# Computational Logic. Recall of First-Order Logic. Damiano Zanardini

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Computational Logic Recall of First-Order Logic Damiano Zanardini UPM European Master in Computational Logic (EMCL) School of Computer Science Technical University of Madrid Academic Year 2009/2010 D. Zanardini Computational Logic Ac. Year 2009/ / 1

2 Syntax of a first-order languagep An alphabet A consists of variable symbols: x, y, z, w, x,... function symbols: f ( ), g(, ),... (arity = no. of args: f /1, g/2) predicate symbols: p( ), q(, ),..., = /2 connectives:,,,, quantifiers:, constants (a, b, c,... ) = 0-arity functions propositions = 0-arity predicates Metalanguage F and G denote arbitrary formulæ D. Zanardini Computational Logic Ac. Year 2009/ / 1

3 Syntax of a first-order languagep variable symbols: x, y, z constant (0-ary functions) symbols: a, b, c, tom, 0, 1 function symbols: f /1, g/2 predicate symbols: p/0, q/2, cat/1, +/3 Terms a variable is a term if t 1,..., t n are terms and f is an n-ary (n 0, thus including constants) function symbol, then f (t 1,..., t n ) is a term Examples a f (tom) f (1 a() a(1) f (2) g(g, g(1)) q(0, f (1)) a + y g(1, f (a)) g(0c) cat(cat(0)) +(a, f (z), c) f (f (f (x))) D. Zanardini Computational Logic Ac. Year 2009/ / 1

4 Syntax of a first-order languagep Atoms variable symbols: x, y, z constant (0-ary functions) symbols: a, b, c, tom, 0, 1 function symbols: f /1, g/2 predicate symbols: p/0, q/2, cat/1, +/3 if t 1,..., t n are terms and p is an n-ary (n 0) predicate symbol, then p(t 1,..., t n ) is an atom Examples cat(g(x, y)) p q q( xp(x), xp(f (x))) q(p, p) f (q(a, a)) q(a, f (1)) +(a, f (z), c) q(0,, z) cat(a, 1) cat(cat(f (1))) D. Zanardini Computational Logic Ac. Year 2009/ / 1

5 Syntax of a first-order languagep variable symbols: x, y, z constant (0-ary functions) symbols: a, b, c, tom, 0, 1 function symbols: f /1, g/2 predicate symbols: p/0, q/2, cat/1, +/3 Formulæ an atom is a formula if F and G are formulæ, and x is a variable, then F, (F G), (F G), (F G), (F G), xf and xf are also formulæ Examples (p q(a, f (x))) p zf (1) a(q(a, 0) g(0, a)) p ( cat(a) ( xp ycat(y))) q(a, b) tom D. Zanardini Computational Logic Ac. Year 2009/ / 1

6 Syntax of a first-order languagep Literals A literal is an atom or the negation of an atom p, p, q(a, f (1)), q(a, f (1)), cat(g(x, y)), cat(g(x, y)) Precedence parentheses give an order between operators, but can make a formula quite unreadable we can use an order of precedence between operators in order to remove some parentheses without introducing ambiguity ((p q) (p r)) p q p r p ( q p) r ((p q) p) r (p q) (p r) p ( q p r) D. Zanardini Computational Logic Ac. Year 2009/ / 1

7 Syntax of a first-order languagep Literals A literal is an atom or the negation of an atom p, p, q(a, f (1)), q(a, f (1)), cat(g(x, y)), cat(g(x, y)) Precedence parentheses give an order between operators, but can make a formula quite unreadable we can use an order of precedence between operators in order to remove some parentheses without introducing ambiguity ((p q) (p r)) p q p r p ( q p) r ((p q) p) r (p q) (p r) p ( q p r) D. Zanardini Computational Logic Ac. Year 2009/ / 1

8 Syntax of a first-order languagep Literals A literal is an atom or the negation of an atom p, p, q(a, f (1)), q(a, f (1)), cat(g(x, y)), cat(g(x, y)) Precedence parentheses give an order between operators, but can make a formula quite unreadable we can use an order of precedence between operators in order to remove some parentheses without introducing ambiguity {,, } higher precedence than {, } {, } higher precedence than {, } the scope of in yp(y) q(y) is only p(y), not the whole formula D. Zanardini Computational Logic Ac. Year 2009/ / 1

9 Syntax of a first-order languagep Free and bounded variable occurrences an occurrence of the variable x is bounded in F if it is in the scope of a quantifier x or x x(p(1, x, y) q(x)) q(x) otherwise, it is said to be free x(p(1, x, y) q(x)) q(x) a formula if closed if it contains no free occurrences occurrences, not variable symbols, can be free or bounded D. Zanardini Computational Logic Ac. Year 2009/ / 1

10 Syntax of a first-order languagep Substitution F (x) denotes a formula where x occurs free somewhere F (x/t) denotes a formula where every free occurrence of x has been replaced by a term t provided x does not occur free in the scope of any y or y for y occurring in t F s(x) ( y(p(x) q(y))) F (x/f (y, y)) cannot be done xp(x) is the same as yp(y) (general result: xf (x) yf (x/y)) if you are not sure about the name of variables, it is never a mistake to rename all the bounded occurrences of a variable D. Zanardini Computational Logic Ac. Year 2009/ / 1

11 Syntax of a first-order languagep Alternative notation &,, xf x. F, x F xf x. F, x F p, q, r P, Q, R More than first-order second-order logic: it allows quantification over functions and predicates (e.g., mathematical induction) p(p(0) k(p(k) p(s(k))) np(n)) higher-order logic allows quantification over functions and predicates of any order (as in functional programming) D. Zanardini Computational Logic Ac. Year 2009/ / 1

