Philadelphia University
Faculty of: Administrative & Financial Sciences
Department of Business Networking and Systems Management
Course Syllabus
Course Title: Artificial Intelligence Systems Course code: 0371441
Course Level: 2nd year
Lecture Time:
Course prerequisite (s) and/or co requisite
Credit hours: 3
Academic Staff
Specifics
Name
Sundus Hamoodi
Rank
Assistance
Professor
Office Number and
Office
Location
Hours
E-mail Address
32418/
SundusHamodi@yahoo.com
Ext. No. 2441
Course module description:
This course highlights a number of concepts of Artificial Intelligence) AI( ; definition,
goals, applications, and searching techniques. During the course duration some
important topics in AI will be presented like Knowledge Base Systems, Knowledge
Representation, Reasoning, and Control Reasoning. This course emphasize on study
the Expert Systems (ES); overview of the purpose, structure, and applications of
expert systems. Topics covered will include expert systems technology, knowledge
engineering, and applications of expert systems, expert systems development, how the
expert information system can be design and implemented the future of expert
systems. Neural Network concept and applications will be introduces in this course. In
this course also we will work together on one of the most important principle of
PROLOG (programming in logic( used in AI and ES systems.
‫ تطبيقاته‬، ‫ اهدافه‬، ‫ تعريف الذكاء الصناعي‬: ‫) ومن اهمها‬AI( ‫يغطي هذا المقرر اساسيات الذكاء الصناعي‬
‫ وخالل فترة الفصل الدراسي سيتم عرض مواضيع هامه في الذكاء الصناعي مثل نظم قواعد‬.‫واليات البحث‬
،‫ لمحة عامة عن الغرض‬: ‫ و المطابقة ويتم التأكيد على دراسة األنظمة الخبيرة‬، ‫ تمثيل المعرفه‬،‫المعرفه‬
‫ تكنولوجيا النظم الخبيرة هندسة‬: ‫ الموضوعات التي سوف يتم تغطيتها تشمل‬.‫ وتطبيقات النظم الخبيرة‬،‫الهيكلية‬
‫ ومستقبل النظم‬، ‫ كيفية تصميم وتنفيذ االنظمة الخبيره‬، ‫ تطوير النظم الخبيرة‬،‫ تطبيقات النظم الخبيرة‬،‫المعرفة‬
‫ الشبكات العصبية مفاهيم وتطبيقات سيتم التطرق لها ايضا في هذا المقرر باالضافة الى اساسيات لغة‬.‫الخبيرة‬
.‫ ( والتي تعتبر من اللغات االساسية المعتمدة في مجال الذكاء الصناعي والنظم الخبيره‬Prolog( ‫البرمجة‬
Course module objectives:
1. Introduce the concept of AI (definition, Applications area, benefits and
the related of AI with other knowledge fields)
2. Understand Knowledge representation Issues and searching strategies.
3. Understand Expert systems applications, features, advantage,
disadvantage, basic component of Expert System Architecture and
work.
4. Enhance student's problem-solving skills.
5. Introduce basic Logic programming statements and concepts.
6. Understand prolog operators, data types, constant, variables and
arithmetic operations.
7. Understand the main concepts involved in Prolog programming
language: Fact, Rule and Query.
8. Understand how we can write a project using Prolog with different
methods
Course/ module components
 Text Book
Introduction to Artificial Intelligence, Walfgang Ertel and Nathaneal T. Black,
Springer, 2011
In addition to the above, the students will be provided with contributions by the
lecturer.
HOMEWORK:
Homework is an essential part of the educational process. The homework in this
course will reinforce the material covered in the classroom and provide time for
practice. Students will earn points for each homework assignment completed.
Homework assignments will be graded based on completion.
Teaching methods:




Duration: 16 weeks in first semester, 48 hours in total
Lectures: 32 hours (2.5 hours per week).
Lectures Assignments
Laboratories: Work with practical exercises and discussion groups.
Learning outcomes:
 Knowledge and understanding






