesterday. s : We will take a canoe trip. 2. Translation into propositional logic: Continued on next slide 15

Valid Arguments 3. Construct the Valid Argument 16

Handling Quantified Statements Valid arguments for quantified statements are a sequence of statements. Each statement is either a premise or follows from previous statements by rules of inference which include: Rules of Inference for Propositional Logic Rules of Inference for Quantified Statements The rules of inference for quantified statements are introduced in the next several slides. 17

Universal Instantiation (UI) Example: Our domain consists of all dogs and Fido is a dog. All dogs are cuddly. Therefore, Fido is cuddly. 18

Universal Generalization (UG) Used often implicitly in Mathematical Proofs. 19

Existential Instantiation (EI) Example: There is someone who got an A in the course. Let s call her a and say that a got an A 20

Existential Generalization (EG) Example: Michelle got an A in the class. Therefore, someone got an A in the class. 21

Universal Modus Ponens Universal Modus Ponens combines universal instantiation and modus ponens into one rule. This rule could be used in the Socrates example. 22

Using Rules of Inference Example 2: Use the rules of inference to construct a valid argument showing that the conclusion Someone who passed the first exam has not read the book. follows from the premises A student in this class has not read the book. Everyone in this class passed the first exam. Solution: Let C(x) denote x is in this class, B(x) denote x has read the book, and P(x) denote x passed the first exam. First we translate the premises and conclusion into symbolic form. Continued on next slide 23

Using Rules of Inference Valid Argument: 24