12 Semantics of a first-order languagep Interpretations An interpretation I is a pair (D, I ), where D is a set (the domain of the universe) and I maps symbols to individuals or functions constants: I (a) = d D variables: I (x) = d D functions: I (f /n) = F : D n D I (f (t 1,..., t n)) = F(I (t 1),..., I (t n)) = F(d 1,..., d n) D predicates: I (p/n) = P : D n {t, f} I (p(t 1,..., t n)) = P(I (t 1),..., I (t n)) = P(d 1,..., d n) {t, f} an interpretation assigns an element of D to any term, and a truth value to any predicate applied to terms P is an n-ary relation R: P(d 1,..., d n ) = t iff d 1,..., d n R D. Zanardini Computational Logic Ac. Year 2009/ / 1

13 Semantics of a first-order languagep Evaluation of a formula Assigning a truth value to a formula, according to: the chosen interpretation of constants, functions and predicates the rules for evaluation (see also truth tables) I ( F ) = t iff I (F ) = f I (F G) = t iff I (F ) = I (G) = t I (F G) = f iff I (F ) = I (G) = f I (F G) = f iff I (F ) = t and I (G) = f I (F G) = t iff I (F ) = I (G) I ( xf (x)) = t iff I (F (x/c)) = t for every constant c I ( xf (x)) = t iff I (F (x/c)) = t for at least one constant c it is required that every element d of D is denoted by at least one constant I (instead of I) will often denote an interpretation when D is clear D. Zanardini Computational Logic Ac. Year 2009/ / 1

14 Semantics of a first-order languagep Example (propositional): (p q) (q r) r first interpretation: I (p) = f, I (q) = f, I (r) = f (f f) (f f) f t t f t f f D. Zanardini Computational Logic Ac. Year 2009/ / 1

15 Semantics of a first-order languagep Example (propositional): (p q) (q r) r second interpretation: I (p) = f, I (q) = f, I (r) = t (f f) (f t) t t t t t t t D. Zanardini Computational Logic Ac. Year 2009/ / 1

16 Semantics of a first-order languagep Example (propositional): (p q) (q r) r this example only needs truth tables, for all possible interpretations p q r p q q r (p q) (q r) (p q) (q r) r t t t t t t t t t f t f f t t f t f t f t t f f f t f t f t t t t t t f t f t f f t f f t t t t t f f f t t t f D. Zanardini Computational Logic Ac. Year 2009/ / 1

17 Semantics of a first-order languagep Example (first-order): x(m(a, x) p(x)) yq(s(y)) first interpretation: D = {0, 1, 2, 3,..} I (a) = 0 I (s(x)) = S(I (x)) = the successor of I (x) p(x) means that x is even q(x) means that x is odd m(x, y) means that x < y I evaluates the formula to t (try it!) D. Zanardini Computational Logic Ac. Year 2009/ / 1

18 Semantics of a first-order languagep Example (first-order): x(m(a, x) p(x)) yq(s(y)) second interpretation: D =,,, I (a) = x s(x) x p(x) t t t and this evaluates to f x q(x) t f t m y x t t t f f t t f f D. Zanardini Computational Logic Ac. Year 2009/ / 1

19 Semantics of a first-order languagep Satisfiable formulæ An interpretation I = (D, I ) satisfies a formula F on D iff I (F ) = t (also written I(F ) = t). In this case, I is a model of F F is satisfiable (written SAT (F )) iff it has at least one model F is unsatisfiable (written UNSAT (F )) iff it has no models that is, all interpretations are countermodels F is valid (written VAL(F )) iff every interpretation is a model this is denoted by = F, and amounts to say UNSAT ( F ) With a set of formulæ {F 1,.., F n }: (D, I ) satisfies {F 1,.., F n } iff I (F i ) = t on D for every i {F 1,.., F n } is satisfiable iff there is such an interpretation Example: x(m(a, x) p(x)) yq(s(y)) this formula is satisfiable but not valid D. Zanardini Computational Logic Ac. Year 2009/ / 1

20 Logical consequencep Logical consequence Given a set of formulæ Γ = {F 1,.., F n } and a formula G over the same language, G is a logical consequence of Γ (written Γ = G) iff every interpretation satisfying Γ also satisfies G, or, equivalently, there is no interpretation which satisfies Γ but not G Important (in some sense, it is a matter of convenience) {F 1,.., F n } = G iff = (F 1.. F n ) G To decide Γ = G can be very hard We have to take all models of Γ and verify that they all satisfy G, or find a counterexample D. Zanardini Computational Logic Ac. Year 2009/ / 1

21 Logical consequencep Example: {p (q r), p q} = r equivalent to = ((p (q r)) (p q)) r p q r p (q r) p q (p (q r)) (p q) G t t t t t t t t t f f t f t t f t t f f t t f f t f f t f t t t f f t f t f t f f t f f t t f f t f f f t f f t D. Zanardini Computational Logic Ac. Year 2009/ / 1

22 Logical consequencep Example: {p (q r), p q} = r equivalent to = ((p (q r)) (p q)) r p q r p (q r) p q (p (q r)) (p q) G t t t t t t f t t f f t f t t f t t f f t t f f t f f t f t t t f f t f t f t f f t f f t t f f t f f f t f f t D. Zanardini Computational Logic Ac. Year 2009/ / 1

