COURSE PORTFOLIO
FACULTY OF SCIENCE
COMPUTER SCIENCE DEPARTMENT
COURSE NAME:
Artificial Intelligence
COURSE NUMBER:
CS435
SEMESTER/YEAR: ------------------------------------------DATE: -------------------------------------------------------------
Instructor Information
 Dr. Gibrael Al Amin Abo Samra.
 Faculty of Science; Main Building 115, Room 512.
 Contact number(s): ext. 64241.
 E-mail address: gabosamra@hotmail.com
 Welcome to the Artificial Intelligence (AI) Course. You will enjoy
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 programming
Languages (PROLOG).
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PART II
COURSE SYLLABUS
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Course Information
Course Name: Artificial Intelligence
Course Code: CS435
Course meeting times: Sunday and Tuesday at 11:00 to 12:30
place: building40 room 210
Prerequisite: CS221
Artificial 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 overview
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 (Third Edition) by Patrick Henry Winston,
Addison ~ Wesley, 1992.
Notes written by Prof. M. Ghonaim.
Slides for certain topics
Software Needed:
PROLOG version 6.X: available at the computer lab.
Course Requirements and Grading
Evaluation:
30% Project, Homework, 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 time 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 facts, rules, and goals.
30% First and Midterm exams
1. 10%: First exam covers the Search Techniques.
2. 20%:Mid-term exam covers Knowledge Representation 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 Search

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

State 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
 Predicate 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 Designing 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
An Expert System Shells<
MYCIN: A Quick Case Study

o
o

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
Introduction to the course
1
Reading
Assignment
Chapter 1
What is Due?
Buy Book 
AI Applications areas
Blind Search
2
Heuristic Search
3
Optimal Search
Problem set 1(
1,2,3,4)
Problem se 1(5(a,
b))
Problem set 1(5(c,
d))
A* Algorithm
State Space Search Algorithms
4
Revision of Search techniques
First Exam
5
Knowledge Representation and Inference
(introduction)
o Introduction
Problem set 1 (610)
Project #1
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Week
#
Date
Topic
Prepositional Calculus
6
7
8
Reading
Assignment
What is Due?
Problem set 2(1,2)
Predicate Logic: Syntax
Predicate Logic inference rules
Problem set 2(4)
Semantic Networks
Problem set 2(3)
Conceptual Graphs
Problem set 2(5)
Structured representation: Frames
Problem set 2(6)
Expert Systems: Introduction
9
Rule-Based Systems
Forward Chaining Systems
10
Backward Chaining Systems
Problem set 3( 1-a)
Problem set 3( 1-b)
Forwards vs Backwards Reasoning
11
An Expert System Shell
Reaction based systems
12
Mid Term Exam
Artificial Intelligence Programming
13
PROLOG syntax:
PROLOG databases and quires
Rules in PROLOG
Backtracking in PROLOG
15
Lists and recursion in PROLOG
Final Exam all sections
Program1:family
relations
Deduce relations
using rules
Trace program1
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PART III
COURSE RELATED MATERIAL
Contains all the 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 project/term paper assignments
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PART IV
EXAMPLES OF STUDENT LEARNING
Examples of student work. (Included good, average, and poor
examples)
Graded work, i.e. exams, homework, quizzes
Students' papers, essays, and other creative work
Final grade roster 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