Artificial Intelligence (CS435)

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COURSE PORTFOLIO






FACULTY OF
SCIENCE


COMPUTER SCIENCE

DEPARTMENT




COURSE NAME
:


Artificial Intelligence


COURSE NUMBER
:


CS4
3
5


SEMESTER/YEAR
:

-------------------------------------------


DATE
:

--------------------
----------------------------
-------------





Instructor

Information



Dr
.
Gibrael Al Amin Abo Samra
.



Faculty of Science; Main

Building 115, Room 512
.



Contact number(s): ext. 64
241
.



E
-
mail address:
gabosamra
@
hotmail.com





Welcome to the
Artificial Intelligence (AI)
Course
. You will en
joy
understanding
what AI is, when we need to apply AI techniques and
how some of these techniques are implemented. You will also enjoy
understanding the basics of expert systems. Finally you will have
some practice on one of the most familiar AI programmi
ng
Languages (PROLOG).


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PART II



COURSE SYLLABUS

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Course Information


Course Name:

Artificial Intelligence



Course

Code:

CS
435


Course meeting times
: Sunday and Tuesday at 11:00 to 12:30


place
: building40 room 210


Prerequisite
:

CS221


Artifici
al Intelligence(AI) def:
-

AI is the branch of science that tries to automate the intelligent
behavior of the human to allow computers to perceive, reason, and decide.



Course Objectives


Course Objectives

This course introduces students to basic concepts

and methods of artificial
intelligence from a computer science perspective. Emphasis of the course will
be on the selection of data representation and algorithms useful in the design
and implementation of intelligent systems. The course will contain an ov
erview
of one Al language and some discussion of important applications of artificial
intelligence methodology
.



At the end of this course the students will be able to :

1.

Select a knowledge representation scheme suitable for a real life problem.

2.

2
-
Apply a
suitable search algorithm to get a solution depending on the problem goal.

3.

3
-
Use an AI programming language to implement simple expert systems.




Benefits of this Course

Students in this course will get the skills and the required background that
enable
them to build Intelligence systems in different application areas.


Learning Resources



Required Textbook:

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Artificial Intelligence: Structures and Strategies for Complex
Problem Solving by George F. Luger, Addison Wesley, 2002.




Artificial Intelligence (T
hird Edition) by Patrick He
n
ry Winston,
Addison ~ Wesley, 1992.




Notes

written by Prof. M. Ghona
im.



Slides for certain topics

Software Needed:

PROLOG version 6.X: available at the

computer lab.


Course Requirements and Grading


Evaluation:

30% Project,

Hom
ework
,
and
lab
.

1.

10
%
:
Home work: Solution of the exercises at the end of each chapter.


All students should solve the problems themselves to be able to solve problems in the exams. If
copy of solution is detected the 10 marks are lost.


2.

10
%
:
Project: Apply

one of the AI techniques on a real problem or a game and introduce this
work written on paper and /or stored on a floppy or compact disk.

A maximum of three students are allowed to join in one project

Projects shouldn't be repeated, if it happens, the tim
e of submission is taken into consideration.

Three

parameters are considered in the
evaluation of a project:



The originality of the project
's

idea.



The understanding
of the

used techniques.




The level of implementation
of the

project.


3.

10:
Implementation
&
Trace of a PROLOG program which contains

fa
c
t
s, rules, and goals.

3
0% First and Midterm exams

1.

10
%
: First

exam covers the Search Techniques
.

2.

20
%
:
Mid
-
term
exam covers Kno
w
led
ge R
epresentation and Expert
Systems
.

40% Final Exam


Covers all the
topics

of

the course.



Course Outline
:





Introduction


o

What is Artificial Intelligence?

o

Is Al Possible?

o

Some Al Tasks.