23 Logical consequencep Example: { xp(x), xq(x)} = x(p(x) q(x)) D = {1, 2, 3, 4,..} p(x): x is even q(x): x is odd It is easy to see that this interpretation makes both premises true (indeed, there exist even numbers and there exist odd numbers), but does not satisfy the conclusion (no numbers are both even and odd) I ( xp(x)) = t I ( xq(x)) = t I ( x(p(x) q(x))) = f Therefore, this deduction is incorrect Example: { xp(x), xq(x)} = x(p(x) q(x)) this is, of course, a correct deduction (or valid, see a few slides above) D. Zanardini Computational Logic Ac. Year 2009/ / 1

24 Syntax vs. semanticsp Formal systems A proof formal system consists of: a formal language (alphabet and rules for building formulæ) a set of logical axioms (i.e., valid formulæ, which do not require proof) a set of inference rules for proving new formulæ a definition of proof Theories A theory T is a formal system extended with a set Γ of non-logical axioms (i.e., formulæ taken for granted) T [Γ] if Γ =, then T is the basic theory of the formal system D. Zanardini Computational Logic Ac. Year 2009/ / 1

25 Syntax vs. semanticsp Proofs A proof of a formula G in a theory T [Γ] (written T [Γ] G) is a finite sequence of formulæ such that each formula of the sequence is either a logical or non-logical axiom of the theory; or the result of applying an inference rule to previous formulæ in the sequence G is the last formula in the sequence Theorems (of a theory) a theorem of the theory T [Γ] is a formula for which there is at least one proof in T [Γ] D. Zanardini Computational Logic Ac. Year 2009/ / 1

26 Syntax vs. semanticsp Proof example: T [p (q r), p q] r ❶ p (q r) first premise ❷ p ( q r) interdefinition of, and on ❶ ❸ ( p q) r associativity on ❷ ❹ (p q) r De Morgan on ❸ ❺ p q r interdefinition of, and on ❹ ❻ p q second premise ❼ r modus ponens on ❺, ❻ Another approach to prove validity Instead of looking at all the possible models of a formula, we exploited our knowledge of logical rules we also say that ((p (q r)) (p q)) r is a tautology D. Zanardini Computational Logic Ac. Year 2009/ / 1

27 Syntax vs. semanticsp Theorem (Validity) Every theorem of T is logically valid: if T G then = G Theorem (Completeness) In a first-order theory T, all valid formulæ are theorems of T : if = G then T G the rest of the course will be basically about finding such formulæ Theorem (Deduction) T [F 1,.., F n ] G iff T (F 1.. F n ) G D. Zanardini Computational Logic Ac. Year 2009/ / 1

28 Syntax vs. semanticsp Theorem (Validity) Every theorem of T is logically valid: if T G then = G Theorem (Completeness) In a first-order theory T, all valid formulæ are theorems of T : if = G then T G the rest of the course will be basically about finding such formulæ Theorem (Deduction) T [F 1,.., F n ] G iff T (F 1.. F n ) G T [F 1,.., F n ] G iff VAL((F 1.. F n ) G) D. Zanardini Computational Logic Ac. Year 2009/ / 1

29 Syntax vs. semanticsp Theorem (Validity) Every theorem of T is logically valid: if T G then = G Theorem (Completeness) In a first-order theory T, all valid formulæ are theorems of T : if = G then T G the rest of the course will be basically about finding such formulæ Theorem (Deduction) T [F 1,.., F n ] G iff T (F 1.. F n ) G T [F 1,.., F n ] G iff VAL((F 1.. F n ) G) T [F 1,.., F n ] G iff UNSAT (F 1.. F n G) D. Zanardini Computational Logic Ac. Year 2009/ / 1

30 Syntax vs. semanticsp Completeness vs. Incompleteness in theories where Gödel s first incompleteness theorem holds, the completeness theorem (also Gödel s) holds as well incompleteness: an effectively generated (whose set of axioms is a recursively enumerable set) theory which is powerful enough to express elementary arithmetic cannot be both consistent (there is no statement so that both the statement and its negation are provable from the axioms) and complete (for any statement in the axioms language, either that statement or its negation is provable from the axioms) there is a statement which is true in the theory, but cannot be proven nor disproven (the Gödel sentence) but such statement is not a logical consequence of the theory (i.e., there are non-standard models, not isomorphic to the standard one, of the theory where it does not hold) D. Zanardini Computational Logic Ac. Year 2009/ / 1

31 What you ABSOLUTELY have to getp Pay attention to these sentences (do they make sense?) the interpretation I is satisfiable there exists a valid model for F a formula F makes an interpretation I false in order to prove that a formula is satisfiable, we need a true interpretation a literal is a negated or non-negated term we are not asking if these sentences are true or false, only if they make sense D. Zanardini Computational Logic Ac. Year 2009/ / 1

32 What you ABSOLUTELY have to getp Names and individuals Names are only there to denote things there is no problem in taking female or even odd(4) to be true if we are always consistent about it never forget that you are dealing with symbols! D. Zanardini Computational Logic Ac. Year 2009/ / 1

33 What you ABSOLUTELY have to getp Types in first-order logic! Equality a function of arity n will never have arity m n in the same formal system a predicate of arity n will never have arity m n in the same formal system a function is not a predicate a predicate is not a function the predicate = /2 is special in the sense that its meaning is often given for granted in a formal system (i.e., being equal means being the same element of the domain) anyway, we could choose to redefine it! D. Zanardini Computational Logic Ac. Year 2009/ / 1

