First Order Logic. Xiaojin Zhu Computer Sciences Department University of Wisconsin, Madison. [Based on slides from Burr Settles]

Similar documents
First Order Logic Part 2

The Language of First-Order Predicate Logic

Peirce's Remarkable Rules of Inference

COMP Intro to Logic for Computer Scientists. Lecture 2

Dynamic Semantics! (Part 1: Not Actually Dynamic Semantics) Brian Morris, William Rose

Articulating Medieval Logic, by Terence Parsons. Oxford: Oxford University Press,

Chapter 4. Predicate logic allows us to represent the internal properties of the statement. Example:

Lecture 7. Scope and Anaphora. October 27, 2008 Hana Filip 1

Introduction p. 1 The Elements of an Argument p. 1 Deduction and Induction p. 5 Deductive Argument Forms p. 7 Truth and Validity p. 8 Soundness p.

6.034 Notes: Section 4.1

Nissim Francez: Proof-theoretic Semantics College Publications, London, 2015, xx+415 pages

MATH 195: Gödel, Escher, and Bach (Spring 2001) Notes and Study Questions for Tuesday, March 20

For every sentences A and B, there is a sentence: A B,

QUANTIFICATION IN AFRICAN LOGIC. Jonathan M. O. Chimakonam Ph.D Department of Philosophy University of Calabar, Nigeria

From Propositional! to Predicate Logic" CSCI 2824, Fall 2011" classes/struct11/home.html "

Knowledge Representation

11. SUMMARY OF THE BASIC QUANTIFIER TRANSLATION PATTERNS SO FAR EXAMINED

The Language Revolution Russell Marcus Fall Class #7 Final Thoughts on Frege on Sense and Reference

Reply to Stalnaker. Timothy Williamson. In Models and Reality, Robert Stalnaker responds to the tensions discerned in Modal Logic

The word digital implies information in computers is represented by variables that take a limited number of discrete values.

8. Numerations The existential quantifier Exemplification Overview

In Defense of the Contingently Nonconcrete

8. Numerations The existential quantifier Overview

Multiple Quantifiers. Multiple uses of a single quantifier. Chapter 11

Natural Language Processing

Lesson THINKING OPERATIONS. Now you re going to say the rule that starts with no chairs. (Pause.) Get ready.

Plurals Jean Mark Gawron San Diego State University

Song Lessons Understanding and Using English Grammar, 3rd Edition. A lesson about adjective, adverb, and noun clauses (Chapters 12, 13, 17)

*High Frequency Words also found in Texas Treasures Updated 8/19/11

Lecture 24: Motivating Modal Logic, Translating into It

Replies to the Critics

Basic Sight Words - Preprimer

Logik für Informatiker Logic for computer scientists

cse371/mat371 LOGIC Professor Anita Wasilewska

Vagueness & Pragmatics

Software Engineering 2DA4. Slides 3: Optimized Implementation of Logic Functions

1. As you study the list, vary the order of the words.

Survey of Knowledge Base Content

Singular Propositions, Abstract Constituents, and Propositional Attitudes

The Reference Book, by John Hawthorne and David Manley. Oxford: Oxford University Press 2012, 280 pages. ISBN

Logica & Linguaggio: Tablaux

Comparatives, Indices, and Scope

On Recanati s Mental Files

QUESTIONS AND LOGICAL ANALYSIS OF NATURAL LANGUAGE: THE CASE OF TRANSPARENT INTENSIONAL LOGIC MICHAL PELIŠ

PLEASE SCROLL DOWN FOR ARTICLE

MONOTONE AMAZEMENT RICK NOUWEN

Department of CSIT. Class: B.SC Semester: II Year: 2013 Paper Title: Introduction to logics of Computer Max Marks: 30

The Philosophy of Language. Frege s Sense/Reference Distinction

Lexical Semantics: Sense, Referent, Prototype. Sentential Semantics (phrasal, clausal meaning)

Introduction to semantic networks and conceptual graphs

Focus Poetry Plan Week 1

Running head: Collective Representational Content for Shared Extended Mind. Collective Representational Content for Shared Extended Mind.

