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Intro to Discrete Structures

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1 Intro to Discrete Structures
Spring 2014 Text: Mathematical Structures for Computer Science

2 Course Overview Discrete versus continuous Major topics:
Develop abstract thinking through the study of logical arguments, proofs Develop recursive thinking, which provides problem solving tools such as recurrence relations and induction Algorithms & analysis (explored more fully in CS 317) Combinatorial techniques for counting objects Discrete structures such as graphs, sets, functions, trees, CS Chapter 1

3 1.1: Statements, Symbolic Representation, and Tautologies
Formal Logic: Chapter 1 1.1: Statements, Symbolic Representation, and Tautologies

4 Why Study Formal Logic? Natural languages are ambiguous
Formal logical proofs are based on the structure of logical expressions, or well-formed formulas, not the semantics Do conclusions follow necessarily from the truth of a set of initial statements. This is deductive reasoning A valid deduction can still be wrong, however, if the logical statements that are its initial assumptions are actually false. CS Chapter 1

5 Applications of Formal Logic
Proof of program correctness Not easy, but can be done in limited cases Digital circuit design Formal specification of computer systems or programming languages Conditional statements are based on logic CS Chapter 1

6 Formal Logic: 1.1 – Overview
Contents Connectives and Truth Values Tautologies Logical connectives in the real world Objectives Learn to use formal symbols of propositional logic Find the truth value of expressions in propositional logic CS Chapter 1

7 Statements, Connectives, and Truth Tables
Statement (proposition): a sentence that is either true or false (but not both at the same time) e.g. “Jane is a doctor” but not “Are you happy?” or “It is pretty” Often used to represent facts TRUE and FALSE (T/F, 1/0) are absolute – not “mostly true” or “almost always false” Simple statements are represented by letters from the beginning of the alphabet: A, B, C, … CS Chapter 1

8 Statements, Connectives, and Truth Tables
Logical connectives are used to combine several simple statements into compound statements. e.g., “The sun is shining and the temperature is 80” AND and OR are common connectors - binary If A and B are two simple statements, then A  B or A  B are compound statements. NOT is a logical modifier that changes the truth value of whatever it modifies (unary connective) If A is true, then A’ (or !A ) is false CS Chapter 1

9 Connectives and Truth Tables
F A B A v B T F A ! A T F NEGATION CONJUNCTION DISJUNCTION Truth tables compute the value of logical expressions based on the truth values of their components. They consist of one column for each variable (statement letters, in our case) and one column for the result of each logical computation that the table represents. The number of rows is a function of the number of variables. There is one row for each possible combination of truth values for the variables. For example with two variables, A & B, the combinations are A = T, B = T, A = T, B = F, etc. For two variables, 22 = 4 rows, for 3 variables, 23 rows, … CS Chapter 1

10 Connectives and Truth Tables
Implication is a logical connective that represents the combination “If A then B” In logic it is represented as “A → B”, read as “A implies B” A is the antecedent, B is the consequent Logic interpretation somewhat different from natural language interpretation Here we ask “is it true that A implies B?” The result depends on the truth values of A and B If A is false, what can we conclude? CS Chapter 1

11 Example What if A = “you make an A on the final exam” B = “you will get an A in the course” A → B “If you make an A on the final exam you will get an A in the course” When is this statement true, and when is it false? What is the truth value of A → B if A = T and B = T ? If A = T and B = F? If A = F and B = T? If A = F and B = F? CS Chapter 1

12 Connectives and Truth Tables
F T ?? B A B→A T F T ?? A→B B→A (A→B) && (B→A) T F Equivalence 1 Logical values are always either true or false, never “I don’t know”, so by convention we assume that if the antecedent of an implication is false the consequent is true. See previous slide. The equivalence relation is written as A ↔ B. A & B are equivalent if it is true that whenever “A implies B” is true, it is also true that “B implies A”. Another way to state this relation is “B is true if and only if A is true” or “A is true if and only if B is true” Notice that A ↔ B only when A and B have the same truth value. A B A ↔ B T F Equivalence 2 simplified CS Chapter 1

13 English to Logic See Table 1.5 for correspondence between words in English and logical connectives. Work some examples on pages 4 & 5 CS Chapter 1

14 Well-formed Formulas (wff’s)
The basic tokens in logical statements are the statement letters, connector symbols AND, OR, NEGATION, IMPLICATION, & parentheses Syntax is governed by precedence rules; from highest to lowest they are Negation AND and OR Implication Equivalence Parentheses can override the normal order, just as in programming languages. Syntactically correct statements are sometimes called well-formed formulas (wff) CS Chapter 1

15 Examples of wff’s A  B’ represents A  (B’), not (A  B)’
A  B → C means (A  B) → C Well formed formulas are represented by letters from the end of the alphabet: P, Q, R, … For example, A  B → C can be written as P → Q It is often easier to simplify around the main connector (the operator that is evaluated last, or that has the lowest precedence). CS Chapter 1

16 Tautologies & Contradictions
A tautology is a statement that is always true, regardless of the truth values assigned to its variables Example: A  A’ A contradiction is a statement that is always false Example: A  A’ Contradictions are usually represented by 0 and tautologies by 1 CS Chapter 1

17 Truth Table for a Tautology
CS Chapter 1

18 Tautological Equivalences
Commutativity A  B  B  A A  B  B  A Associativity (A  B )  C  A  (B  C) (A  B )  C  A  (B  C) Distribution A  (B  C)  A  (B  C)  (A  B )  (A  C ) (A  B )  (A  C ) Identity A  0  A A  1  A Complement A  A’  A  A’  0 Equivalents: the left side may be substituted for the right, and vice versa Tautologies: the equivalences above are also tautologies; always true, regardless of the truth values assigned to A, B, C. (See Table 1.9 on page 9 for examples) Similar relations exist in mathematics (6 + 3 = 3 + 6, for example) For each pair of relations, the relation on the left is the dual of the relation on the right – and vice versa It’s useful to be familiar with equivalences because in constructing logical arguments (or even writing conditions in a computer program), one may be easier to understand than another. CS Chapter 1

19 DeMorgan’s Laws (A  B)’ A’  B’ (A  B)’ A’  B’
DeMorgan’s laws are helpful in forming the negation of compound statements. Example: suppose P is “ice cream is cold and sweet” Negation? “Ice cream is not cold and sweet” No; P should really be “ice cream is cold and ice cream is sweet”. The negation is “Ice cream is not cold OR ice cream is not sweet” Be careful to think in terms of complete statements – this is an example of fuzzy natural language meaning, a reason for using logical notation to avoid ambiguity. CS Chapter 1

20 Why Do We Care? Equivalences allow us to choose the easiest way to write a particular logical condition Logical connectives are useful in building precise descriptions of things – example: refining a web search e.g. recipe AND chicken AND easy (or “recipe + chicken + easy” ) CS Chapter 1

21 Algorithm An algorithm is a set of instructions that can be mechanically executed in a finite amount of time in order to solve some problem. Important features: Finiteness “Mechanical” : the steps are clearly described and can be executed by hand or on a computer Algorithms are often described in pseudocode See the example on page 12: an algorithm to determine if, given two wffs P and Q, is P → Q a tautology. CS Chapter 1

22 Preview of 1.2 – Propositional Logic
In this section we’ll see how to use wffs to perform deductive reasoning. Propositional logic (also called propositional calculus or statement logic) is a formal system for reaching logical conclusions derived from a given set of statements, or facts. First homework will be assigned. CS Chapter 1

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