34 What you ABSOLUTELY have to getp Interpretations and imagination When we want to prove that a formula is not valid, we need one interpretation which makes it false you can choose anything you want as D and I it s ok if we take that function s/1 to map 45 to! D. Zanardini Computational Logic Ac. Year 2009/ / 1

35 What you ABSOLUTELY have to getp Implication that the left-hand side of an implication is false is enough to say that the implication is true that the left-hand side of an implication is false is enough to say that the implication is true that the left-hand side of an implication is false is enough to say that the implication is true that the left-hand side of an implication is false is enough to say that the implication is true that the left-hand side of an implication is false is enough to say that the implication is true that the left-hand side of an implication is false is enough to say that the implication is true that the left-hand side of an implication is false is enough to say that the implication is true D. Zanardini Computational Logic Ac. Year 2009/ / 1

36 What you ABSOLUTELY have to getp Implication, cont. all red apples are good x((apple(x) red(x)) good(x)) there are apples which are red and good all apples are red and good there are no apples which are red but not good the set of good apples is a superset of the set of red apples whenever an apple is not good, it cannot be red whenever an apple is not good, it must be red there exists a yellow apple which is bad x(apple(x) yellow(x) bad(x)) whenever an apple is yellow, it is bad there is at least an object which is bad and yellow, and is an apple every time an apple is good, it is not yellow D. Zanardini Computational Logic Ac. Year 2009/ / 1

37 What you ABSOLUTELY have to getp Implication, cont. all red apples are good x((apple(x) red(x)) good(x)) there are apples which are red and good NO all apples are red and good NO there are no apples which are red but not good YES the set of good apples is a superset of the set of red apples YES whenever an apple is not good, it cannot be red YES whenever an apple is not good, it must be red NO there exists a yellow apple which is bad x(apple(x) yellow(x) bad(x)) whenever an apple is yellow, it is bad NO there is at least an object which is bad and yellow, and is an apple YES every time an apple is good, it is not yellow NO D. Zanardini Computational Logic Ac. Year 2009/ / 1

### Part I: Propositional Calculus

Logic Part I: Propositional Calculus Statements Undefined Terms True, T, #t, 1 False, F, #f, 0 Statement, Proposition Statement/Proposition -- Informal Definition Statement = anything that can meaningfully

### Predicate Logic. Andreas Klappenecker

Predicate Logic Andreas Klappenecker Predicates A function P from a set D to the set Prop of propositions is called a predicate. The set D is called the domain of P. Example Let D=Z be the set of integers.

### Examples: P: it is not the case that P. P Q: P or Q P Q: P implies Q (if P then Q) Typical formula:

Logic: The Big Picture Logic is a tool for formalizing reasoning. There are lots of different logics: probabilistic logic: for reasoning about probability temporal logic: for reasoning about time (and

### First-Order Predicate Logic. Basics

First-Order Predicate Logic Basics 1 Syntax of predicate logic: terms A variable is a symbol of the form x i where i = 1, 2, 3.... A function symbol is of the form fi k where i = 1, 2, 3... und k = 0,

### First-Order Logic. 1 Syntax. Domain of Discourse. FO Vocabulary. Terms

First-Order Logic 1 Syntax Domain of Discourse The domain of discourse for first order logic is FO structures or models. A FO structure contains Relations Functions Constants (functions of arity 0) FO

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

3. The Logic of Quantified Statements Summary Aaron Tan 28 31 August 2017 1 3. The Logic of Quantified Statements 3.1 Predicates and Quantified Statements I Predicate; domain; truth set Universal quantifier,

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

Intelligent Agents First Order Logic Ute Schmid Cognitive Systems, Applied Computer Science, Bamberg University last change: 19. Mai 2015 U. Schmid (CogSys) Intelligent Agents last change: 19. Mai 2015

### Computational Logic. Standardization of Interpretations. Damiano Zanardini

Computational Logic Standardization of Interpretations Damiano Zanardini UPM European Master in Computational Logic (EMCL) School of Computer Science Technical University of Madrid damiano@fi.upm.es Academic

### Logic: The Big Picture

Logic: The Big Picture A typical logic is described in terms of syntax: what are the legitimate formulas semantics: under what circumstances is a formula true proof theory/ axiomatization: rules for proving

### Predicate Logic: Sematics Part 1

Predicate Logic: Sematics Part 1 CS402, Spring 2018 Shin Yoo Predicate Calculus Propositional logic is also called sentential logic, i.e. a logical system that deals with whole sentences connected with

### Introduction to Sets and Logic (MATH 1190)

Introduction to Sets Logic () Instructor: Email: shenlili@yorku.ca Department of Mathematics Statistics York University Sept 18, 2014 Outline 1 2 Tautologies Definition A tautology is a compound proposition

### Logic: Propositional Logic (Part I)

Logic: Propositional Logic (Part I) Alessandro Artale Free University of Bozen-Bolzano Faculty of Computer Science http://www.inf.unibz.it/ artale Descrete Mathematics and Logic BSc course Thanks to Prof.