Symbolization and Truth-Functional Connectives in SL

AN EXAMPLE FOR NATURAL LANGUAGE UNDERSTANDING AND THE AI PROBLEMS IT RAISES

Background to Gottlob Frege

ARISTOTLE ON LANGUAGE PARALOGISMS SophElen. c.4 p.165b-166b

Formalizing Irony with Doxastic Logic

Consistency and Completeness of OMEGA, a Logic for Knowledge Representation

Study Guide. The House on Mango Street by Sandra Cisneros. Student Name

Varieties of Nominalism Predicate Nominalism The Nature of Classes Class Membership Determines Type Testing For Adequacy

Phil 004. Week 4 Chapter 3 Clarity of an Argument

An Introduction to Description Logic I

Sentences for the vocabulary of The Queen and I

Tropes and the Semantics of Adjectives

1 Pair-list readings and single pair readings

Action Sheet I am unique! At first sight the same, at second sight very similar but still unique?

High Frequency Word Sheets Words 1-10 Words Words Words Words 41-50

THE PROBLEM OF INTERPRETING MODAL LOGIC w. V. QUINE

The Function Is Unsaturated

Notes #1: ELEMENTS OF A STORY

Lecture 5: Tuning Systems

Topics in Linguistic Theory: Propositional Attitudes

Chapter 7 Probability

MATH 214 (NOTES) Math 214 Al Nosedal. Department of Mathematics Indiana University of Pennsylvania. MATH 214 (NOTES) p. 1/3

Norcross on the Definition of Harm

Scientific Philosophy

KANT S TRANSCENDENTAL LOGIC

The Language Revolution Russell Marcus Fall 2015

Elements of Style. Anders O.F. Hendrickson

Sight Words Sentences

Intro to Pragmatics (Fox/Menéndez-Benito) 10/12/06. Questions 1

The First Hundred Instant Sight Words. Words 1-25 Words Words Words

LEVEL PRE-A1 LAAS LANGUAGE ATTAINMENT ASSESSMENT SYSTEM. English English Language Language Examinations Examinations. December 2005 May 2013

Also highly recommended: Graphing Resources ( homepage.htm), particularly their Revising your Visuals section.

TRANSLATIONS IN SENTENTIAL LOGIC

Cambridge Primary English as a Second Language Curriculum Framework mapping to English World

Section 3.1 Statements, Negations, and Quantified Statements

Argument and argument forms

Object Theory and Modal Meinongianism

Dolch Word List. List 1 List 2 List 3 List 4 List 5 List 6 List 7 List 8 List 9 List 10 List 11. Name. Parents,

Eighth Note Subdivisions

1 Introduction to Finite-State Machines and State Diagrams for the Design of Electronic Circuits and Systems

Collective representational content for shared extended mind

Encoders and Decoders: Details and Design Issues

Instantiation and Characterization: Problems in Lowe s Four-Category Ontology

Lab experience 1: Introduction to LabView

What is Character? David Braun. University of Rochester. In "Demonstratives", David Kaplan argues that indexicals and other expressions have a

A Solution to Frege's Puzzle

Review Jean Mark Gawron SDSU. March 14, Translation basics (you shouldnt get these things wrong):

CPSC 121: Models of Computation. Module 1: Propositional Logic

Transcription:

First Order Logic Xiaojin Zhu jerryzhu@cs.wisc.edu Computer Sciences Department University of Wisconsin, Madison [Based on slides from Burr Settles] slide 1

Problems with propositional logic Consider the game minesweeper on a 10x10 field with only one landmine. How do you express the knowledge, with propositional logic, that the squares adjacent to the landmine will display the number 1? slide 2

Problems with propositional logic Consider the game minesweeper on a 10x10 field with only one landmine. How do you express the knowledge, with propositional logic, that the squares adjacent to the landmine will display the number 1? Intuitively with a rule like landmine(x,y) Þ number1(neighbors(x,y)) but propositional logic cannot do this slide 3

