Convert to clause form:

Similar documents
Logic. Knowledge Representation & Reasoning Mechanisms. Logic. Propositional Logic Predicate Logic (predicate Calculus) Automated Reasoning

Strong AI vs. Weak AI Automated Reasoning

Knowledge representation DATA INFORMATION KNOWLEDGE WISDOM. Figure Relation ship between data, information knowledge and wisdom.

Inference in first-order logic

Propositional Logic: Review

Resolution: Motivation

Propositional Resolution

Propositional Resolution

Deductive Systems. Lecture - 3

CogSysI Lecture 8: Automated Theorem Proving

Inference in first-order logic

Propositional Reasoning


Advanced Topics in LP and FP

Propositional Logic: Models and Proofs

README - Syntax. Skolemization. Skolemization - Example 1. Skolemization - Example 1. Skolemization - Example 1. Skolemization - Example 2

Mat 243 Exam 1 Review

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

Propositional and First-Order Logic

CS 730/730W/830: Intro AI

Resolution for Predicate Logic

COMP9414: Artificial Intelligence First-Order Logic

Inference in first-order logic

Intelligent Systems. Propositional Logic. Dieter Fensel and Dumitru Roman. Copyright 2008 STI INNSBRUCK

Propositional Logic Not Enough

Inference in Propositional Logic

Part 1: Propositional Logic

CS 730/830: Intro AI. 3 handouts: slides, asst 6, asst 7. Wheeler Ruml (UNH) Lecture 12, CS / 16. Reasoning.

Artificial Intelligence. Propositional Logic. Copyright 2011 Dieter Fensel and Florian Fischer

1 Introduction to Predicate Resolution

Introduction to Sets and Logic (MATH 1190)

Propositional Resolution Introduction

CS1021. Why logic? Logic about inference or argument. Start from assumptions or axioms. Make deductions according to rules of reasoning.

Inference in First Order Logic

Marie Duží

Logic. Stephen G. Ware CSCI 4525 / 5525

6. Logical Inference

7.5.2 Proof by Resolution

Artificial Intelligence: Methods and Applications Lecture 3: Review of FOPL

Propositional Logic Language

CSC384: Intro to Artificial Intelligence Knowledge Representation II. Required Readings: 9.1, 9.2, and 9.5 Announcements:

Probabilistic Theorem Proving. Vibhav Gogate (Joint work with Pedro Domingos)

CS 520: Introduction to Artificial Intelligence. Review

First-Order Theorem Proving and Vampire. Laura Kovács (Chalmers University of Technology) Andrei Voronkov (The University of Manchester)

INF3170 / INF4171 Notes on Resolution

Outline. Logic. Definition. Theorem (Gödel s Completeness Theorem) Summary of Previous Week. Undecidability. Unification

Resolution and Refutation

α-formulas β-formulas

software design & management Gachon University Chulyun Kim

Technische Universität München Summer term 2011 Theoretische Informatik Prof. Dr. Dr. h.c J. Esparza / M. Luttenberger / R.

Logic: First Order Logic

Logic and Inferences

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

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

1.1 Language and Logic

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

Inferences Critical. Lecture 30: Inference. Application: Question Answering. Types of Inferences

COMP4418: Knowledge Representation and Reasoning First-Order Logic

Propositional Logic. Logic. Propositional Logic Syntax. Propositional Logic

Lecture 12 September 26, 2007

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

Lecture 10: Even more on predicate logic" Prerequisites for lifted inference: Skolemization and Unification" Inference in predicate logic"

Propositional Logic Part 1

Propositional Logic Resolution (6A) Young W. Lim 12/12/16

Reasoning. Inference. Knowledge Representation 4/6/2018. User

Exercises 1 - Solutions

1.1 Language and Logic

Computational Logic Automated Deduction Fundamentals

PROPOSITIONAL CALCULUS

Logic and Computation

Computation and Logic Definitions

Propositional Logic Resolution (6A) Young W. Lim 12/31/16

Propositional and Predicate Logic - V

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

Logic and Reasoning. Foundations of Computing Science. Pallab Dasgupta Professor, Dept. of Computer Sc & Engg INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR

CSC Discrete Math I, Spring Propositional Logic

Logic and Propositional Calculus

Propositional logic II.

Introduction to Intelligent Systems

Closed Book Examination. Two hours UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE. M.Sc. in Advanced Computer Science

First order Logic ( Predicate Logic) and Methods of Proof

Introduction to Intelligent Systems

Supplementary exercises in propositional logic

Computational Logic. Davide Martinenghi. Spring Free University of Bozen-Bolzano. Computational Logic Davide Martinenghi (1/30)

Learning Goals of CS245 Logic and Computation

Language of Propositional Logic

COMP9414: Artificial Intelligence Propositional Logic: Automated Reasoning

Knowledge based Agents

Propositional and First-Order Logic

Warm-Up Problem. Is the following true or false? 1/35

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

Logic Programming (PLP 11) Predicate Calculus Clocksin-Mellish Procedure Horn Clauses

Resolution for Predicate Logic

CHAPTER 10. Predicate Automated Proof Systems

Automated Reasoning in First-Order Logic

AI Programming CS S-09 Knowledge Representation

A. Propositional Logic

First-Order Theorem Proving and Vampire

Mathematical Logic Part Three

Review: Potential stumbling blocks

Transcription:

Convert to clause form: Convert the following statement to clause form: x[b(x) ( y [ Q(x,y) P(y) ] y [ Q(x,y) Q(y,x) ] y [ B(y) E(x,y)] ) ] 1- Eliminate the implication ( ) E1 E2 = E1 E2 x[ B(x) ( y [ Q(x,y) P(y) ] y [ Q(x,y) Q(y,x) ] y [ ( B(y)) E(x,y)] ) ] 2- Move the negation down to the atomic formulas (by using the following rules) (P Q) P Q (P Q) P Q ( ( P ) ) P x ( P (x) ) x ( P (x) ) x ( P (x) ) x ( P (x) ) x[ B(x) ( y [ Q(x,y) P(y) ] y [ Q(x,y) Q(y,x) ] y [ B(y) E(x,y)] ) ] Assist lec. : Ahmed M. Al-Saleh 17 2010-2011

3- Purge existential quantifiers The function that is eliminate the existential are called Skolem function x[ B(x) ( [ Q(x, f (x)) P(f (x)) ] y [ Q(x,y) Q(y,x) ] y [ B(y) E(x,y)] ) ] 4- Rename variables, as necessary, so that no two variables are the same. x[ B(x) ( [ Q(x, f (x)) P(f (x)) ] y [ Q(x,y) Q(y,x) ] z [ B(z) E(x,z)] ) ] 5- Move the Universal quantifiers to the left of the statement. x y z [ B(x) ( [ Q(x, f (x)) P(f (x)) ] [ Q(x,y) Q(y,x) ] [ B(z) E(x,z)] ) ] 6- Move the disjunction down to the literals, using distributive laws E1 (E2 E3 E4 ) (E1 E2) (E1 E3). E1 (E2 E3 E4 ) (E1 E2) (E1 E3). x y z [ ( B(x) ( Q(x, f (x)) P(f (x) ) ) ) [ B(x) Q(x,y) Q(y,x) ] [ B(x) B(z) E(x,z)] ] x y z [ ( B(x) ( Q(x, f (x)) ( B(x) P(f (x) ) ) ( B(x) Q(x,y) Q(y,x) ) ( B(x) B(z) E(x,z) ) ] Assist lec. : Ahmed M. Al-Saleh 18 2010-2011

7- Eliminate the conjunctions x [ B(x) ( Q(x, f (x) ] x [ B(x) P(f (x) ) ] x y [ B(x) Q(x,y) Q(y,x) ] x z [ B(x) B(z) E(x,z) ] 8- Rename all the variables, as necessary, so that no two variables are the same. x [ B(x) ( Q(x, f (x) ] w [ B(w) P(f (w) ) ] u y [ B(u) Q(u,y) Q(y,u) ] a z [ B(a) B(z) E(a,z) ] 9- Purg the universal quntifiers. B(x) ( Q(x, f (x) B(w) P(f (w) ) B(u) Q(u,y) Q(y,u) B(a) B(z) E(a,z) ) Assist lec. : Ahmed M. Al-Saleh 19 2010-2011

Resolution Theorem Proving Resolution is a technique for proving theorems in the propositional or predicate calculus that has been a part of AI problem-solving. Resolution describes a way of finding contradictions in a database of clauses with minimum use of substitution. Resolution refutation proves a theorem by negating the statement to be proved and adding this negated goal to the set of axioms that are known (have been assumed) to be true. It then uses the resolution rule of inference to show that this leads to a contradiction. Once the theorem prover shows that the negated goal is inconsistent with the given set of axioms, it follows that the original goal must be consistent. This proves the theorem.. Resolution refutation proofs involve the following steps: 1. Put the premises or axioms into clause form. 2. Add the negation of what is to be proved, in clause form, to the set of axioms. 3. Resolve these clauses together, producing new clauses that logically follow from them. 4. Produce a contradiction by generating the empty clause. 5. The substitutions used to produce the empty clause are those under which the opposite of the negated goal is true. The following example illustrates the use of resolution theorem for reasoning with propositional logic. Assist lec. : Ahmed M. Al-Saleh 20 2010-2011

Unification of Predicates Two predicates P (t1,t2,, tn) and Q (s1, s2,, sn) can be unified if terms ti can be replaced by si or vice-versa. Loves (mary, Y) and Loves (X, Father-of (X)), for instance, can be unified by the substitution S ={ mary / X, Father-of ( mary) / Y }. Conditions of Unification: 1- Both the predicates to be unified should have an equal number of terms. 2- Neither ti nor si can be a negation operator, or predicate or functions of different variables, or if ti = term belonging to si or if si = term belonging to ti then unification is not possible. Example 1 : Consider the following knowledge base: 1. The-humidity-is-high v the-sky-is-cloudy. 2. If the-sky-is-cloudy then it-will-rain 3. If the-humidity-is-high then it-is-hot. 4. it-is-not-hot and the goal : it-will-rain. Prove by resolution theorem that the goal is derivable from the knowledge base. Proof: Let us first denote the above clauses by the following symbols. p = the-humidity-is-high, q = the-sky-is-cloudy, r = it-will-rain, s = it-is-hot. The Conjuctive Normal Form (CNF) from the above clauses thus become : 1. p v q 2. q v r 3. p v s 4. s Assist lec. : Ahmed M. Al-Saleh 21 2010-2011

and the negated goal = r. Set S thus includes all these 5 clauses. Now by resolution algorithm, we construct the solution by a tree. Since it terminates with a null clause, the goal is proved. Fig. 1: The resolution tree to prove that it-will-rain. Assist lec. : Ahmed M. Al-Saleh 22 2010-2011

Example 2: "All people who are not poor and are smart are hippy. Those people who' read are not stupid. John can read are is wealthy. Happy people have exciting lives. Can anyone be found with an exciting life?" Sol.: a) First change the sentences to predicate form: b) These predicate calculus expressions for the happy life problem are transformed into the following clauses: The resolution refutation for this example is found in Figure 2. Assist lec. : Ahmed M. Al-Saleh 23 2010-2011

Figure (2): Resolution prove for the "exciting life" problem. Assist lec. : Ahmed M. Al-Saleh 24 2010-2011