### Introduction to Metalogic

Philosophy 135 Spring 2008 Tony Martin Introduction to Metalogic 1 The semantics of sentential logic. The language L of sentential logic. Symbols of L: Remarks: (i) sentence letters p 0, p 1, p 2,... (ii)

### Propositional Logic: Syntax

Logic Logic is a tool for formalizing reasoning. There are lots of different logics: probabilistic logic: for reasoning about probability temporal logic: for reasoning about time (and programs) epistemic

### Syntax. Notation Throughout, and when not otherwise said, we assume a vocabulary V = C F P.

First-Order Logic Syntax The alphabet of a first-order language is organised into the following categories. Logical connectives:,,,,, and. Auxiliary symbols:.,,, ( and ). Variables: we assume a countable

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

COMP 182 Algorithmic Thinking Proofs Luay Nakhleh Computer Science Rice University 1 Reading Material Chapter 1, Section 3, 6, 7, 8 Propositional Equivalences The compound propositions p and q are called

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

Mathematics 114L Spring 2018 D.A. Martin Mathematical Logic 1 First-Order Languages. Symbols. All first-order languages we consider will have the following symbols: (i) variables v 1, v 2, v 3,... ; (ii)

### Outline. Formale Methoden der Informatik First-Order Logic for Forgetters. Why PL1? Why PL1? Cont d. Motivation

Outline Formale Methoden der Informatik First-Order Logic for Forgetters Uwe Egly Vienna University of Technology Institute of Information Systems Knowledge-Based Systems Group Motivation Syntax of PL1

### Computational Logic. Davide Martinenghi. Spring Free University of Bozen-Bolzano. Computational Logic Davide Martinenghi (1/26)

Computational Logic Davide Martinenghi Free University of Bozen-Bolzano Spring 2010 Computational Logic Davide Martinenghi (1/26) Propositional Logic - algorithms Complete calculi for deciding logical

### Propositional Logic Not Enough

Section 1.4 Propositional Logic Not Enough If we have: All men are mortal. Socrates is a man. Does it follow that Socrates is mortal? Can t be represented in propositional logic. Need a language that talks

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

Applied Logic Lecture 1 - Propositional logic Marcin Szczuka Institute of Informatics, The University of Warsaw Monographic lecture, Spring semester 2017/2018 Marcin Szczuka (MIMUW) Applied Logic 2018

### Learning Goals of CS245 Logic and Computation

Learning Goals of CS245 Logic and Computation Alice Gao April 27, 2018 Contents 1 Propositional Logic 2 2 Predicate Logic 4 3 Program Verification 6 4 Undecidability 7 1 1 Propositional Logic Introduction

### Advanced Topics in LP and FP

Lecture 1: Prolog and Summary of this lecture 1 Introduction to Prolog 2 3 Truth value evaluation 4 Prolog Logic programming language Introduction to Prolog Introduced in the 1970s Program = collection

### Propositional Logic: Models and Proofs

Propositional Logic: Models and Proofs C. R. Ramakrishnan CSE 505 1 Syntax 2 Model Theory 3 Proof Theory and Resolution Compiled at 11:51 on 2016/11/02 Computing with Logic Propositional Logic CSE 505

### Predicate Calculus - Syntax

Predicate Calculus - Syntax Lila Kari University of Waterloo Predicate Calculus - Syntax CS245, Logic and Computation 1 / 26 The language L pred of Predicate Calculus - Syntax L pred, the formal language

### 03 Review of First-Order Logic

CAS 734 Winter 2014 03 Review of First-Order Logic William M. Farmer Department of Computing and Software McMaster University 18 January 2014 What is First-Order Logic? First-order logic is the study of

### First Order Logic: Syntax and Semantics

CS1081 First Order Logic: Syntax and Semantics COMP30412 Sean Bechhofer sean.bechhofer@manchester.ac.uk Problems Propositional logic isn t very expressive As an example, consider p = Scotland won on Saturday

### Discrete Mathematics and Its Applications

Discrete Mathematics and Its Applications Lecture 1: The Foundations: Logic and Proofs (1.3-1.5) MING GAO DASE @ ECNU (for course related communications) mgao@dase.ecnu.edu.cn Sep. 19, 2017 Outline 1 Logical

### 06 From Propositional to Predicate Logic

Martin Henz February 19, 2014 Generated on Wednesday 19 th February, 2014, 09:48 1 Syntax of Predicate Logic 2 3 4 5 6 Need for Richer Language Predicates Variables Functions 1 Syntax of Predicate Logic

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

Logic Knowledge-based agents Inference engine Knowledge base Domain-independent algorithms Domain-specific content Knowledge base (KB) = set of sentences in a formal language Declarative approach to building

### Formal Logic: Quantifiers, Predicates, and Validity. CS 130 Discrete Structures

Formal Logic: Quantifiers, Predicates, and Validity CS 130 Discrete Structures Variables and Statements Variables: A variable is a symbol that stands for an individual in a collection or set. For example,

### First Order Logic (FOL) 1 znj/dm2017

First Order Logic (FOL) 1 http://lcs.ios.ac.cn/ znj/dm2017 Naijun Zhan March 19, 2017 1 Special thanks to Profs Hanpin Wang (PKU) and Lijun Zhang (ISCAS) for their courtesy of the slides on this course.

