artificial intelligence

04/08/23

Department of Computer Science, University of Agriculture, Abeokuta,

Nigeria. www.unaab.edu.ng 1

CSC 427: ARTIFICIAL INTELLIGENCE

BY

DR. A. F. ADEKOYARoom No: COLNAS B321

Department of Computer Science

College of Natural Sciences

University of Agriculture, Abeokuta

04/08/23




Course Contents:1. Introduction to Artificial Intelligence

2. Overview of Artificial Intelligence Techniques/Tools

3. Descriptive Logic

4. Artificial Neural Network

5. Fuzzy Logic

6. Genetic Algorithm

7. Knowledge Representation, Expert Systems and Pattern Recognition

8. Natural Language Processing

04/08/23




References:1. Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed

Kantardzic John Wiley & Sons ISBN:0471228524 © 2003

2. Artificial Intelligence and Expert Systems for Engineers by C.S. Krishnamoorthy; S. Rajeev CRC Press, CRC Press LLC ISBN: 0849391253 1996

3. C++ Neural Networks and Fuzzy Logic by Valluru B. Rao M&T Books, IDG Books Worldwide, Inc. ISBN: 1558515526 1995

4. Mathematics for Computingby R. Callan, Letts Educational, London.ISBN 1-85805-375-7 1998

5. Artificial Intelligence: A Guide to Intelligent Systemsby Michael Negnevitsky, Addison Wesley Pearson EducationISBN

04/08/23



Assessments1. Attendance 5%2. Assignments 10%3. Mid-Semester Exams 15%4. Semester Examination 70%

Note: Deadlines for submission of assignments are not negotiable

and must be strictly complied with. Copying of assignments is forbidden and would attract zero

mark. There would be one or two impromptu CAT(s). 75% Attendance is required before a student would be

allowed to sit for the semester examination.


04/08/23




Overview:

Computers have been extensively used to expedite or automate many complex and sometimes dangerous tasks. The history of the use of computers in problems solving parallels the developments in computer hardware and software technology.

The emergence of improved paradigms such as evolutionary, soft, parallel and distributed computing, backed up by appropriate software environments, has virtually transformed the direction of research in computer usage. This has resulted in the transformation of computers from large numerical computing machines to aids to engineers at every stage of problem solving.

Introduction to Artificial Intelligence

04/08/23




A key task in problem solving especially with computers at different stages of execution is decision making.

Decision making requires processing of symbolic information in handling of facts and inference using domain knowledge. Inference is nothing but search through the knowledge base using the facts.

The intensive research carried out in the area of AI in the last six decades resulted in the emergence of a number of useful techniques which can be used for solving many complex problems.


04/08/23




Intelligence is synonymous with human (animal) ability to store and recall fact (cognitive), solve a given problem based on known fact and relevant theorem (psychomotor).

This ability is inherent and innate, trainable and can be developed.

Artificial Intelligence (AI) is the ability of an electronic device (computer) to accomplish any tasks that ordinary would have been handled by human.


04/08/23





AI models the richness and dynamisms of human brain and its analytic and memory capability.

Artificial Intelligence focus on

(i) the use of computers to process symbols,

(ii) the need for new languages, and

(iii) the role of computers for theorem proving instead of focusing on hardware that simulated intelligence.

(J. McCarthy, M. Minsky, N. Rochester and C. Shanon 1956)

04/08/23





Major Categories of AI

1. Symbolic

Based on logic and uses of sequences of rules. Symbolic programs are good in modeling how human think, act and accomplish tasks.

2. Connectionist

Based on network of neurons in the brain. Brittle and good for machine learning and pattern recognition.

3. Evolutionary

Based on genetics evolution theory in biology.

04/08/23





Purpose of AI

1. Technological

2. Psychological

3. Economic

Home Task

Do a critic of Alan Turing and

John Searle views of AI

04/08/23




Logic

• Proposition Logic

• Predicate Logic

04/08/23




Logic

Proposition Logic

A propositional logic is a logical statement whose truth value can be evaluated as either TRUE or FALSE.

Where T denotes TRUE

And F denotes FALSE

Propositional logic is of two types, namely

• Simple Propositional Logic

• Complex or Compound Propositional Logic.

04/08/23




Logic

Simple Propositional Logic

A simple propositional logic refers to single logical statement whose truth value can be verified or evaluated.

e.g. UNAAB is a university.

Complex or Compound Propositional Logic

A complex propositional logic refers to logical statements which are combinations of two or more simple propositional logic statements with the use of connectors (connectives) such as disjunction, conjunction etc.

e.g. UNAAB is a university and it is located in Abeokuta.