Using Search in Problem Solving

o

Introduction

o

Basic Search Techniques For Trees


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Simple Search Techniques

o

Depth first Search

o

Breadth first Sear
ch




Heuristic Search Techniques

o

Hill Climbing

o

Beam Search

o

Best First Search




Optimal Search Techniques

o

Branch and Bound Search

o

Branch and Bound Search Augmented by Underestimation

o

Branch and Bound Search with Eliminating Redundant Paths

o

The A* Algorithm




S
tate Space Search

Algorithms

o

Breadth First Algorithm

o

Depth First Algorithm

o

Best First Algorithm




Knowledge Representation and Inference

o

Introduction

o

Logical Representation Schemes



Prepositional Calculus



Predicate Logic



Review of Prepositional Logic



Predica
te Logic: Syntax



Predicate Logic: Semantics



Proving Things in Predicate Logic



Representing Things in Predicate Logic



Network Representation Schemes



Semantic Networks



Conceptual Graph



Structured Representation Schemes



Frames




Expert Systems

o

Introduction

o

Des
igning an Expert System

o

Expert System Architecture

o

Choosing a Problem

o

Knowledge Engineering

o

Rules and Expert Systems

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A Simple Example



Explanation facilities



More Complex Systems



Rule
-
Based Systems



Forward Chaining Systems



Backward Chaining Systems



Forwards

vs
.

Backwards Reasoning

o

An Expert System Shells<

o

MYCIN: A Quick Case Study




Artificial Intelligence Programming in Prolog

o

Artificial Intelligence Programming

o

The Main Al Languages

o

The Basics of Prolog



Prolog Terms, Backtracking and Unification



Basic Data
Structures and Syntax



More about Prolog Matching



Backtracking



Declarative and Procedural Views of Programs



Some Exercises



Recursion



Tracing Prolog Execution



Exercises


Course Schedule Model

(meeting two times a week)



Week

#

Date

Topic

Reading
Assignment

What is Due?

1


Introduction to the course

Chapter 1

Buy Book



AI Applications

areas


2


Blind Search


Problem set 1(

1,2,3,4
)


Heuristic

Search


Problem

se 1(
5(a,
b)
)

3


Optimal Search


Problem

set 1(
5(c,
d)
)



A* Algorithm



4


State Space Search

Algorithms


Problem

set 1 (
6
-
10
)



Revision of Search

techniques


Project #1

5



First Exam





Knowledge Representation and Inference

(introduction)

o

Introduction




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Week

#

Date

Topic

Reading
Assignment

What is Due?

6



Prepositional Calculus



Problem set 2(1,2)



Predicate Logic: Syntax




7



Predicate Logic

inference rules



Problem set 2(4)



Semantic Networks



Problem set 2(3)

8



Conceptual Graph
s



Problem set 2(5)



Str
u
ctured representation
:

Frames



Problem set 2(6)

9



Expert Systems
:

Introduction





Rule
-
Based Systems




10



Forward Chaining Systems


Problem set 3( 1
-
a)



B
ackward Chaining Systems



Problem set 3( 1
-
b)

11



Forwards vs Backwards Reasoning





An Expert System Shell



12



Reaction based systems





Mid Term Exam



13



Artificial Intelligence Programming





PROLOG
syntax:






PROLOG

databases an
d quires


Program1:family
relations



Rules in PROLOG


Deduce relations
using rules

15



Backtracking in PROLOG


Trace program1



Lists and recursion in PROLOG






Final Exam all sections




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PART III



COURSE RELATED MATERIAL




Contains all t
he materials considered essential to teaching the
course, includes:


lab quizzes, mid
-
terms, and final exams and their solution set

Paper or transparency copies of lecture
notes/
handouts
(optional)

Practical Session Manual (if one exists)

Handouts for pro
ject/term paper assignments


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PART IV



EXAMPLES OF STUDENT LEARNING




Examples of student work.

(Inclu
de
d

good, average, and poor
examples)


Graded work,
i.e.

exams, homework, quizzes

Students' papers, essays, and other creative work

Final grade ros
ter and grade distribution




PART V



INSTRUCTOR REFLECTION (optional)










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Part V
. Instructor Reflections on the Course

There is a big need for a projector to show real examples and to illustrate huge
problems.

There is a big need for an assistant

person to allow for more projects and support
problem solving.

There is a big need for lab hours to experiment PROLOG examples.

There is a big need for a website for the course with editing tools to allow for
improvements.


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COURSE PORTFOLIO

CHECKLIST






TITLE PAGE


COURSE SYLLABUS


COURSE RELATED MATERIAL


EXAMPLES OF EXTENT OF STUDENT LEARNING


INSTRUCTOR REFLECTION ON THE COURSE