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

Přednáška 12 Důkazové kalkuly Kalkul Hilbertova typu 11/29/2006 Hilbertův kalkul 1 Formal systems, Proof calculi A proof calculus (of a theory) is given by: A. a language B. a set of axioms C. a set of

### Today. Proof using contrapositive. Compound Propositions. Manipulating Propositions. Tautology

1 Math/CSE 1019N: Discrete Mathematics for Computer Science Winter 2007 Suprakash Datta datta@cs.yorku.ca Office: CSEB 3043 Phone: 416-736-2100 ext 77875 Course page: http://www.cs.yorku.ca/course/1019

### First-Order Logic. Chapter Overview Syntax

Chapter 10 First-Order Logic 10.1 Overview First-Order Logic is the calculus one usually has in mind when using the word logic. It is expressive enough for all of mathematics, except for those concepts

### Introduction to Logic in Computer Science: Autumn 2006

Introduction to Logic in Computer Science: Autumn 2006 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Plan for Today Today s class will be an introduction

### 3. Only sequences that were formed by using finitely many applications of rules 1 and 2, are propositional formulas.

1 Chapter 1 Propositional Logic Mathematical logic studies correct thinking, correct deductions of statements from other statements. Let us make it more precise. A fundamental property of a statement is

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

CSCE 222 Discrete Structures for Computing Predicate Logic Dr. Hyunyoung Lee Based on slides by Andreas Klappenecker 1 Predicates A function P from a set D to the set Prop of propositions is called a predicate.

### Argument. whenever all the assumptions are true, then the conclusion is true. If today is Wednesday, then yesterday is Tuesday. Today is Wednesday.

Logic and Proof Argument An argument is a sequence of statements. All statements but the first one are called assumptions or hypothesis. The final statement is called the conclusion. An argument is valid

### Automated Reasoning Lecture 5: First-Order Logic

Automated Reasoning Lecture 5: First-Order Logic Jacques Fleuriot jdf@inf.ac.uk Recap Over the last three lectures, we have looked at: Propositional logic, semantics and proof systems Doing propositional

### Predicates, Quantifiers and Nested Quantifiers

Predicates, Quantifiers and Nested Quantifiers Predicates Recall the example of a non-proposition in our first presentation: 2x=1. Let us call this expression P(x). P(x) is not a proposition because x

### Predicate Calculus. Formal Methods in Verification of Computer Systems Jeremy Johnson

Predicate Calculus Formal Methods in Verification of Computer Systems Jeremy Johnson Outline 1. Motivation 1. Variables, quantifiers and predicates 2. Syntax 1. Terms and formulas 2. Quantifiers, scope

### Predicate Logic - Deductive Systems

CS402, Spring 2018 G for Predicate Logic Let s remind ourselves of semantic tableaux. Consider xp(x) xq(x) x(p(x) q(x)). ( xp(x) xq(x) x(p(x) q(x))) xp(x) xq(x), x(p(x) q(x)) xp(x), x(p(x) q(x)) xq(x),

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

CS 103 Discrete Structures Chapter 1 Propositional Logic Chapter 1.1 Propositional Logic 1 1.1 Propositional Logic Definition: A proposition :is a declarative sentence (that is, a sentence that declares

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

1. Define statement function of one variable. When it will become a statement? Statement function is an expression containing symbols and an individual variable. It becomes a statement when the variable

### COMP9414: Artificial Intelligence First-Order Logic

COMP9414, Wednesday 13 April, 2005 First-Order Logic 2 COMP9414: Artificial Intelligence First-Order Logic Overview Syntax of First-Order Logic Semantics of First-Order Logic Conjunctive Normal Form Wayne

### Propositional Logic: Review

Propositional Logic: Review Propositional logic Logical constants: true, false Propositional symbols: P, Q, S,... (atomic sentences) Wrapping parentheses: ( ) Sentences are combined by connectives:...and...or

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

Propositional Logic: Part II - Syntax & Proofs 0-0 Outline Syntax of Propositional Formulas Motivating Proofs Syntactic Entailment and Proofs Proof Rules for Natural Deduction Axioms, theories and theorems

### Logic. Propositional Logic: Syntax. Wffs

Logic Propositional Logic: Syntax Logic is a tool for formalizing reasoning. There are lots of different logics: probabilistic logic: for reasoning about probability temporal logic: for reasoning about

### For all For every For each For any There exists at least one There exists There is Some

Section 1.3 Predicates and Quantifiers Assume universe of discourse is all the people who are participating in this course. Also let us assume that we know each person in the course. Consider the following

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

(1/27) Description Logics Foundations of Propositional Logic Enrico Franconi franconi@cs.man.ac.uk http://www.cs.man.ac.uk/ franconi Department of Computer Science, University of Manchester (2/27) Knowledge

### Logic Part I: Classical Logic and Its Semantics

Logic Part I: Classical Logic and Its Semantics Max Schäfer Formosan Summer School on Logic, Language, and Computation 2007 July 2, 2007 1 / 51 Principles of Classical Logic classical logic seeks to model

### COMP219: Artificial Intelligence. Lecture 19: Logic for KR

COMP219: Artificial Intelligence Lecture 19: Logic for KR 1 Overview Last time Expert Systems and Ontologies Today Logic as a knowledge representation scheme Propositional Logic Syntax Semantics Proof

### Logic as a Tool Chapter 4: Deductive Reasoning in First-Order Logic 4.4 Prenex normal form. Skolemization. Clausal form.