Problems with propositional logic Propositional logic has to say, e.g. for cell (3,4): Landmine_3_4 Þ number1_2_3 Landmine_3_4 Þ number1_2_4 Landmine_3_4 Þ number1_2_5 Landmine_3_4 Þ number1_3_3 Landmine_3_4 Þ number1_3_5 Landmine_3_4 Þ number1_4_3 Landmine_3_4 Þ number1_4_4 Landmine_3_4 Þ number1_4_5 And similarly for each of Landmine_1_1, Landmine_1_2, Landmine_1_3,, Landmine_10_10! Difficult to express large domains concisely Don t have objects and relations First Order Logic is a powerful upgrade slide 4

Ontological commitment Logics are characterized by what they consider to be primitives Logic Primitives Available Knowledge Propositional facts true/false/unknown First-Order facts, objects, relations true/false/unknown Temporal facts, objects, relations, times true/false/unknown Probability Theory facts degree of belief 0 1 Fuzzy degree of truth degree of belief 0 1 slide 5

First Order Logic syntax Term: an object in the world Constant: Jerry, 2, Madison, Green, Variables: x, y, a, b, c, Function(term 1,, term n ) Sqrt(9), Distance(Madison, Chicago) Maps one or more objects to another object Can refer to an unnamed object: LeftLeg(John) Represents a user defined functional relation A ground term is a term without variables. slide 6

FOL syntax Atom: smallest T/F expression Predicate(term 1,, term n ) Teacher(Jerry, you), Bigger(sqrt(2), x) Convention: read Jerry (is)teacher(of) you Maps one or more objects to a truth value Represents a user defined relation term 1 = term 2 Radius(Earth)=6400km, 1=2 Represents the equality relation when two terms refer to the same object slide 7

FOL syntax Sentence: T/F expression Atom Complex sentence using connectives: Ù Ú Þ Û Spouse(Jerry, Jing) Þ Spouse(Jing, Jerry) Less(11,22) Ù Less(22,33) Complex sentence using quantifiers ", $ Sentences are evaluated under an interpretation Which objects are referred to by constant symbols Which objects are referred to by function symbols What subsets defines the predicates slide 8

FOL quantifiers Universal quantifier: " Sentence is true for all values of x in the domain of variable x. Main connective typically is Þ Forms if-then rules all humans are mammals " x human(x) Þ mammal(x) Means if x is a human, then x is a mammal slide 9

FOL quantifiers " x human(x) Þ mammal(x) It s a big AND: Equivalent to the conjunction of all the instantiations of variable x: (human(jerry) Þ mammal(jerry)) Ù (human(jing) Þ mammal(jing)) Ù (human(laptop) Þ mammal(laptop)) Ù Common mistake is to use Ù as main connective " x human(x) Ù mammal(x) This means everything is human and a mammal! (human(jerry) Ù mammal(jerry)) Ù (human(jing) Ù mammal(jing)) Ù (human(laptop) Ù mammal(laptop)) Ù slide 10

FOL quantifiers Existential quantifier: $ Sentence is true for some value of x in the domain of variable x. Main connective typically is Ù some humans are male $ x human(x) Ù male(x) Means there is an x who is a human and is a male slide 11

FOL quantifiers $ x human(x) Ù male(x) It s a big OR: Equivalent to the disjunction of all the instantiations of variable x: (human(jerry) Ù male(jerry)) Ú (human(jing) Ù male(jing)) Ú (human(laptop) Ù male(laptop)) Ú Common mistake is to use Þ as main connective Some pig can fly $ x pig(x) Þ fly(x) (wrong) slide 12

FOL quantifiers $ x human(x) Ù male(x) It s a big OR: Equivalent to the disjunction of all the instantiations of variable x: (human(jerry) Ù male(jerry)) Ú (human(jing) Ù male(jing)) Ú (human(laptop) Ù male(laptop)) Ú Common mistake is to use Þ as main connective Some pig can fly $ x pig(x) Þ fly(x) (wrong) This is true if there is something not a pig! (pig(jerry) Þ fly(jerry)) Ú (pig(laptop) Þ fly(laptop)) Ú slide 13

FOL quantifiers Properties of quantifiers: " x " y is the same as " y " x $ x $ y is the same as $ y $ x Example: " x " y likes(x,y) Everyone likes everyone. " y " x likes(x,y) Everyone is liked by everyone. slide 14