04/08/23




Logic

Truth Tables

Truth tables are used for stating precise logic values for logic

statements. The number of rows in a truth table is 2n, where n is the number of simple propositions in the logical statements.

Each of the propositions are given labels such as A, B, C etc.

e.g. UNAAB is a university A

e.g. UNAAB is a university and it is located in Abeokuta.

comprises UNAAB is a university A

UNAAB is located in Abeokuta B

04/08/23




Logic

Logic Connectives

Logic uses names and symbols to represent the connectives as illustrated in this table.

Symbol Connectives Logic Name

., Conjunction AND

+, Disjunction OR

Negation NOT

Implication Implies

Equivalence Double implication

Exclusive OR

EX-OR

04/08/23




AND Truth Tablee.g. UNAAB is a university and it is located in Abeokuta.

comprises UNAAB is a university A UNAAB is located in Abeokuta B

Connective: AND, ., A B A AND B A B A . B

T T T T T

T F F F F

F T F F F

F F F F F

Rules of AND

1. The output is TRUE when all the inputs are TRUE.

2. The output is FALSE if any or all the inputs are FALSE

04/08/23




OR Truth Tablee.g. Either UNAAB is a university or is a polytechnic.

comprises UNAAB is a university A UNAAB is a polytechnic B

Connective: OR, +,

A B A OR B A B A + B

T T T T T

T F T T T

F T T T T

F F F F F

Rules of OR

1. The output is TRUE when at least one or all the inputs are TRUE.

2. The output is FALSE if all the inputs are FALSE

04/08/23




NOT Truth Table

e.g. UNAAB is a university A UNAAB is not a university A

Connective: NOT, -,

A NOT A A

- A

T F F F

F T T T

Rules of OR

The output is a negation of the input.

i.e. when the

• Input is TRUE, the output is FALSE.

• Input is FALSE, the output is TRUE.

04/08/23




IMPLICATION Truth Tablee.g. IF UNAAB is a university THEN it is an higher institution. comprises UNAAB is a university A

UNAAB is an higher institution B

Connective: Implies, -,

A B A Implies B A B

T T T T

T F F F

F T T T

F F T T

Rules of Implication

1. The output is TRUE when at least one or all the inputs are TRUE.

2. The output is FALSE if all the input are FALSE

04/08/23




EQUIVALENCE Truth Tablee.g. A university degree is equivalent to a polytechnic diploma

Connective: Equivalence, ↔

A B A ↔ B

T T T

T F F

F T F

F F T

Rules of Equivalence

1. The output is TRUE when all the inputs are TRUE.

2. The output is TRUE when all the inputs are FALSE.

3. The output is FALSE if any of the input is FALSE

Exercise: Using a truth table, prove that (A ↔B) ↔((A ↔B)^(B→A))

04/08/23




EXCLUSIVE-OR Truth Tablee.g. A university degree is equivalent to a polytechnic diploma

Connective: EX-OR,

A B A B

T T F

T F T

F T T

F F F

Rules of Equivalence

1. The output is TRUE

when either at least an input is TRUE or FALSE.

2. The output is FALSE if

either all the inputs are TRUE or all the inputs are FALSE

04/08/23




TAUTOLOGYAn expression with a truth value T irrespective of the truth values of the constituent atoms.

A B A ^ B→A

T T T

T F T

F T T

F F T

CONTRADICTION An expression with a truth value F irrespective of the truth values of the constituent atoms.

TAUTOLOGY AND CONTRADICTION

A B A ^ B→A

T T F

T F F

F T F

F F F

04/08/23




ARGUMENT AND VALIDITY

Argument: An argument present a conclusion as following logically from a set of assumptions.

e.g. If we say “John’s keys are in the car or hung up in the office. John’s keys are not in the car. Then John’s keys are hung up in the office.”

We can always write this argument in a clear and precise formal expression, such as:

John’s keys are in the car or hung up in the office P

John’s keys are in the car ¬ P Therefore, John’s keys are hung up in the office Q

We can express the argument in a more formal formi. P νQ Assumptionsii. ¬ Piii, Q Conclusion

}Logically, it is written as

PνQ

¬P

Q

04/08/23




ARGUMENT AND VALIDITY

Validity of an argument: If we express the argument in the form [ A

04/08/23




artificial intelligence

Documents

compound propositional logic

polytechnic diploma connective

artificial intelligence

johns keys

logical statements

artificial intelligenceargument

artificial intelligenceintroduction

computer science