Logic as a Tool Chapter 4: Deductive Reasoning in First-Order Logic 4.4 Prenex normal form. Skolemization. Clausal form. Valentin Stockholm University October 2016 Revision: CNF and DNF of propositional

### Predicate Logic. CSE 191, Class Note 02: Predicate Logic Computer Sci & Eng Dept SUNY Buffalo

Predicate Logic CSE 191, Class Note 02: Predicate Logic Computer Sci & Eng Dept SUNY Buffalo c Xin He (University at Buffalo) CSE 191 Discrete Structures 1 / 22 Outline 1 From Proposition to Predicate

### Propositional Logic. Logic. Propositional Logic Syntax. Propositional Logic

Propositional Logic Reading: Chapter 7.1, 7.3 7.5 [ased on slides from Jerry Zhu, Louis Oliphant and ndrew Moore] Logic If the rules of the world are presented formally, then a decision maker can use logical

### Logical Agents (I) Instructor: Tsung-Che Chiang

Logical Agents (I) Instructor: Tsung-Che Chiang tcchiang@ieee.org Department of Computer Science and Information Engineering National Taiwan Normal University Artificial Intelligence, Spring, 2010 編譯有誤

### MAI0203 Lecture 7: Inference and Predicate Calculus

MAI0203 Lecture 7: Inference and Predicate Calculus Methods of Artificial Intelligence WS 2002/2003 Part II: Inference and Knowledge Representation II.7 Inference and Predicate Calculus MAI0203 Lecture

### 2-4: The Use of Quantifiers

2-4: The Use of Quantifiers The number x + 2 is an even integer is not a statement. When x is replaced by 1, 3 or 5 the resulting statement is false. However, when x is replaced by 2, 4 or 6 the resulting

### Conjunction: p q is true if both p, q are true, and false if at least one of p, q is false. The truth table for conjunction is as follows.

Chapter 1 Logic 1.1 Introduction and Definitions Definitions. A sentence (statement, proposition) is an utterance (that is, a string of characters) which is either true (T) or false (F). A predicate is

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

First-Order Logic First-Order Theories Roopsha Samanta Partly based on slides by Aaron Bradley and Isil Dillig Roadmap Review: propositional logic Syntax and semantics of first-order logic (FOL) Semantic

### Logic: Propositional Logic Truth Tables

Logic: Propositional Logic Truth Tables Raffaella Bernardi bernardi@inf.unibz.it P.zza Domenicani 3, Room 2.28 Faculty of Computer Science, Free University of Bolzano-Bozen http://www.inf.unibz.it/~bernardi/courses/logic06

### Propositional logic (revision) & semantic entailment. p. 1/34

Propositional logic (revision) & semantic entailment p. 1/34 Reading The background reading for propositional logic is Chapter 1 of Huth/Ryan. (This will cover approximately the first three lectures.)

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

Discrete Math Review Discrete Math Review (Rosen, Chapter 1.1 1.6) TOPICS Propositional Logic Logical Operators Truth Tables Implication Logical Equivalence Inference Rules What you should know about propositional

### Supplementary Logic Notes CSE 321 Winter 2009

1 Propositional Logic Supplementary Logic Notes CSE 321 Winter 2009 1.1 More efficient truth table methods The method of using truth tables to prove facts about propositional formulas can be a very tedious

### Chapter 1 Elementary Logic

2017-2018 Chapter 1 Elementary Logic The study of logic is the study of the principles and methods used in distinguishing valid arguments from those that are not valid. The aim of this chapter is to help

### AAA615: Formal Methods. Lecture 2 First-Order Logic

AAA615: Formal Methods Lecture 2 First-Order Logic Hakjoo Oh 2017 Fall Hakjoo Oh AAA615 2017 Fall, Lecture 2 September 24, 2017 1 / 29 First-Order Logic An extension of propositional logic with predicates,

### Logical Agents. Outline

Logical Agents *(Chapter 7 (Russel & Norvig, 2004)) Outline Knowledge-based agents Wumpus world Logic in general - models and entailment Propositional (Boolean) logic Equivalence, validity, satisfiability

### Informal Statement Calculus

FOUNDATIONS OF MATHEMATICS Branches of Logic 1. Theory of Computations (i.e. Recursion Theory). 2. Proof Theory. 3. Model Theory. 4. Set Theory. Informal Statement Calculus STATEMENTS AND CONNECTIVES Example

### Overview. CS389L: Automated Logical Reasoning. Lecture 7: Validity Proofs and Properties of FOL. Motivation for semantic argument method

Overview CS389L: Automated Logical Reasoning Lecture 7: Validity Proofs and Properties of FOL Agenda for today: Semantic argument method for proving FOL validity Işıl Dillig Important properties of FOL

### Tools for reasoning: Logic. Ch. 1: Introduction to Propositional Logic Truth values, truth tables Boolean logic: Implications:

Tools for reasoning: Logic Ch. 1: Introduction to Propositional Logic Truth values, truth tables Boolean logic: Implications: 1 Why study propositional logic? A formal mathematical language for precise

### Gödel s Incompleteness Theorems

Seminar Report Gödel s Incompleteness Theorems Ahmet Aspir Mark Nardi 28.02.2018 Supervisor: Dr. Georg Moser Abstract Gödel s incompleteness theorems are very fundamental for mathematics and computational

### cse 311: foundations of computing Fall 2015 Lecture 6: Predicate Logic, Logical Inference

cse 311: foundations of computing Fall 2015 Lecture 6: Predicate Logic, Logical Inference quantifiers x P(x) P(x) is true for every x in the domain read as for all x, P of x x P x There is an x in the

### Logic for Computer Scientists

Logic for Computer Scientists Pascal Hitzler http://www.pascal-hitzler.de CS 499/699 Lecture, Winter Quarter 2011 Wright State University, Dayton, OH, U.S.A. [final version: 03/10/2011] Contents 1 Propositional

### First Order Logic (FOL)

First Order Logic (FOL) Testing, Quality Assurance, and Maintenance Winter 2018 Prof. Arie Gurfinkel based on slides by Prof. Ruzica Piskac, Nikolaj Bjorner, and others References Chpater 2 of Logic for