FOL quantifiers Properties of quantifiers: " x $ y is not the same as $ y " x $ x " y is not the same as " y $ x Example: " x $ y likes(x,y) Everyone likes someone (can be different). $ y " x likes(x,y) There is someone who is liked by everyone. slide 15

FOL quantifiers Properties of quantifiers: " x P(x)when negated becomes $ x P(x) $ x P(x)when negated becomes " x P(x) Example: " x sleep(x) Everybody sleeps. $ x sleep(x) Somebody does not sleep. slide 16

FOL quantifiers Properties of quantifiers: " x P(x)is the same as $ x P(x) $ x P(x)is the same as " x P(x) Example: " x sleep(x) Everybody sleeps. $ x sleep(x) There does not exist someone who does not sleep. slide 17

FOL syntax A free variable is a variable that is not bound by an quantifier, e.g. $ y Likes(x,y): x is free, y is bound A well-formed formula (wff) is a sentence in which all variables are quantified (no free variable) Short summary so far: Constants: Bob, 2, Madison, Variables: x, y, a, b, c, Functions: Income, Address, Sqrt, Predicates: Teacher, Sisters, Even, Prime Connectives: Ù Ú Þ Û Equality: = Quantifiers: " $ slide 18

More summary Term: constant, variable, function. Denotes an object. (A ground term has no variables) Atom: the smallest expression assigned a truth value. Predicate and = Sentence: an atom, sentence with connectives, sentence with quantifiers. Assigned a truth value Well-formed formula (wff): a sentence in which all variables are quantified slide 19

Thinking in logical sentences Convert the following sentences into FOL: Elmo is a monster. What is the constant? Elmo What is the predicate? Is a monster Answer: monster(elmo) Tinky Winky and Dipsy are teletubbies Tom, Jerry or Mickey is not a mouse. slide 20

Thinking in logical sentences We can also do this with relations: America bought Alaska from Russia. What are the constants? America, Alaska, Russia What are the relations? Bought Answer: bought(america, Alaska, Russia) Warm is between cold and hot. Jerry and Jing are married. slide 21

Thinking in logical sentences Now let s think about quantifiers: Jerry likes everything. What s the constant? Jerry Thing? Just use a variable x Everything? Universal quantifier Answer: " x likes(jerry, x) i.e. likes(jerry, IceCream) Ù likes(jerry, Jing) Ù likes(jerry, Armadillos) Ù Jerry likes something. Somebody likes Jerry. slide 22

Thinking in logical sentences We can also have multiple quantifiers: somebody heard something. What are the variables? Somebody, something How are they quantified? Both are existential Answer: $ x,y heard(x,y) Everybody heard everything. Somebody did not hear everything. slide 23

Thinking in logical sentences Let s allow more complex quantified relations: All stinky shoes are allowed. How are ideas connected? Being a shoe and being stinky implies it s allowed Answer: " x shoe(x) Ù stinky(x) Þ allowed(x) No stinky shoes are allowed. Answers: " x shoe(x) Ù stinky(x) Þ allowed(x) $ x shoe(x) Ù stinky(x) Ù allowed(x) $ x shoe(x) Ù stinky(x) Þ allowed(x) (?) slide 24

Thinking in logical sentences No stinky shoes are allowed. $ x shoe(x) Ù stinky(x) Þ allowed(x) (?) $ x (shoe(x) Ù stinky(x)) Ú allowed(x) " x ( (shoe(x) Ù stinky(x)) Ú allowed(x)) " x (shoe(x) Ù stinky(x)) Ù allowed(x) But this says Jerry is a stinky shoe and Jerry is not allowed. How about " x allowed(x) Þ (shoe(x) Ù stinky(x)) slide 25

Thinking in logical sentences And some more complex relations: No one sees everything. Answer: $ x " y sees(x,y) Equivalently: Everyone doesn t see something. Answer: " x $ y sees(x,y) Everyone sees nothing. Answer: " x $ y sees(x,y) slide 26

Thinking in logical sentences And some really complex relations: Any good amateur can beat some professional. Ingredients: x, amateur(x), good(x), y, professional(y), beat(x,y) Answer: " x [{amateur(x) Ù good(x)} Þ $ y {professional(y) Ù beat(x,y)}] Some professionals can beat all amateurs. Answer: $ x [professional(x) Ù " y {amateur(y) Þ beat(x,y)}] slide 27