### software design & management Gachon University Chulyun Kim

Gachon University Chulyun Kim 2 Outline Propositional Logic Propositional Equivalences Predicates and Quantifiers Nested Quantifiers Rules of Inference Introduction to Proofs 3 1.1 Propositional Logic

### COMP219: Artificial Intelligence. Lecture 19: Logic for KR

COMP219: Artificial Intelligence Lecture 19: Logic for KR 1 Overview Last time Expert Systems and Ontologies Today Logic as a knowledge representation scheme Propositional Logic Syntax Semantics Proof

### Logic for Computer Scientists

Logic for Computer Scientists Pascal Hitzler http://www.pascal-hitzler.de CS 499/699 Lecture, Spring Quarter 2010 Wright State University, Dayton, OH, U.S.A. Final version. Contents 1 Propositional Logic

185.A09 Advanced Mathematical Logic www.volny.cz/behounek/logic/teaching/mathlog13 Libor Běhounek, behounek@cs.cas.cz Lecture #1, October 15, 2013 Organizational matters Study materials will be posted

### 1.1 Language and Logic

c Oksana Shatalov, Spring 2018 1 1.1 Language and Logic Mathematical Statements DEFINITION 1. A proposition is any declarative sentence (i.e. it has both a subject and a verb) that is either true or false,

### Intelligent Agents. Pınar Yolum Utrecht University

Intelligent Agents Pınar Yolum p.yolum@uu.nl Utrecht University Logical Agents (Based mostly on the course slides from http://aima.cs.berkeley.edu/) Outline Knowledge-based agents Wumpus world Logic in

### Propositional Logic Language

Propositional Logic Language A logic consists of: an alphabet A, a language L, i.e., a set of formulas, and a binary relation = between a set of formulas and a formula. An alphabet A consists of a finite

### PREDICATE LOGIC: UNDECIDABILITY AND INCOMPLETENESS HUTH AND RYAN 2.5, SUPPLEMENTARY NOTES 2

PREDICATE LOGIC: UNDECIDABILITY AND INCOMPLETENESS HUTH AND RYAN 2.5, SUPPLEMENTARY NOTES 2 Neil D. Jones DIKU 2005 14 September, 2005 Some slides today new, some based on logic 2004 (Nils Andersen) OUTLINE,

### Predicate Calculus lecture 1

Predicate Calculus lecture 1 Section 1.3 Limitation of Propositional Logic Consider the following reasoning All cats have tails Gouchi is a cat Therefore, Gouchi has tail. MSU/CSE 260 Fall 2009 1 MSU/CSE

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

0/0/4 Knowledge based agents Logical Agents Agents need to be able to: Store information about their environment Update and reason about that information Russell and Norvig, chapter 7 Knowledge based agents

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

B.Y. Choueiry 1 Instructor s notes #12 Title: Logical Agents AIMA: Chapter 7 (Sections 7.4 and 7.5) Introduction to Artificial Intelligence CSCE 476-876, Fall 2018 URL: www.cse.unl.edu/ choueiry/f18-476-876

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

B.Y. Choueiry 1 Instructor s notes #12 Title: Logical Agents AIMA: Chapter 7 (Sections 7.4 and 7.5) Introduction to Artificial Intelligence CSCE 476-876, Fall 2017 URL: www.cse.unl.edu/ choueiry/f17-476-876

### 1 The Foundation: Logic and Proofs

1 The Foundation: Logic and Proofs 1.1 Propositional Logic Propositions( 명제 ) a declarative sentence that is either true or false, but not both nor neither letters denoting propositions p, q, r, s, T:

### Introduction to Logic

Introduction to Logic 1 What is Logic? The word logic comes from the Greek logos, which can be translated as reason. Logic as a discipline is about studying the fundamental principles of how to reason

### CSE 311: Foundations of Computing. Lecture 6: More Predicate Logic

CSE 311: Foundations of Computing Lecture 6: More Predicate Logic Last class: Predicates Predicate A function that returns a truth value, e.g., Cat(x) ::= x is a cat Prime(x) ::= x is prime HasTaken(x,

### First-Order Logic (FOL)

First-Order Logic (FOL) Also called Predicate Logic or Predicate Calculus 2. First-Order Logic (FOL) FOL Syntax variables x, y, z, constants a, b, c, functions f, g, h, terms variables, constants or n-ary

### Foundations of Mathematics MATH 220 FALL 2017 Lecture Notes

Foundations of Mathematics MATH 220 FALL 2017 Lecture Notes These notes form a brief summary of what has been covered during the lectures. All the definitions must be memorized and understood. Statements

### LECTURE NOTES DISCRETE MATHEMATICS. Eusebius Doedel

LECTURE NOTES on DISCRETE MATHEMATICS Eusebius Doedel 1 LOGIC Introduction. First we introduce some basic concepts needed in our discussion of logic. These will be covered in more detail later. A set is

### I thank the author of the examination paper on which sample paper is based. VH

I thank the author of the examination paper on which sample paper is based. VH 1. (a) Which of the following expressions is a sentence of L 1 or an abbreviation of one? If an expression is neither a sentence

### First order Logic ( Predicate Logic) and Methods of Proof

First order Logic ( Predicate Logic) and Methods of Proof 1 Outline Introduction Terminology: Propositional functions; arguments; arity; universe of discourse Quantifiers Definition; using, mixing, negating

### The Syntax of First-Order Logic. Marc Hoyois

The Syntax of First-Order Logic Marc Hoyois Table of Contents Introduction 3 I First-Order Theories 5 1 Formal systems............................................. 5 2 First-order languages and theories..................................