Thinking in logical sentences We can throw in functions and equalities, too: Jerry and Jing are the same age. Are functional relations specified? Are equalities specified? Answer: age(jerry) = age(jing) There are exactly two shoes.? slide 28

Thinking in logical sentences There are exactly two shoes. First try: $ x $ y shoe(x) Ù shoe(y) slide 29

Thinking in logical sentences There are exactly two shoes. First try: $ x $ y shoe(x) Ù shoe(y) Second try: $ x $ y shoe(x) Ù shoe(y) Ù (x=y) slide 30

Thinking in logical sentences There are exactly two shoes. First try: $ x $ y shoe(x) Ù shoe(y) Second try: $ x $ y shoe(x) Ù shoe(y) Ù (x=y) Third try: $ x $ y shoe(x) Ù shoe(y) Ù (x=y) Ù " z (shoe(z) Þ (x=z) Ú (y=z)) slide 31

Thinking in logical sentences Interesting words: always, sometimes, never Good people always have friends. slide 32

Thinking in logical sentences Interesting words: always, sometimes, never Good people always have friends. " x person(x) Ù good(x) Þ $ y(friend(x,y)) Busy people sometimes have friends. slide 33

Thinking in logical sentences Interesting words: always, sometimes, never Good people always have friends. " x person(x) Ù good(x) Þ $ y(friend(x,y)) Busy people sometimes have friends. $ x person(x) Ù busy(x) Ù $ y(friend(x,y)) Bad people never have friends. slide 34

Thinking in logical sentences Interesting words: always, sometimes, never Good people always have friends. " x person(x) Ù good(x) Þ $ y(friend(x,y)) Busy people sometimes have friends. $ x person(x) Ù busy(x) Ù $ y(friend(x,y)) Bad people never have friends. " x person(x) Ù bad(x) Þ $ y(friend(x,y)) slide 35

Thinking in logical sentences Tricky sentences x is above y if and only if x is directly on the top of y, or else there is a pile of one or more other objects directly on top of one another, starting with x and ending with y. slide 36

Thinking in logical sentences Tricky sentences x is above y if and only if x is directly on the top of y, or else there is a pile of one or more other objects directly on top of one another, starting with x and ending with y. " x " y above(x,y) Û [ontop(x,y) Ú $ z{ontop(x,z) Ù above(z,y)}] slide 37

Professor Snape s Puzzle Danger lies before you, while safety lies behind, Two of us will help you, whichever you would find, One among us seven will let you move ahead, Another will transport the drinker back instead, Two among our number hold only nettle-wine, Three of us are killers, waiting hidden in line Choose, unless you wish to stay here forevermore To help you in your choice, we give you these clues four: First, however slyly the poison tries to hide You will always find some on nettle wine's left side Second, different are those who stand at either end But if you would move onward, neither is your friend; Third as you see clearly, all are different size Neither dwarf nor giant hold death in their insides; Fourth, the second left and the second on the right Are twins once you taste them, though different at first sight. slide 38 JKR/POTTERMORE LTD. WARNER BROS.

1. $ x A(x)Ù (" y A(y)Þ x=y) 2. $ x B(x)Ù (" y B(y)Þ x=y) 3. $ x$ y W(x)Ù W(y)Ù (x=y)ù (" z W(z)Þ z=xú z=y) 4. " x (A(x)Ú B(x)Ú W(x)) Þ P(x) 5. " x" y W(x)Ù L(y,x) Þ P(y) 6. (P(b1) Ù P(b7)) 7. (W(b1) Ù W(b7)) 8. A(b1) 9. A(b7) 10. P(b3) 11. P(b6) 12.(P(b2) Ù P(b6)) Ú (W(b2) Ù W(b6)) slide 39

Next: Inference for FOL Recall that in propositional logic, inference is easy Enumerate all possibilities (truth tables) Apply sound inference rules on facts But in FOL, we have the concepts of variables, relations, and quantification This complicates things quite a bit! We will discuss inference in FOL next time. slide 40