Learning basic principle of Artificial Intelligence and Expert Systems.
How to use different types of knowledge representation
Dealing with Expert system and their tasks.
Learning different types of Expert Systems Building methodologies.
Learning basic principle of Logic Programming.
Enable students to write a project using prolog language through different stages
(Knowledge Base design, coding and running programs)
 Cognitive skills (thinking and analysis).
- The lecturer will present the material in interactive ways that motivate the thinking
side of students.
- Performing the learning objectives for each module components in clear manner to
cover the material and asked questions by the students.
- encourage students to thinking by permit group discussions and practical sessions in
an effective way
 Communication skills (personal and academic).
-Module language: English
-For every lecture the beginning minutes will be open for discussion and review about
the last lecture. For further discussion, office hour as come into view in first page will
help the students to suggest more questions and queries.
Time Management: therefore Assignments and Prolog programs are diverse and
incorporated. Those requirements give responsibilities to students to planning their
own work as well as early time management skills.
Project Development: Assignment and oral presentation using visual aids help
students to distinguish more researches and projects in related subjects and enhanced
programming skills.
Group Management: Each student will be able to show effective leadership styles,
teamwork and collaborative behavior.
 Practical and subject specific skills (Transferable Skills).
Using Prolog language, aim students toward learning computer programming in Logic that is
suitable for Artificial intelligence and Expert applications.
Assessment instruments




Short reports and/ or presentations, and/ or Short research projects
Quizzes.
Home works
Final examination: 50 marks
Allocation of Marks
Assessment Instruments
Mark
First examination
20
Second examination
20
Final examination: 50 marks
40
Reports, Quizzes, Home works
20
Total
100
Documentation and academic honesty
 Documentation style (with illustrative examples)
This course is given from the textbook mentioned above mainly and from other books
and website listed below, copyright protected. Students are encouraged to purchase
reference.
 Avoiding plagiarism.
Course Contents
week
Basic and support material to be
covered
(1)
Introduction to Artificial Intelligence, AI
definitions , Characteristics and history
(2)
The major application area of Artificial
Intelligence , Searching strategies and
Terminology
(3)
Knowledge Representation, Reasoning &
Control, Production Rule Systems,
Backward Search, Forward Search
(4)
General structure of Expert System,
Overview, Expert System Architecture,
Major Components of Expert Systems ,
Knowledge base, Inference engine and User
interface
(5)
(6)
First
examination
(7)
(8)
(9)
Expert Systems features, advantages and
disadvantages, other issues
Different ES applications and examples ,
major ES terms, different types of Expert
Systems
First Exam, Knowledge representation
Issues, Advantages and Disadvantages ,
Production rules, Semantic networks Logic
statements and design issues
Graph Theory, Searching Strategies, Using
State space to represent reasoning, Heuristic
Search, Control and Implementation of state
space search
Introduction to logic programming, prolog
Homework/reports
and their due dates
programming language, definitions, Facts,
Rules, Prolog KB and Queries, and
Examples.
(10)
(11)
Second
examination
(12)
Matching, Search and unification, and
Programming with matching examples and
practical applications.
Prolog Structure, Atoms, Numbers Variables
and Complex terms and practical examples
and applications using prolog language
Arithmetic in Prolog, Comparison Operators,
Comparing integers and practical examples
and applications using prolog language.
(13)
List definition, Lists as terms, Examples,
Reversing a list, Arithmetic and lists and
Practical sessions.
(14)
Introduces the concept of machine learning.
Definition, Architect and related issues.
Artificial Neural Networks (ANN)
Introduction to NN, NN definitions, Backpropagation, Implementations and examples
Tutorial, revision and Practical Exam,
Comprehensive review for all the topics
learned in the whole semester, report
discussions and Final Exam.
(15)
(16)
Final
Examination
Expected workload:
On average students need to spend 2 hours of study and preparation for each 50-minute
lecture/tutorial.
Attendance policy:
Absence from lectures and/or tutorials shall not exceed 15%. Students who exceed the 15%
limit without a medical or emergency excuse acceptable to and approved by the Dean of the
relevant college/faculty shall not be allowed to take the final examination and shall receive
a mark of zero for the course. If the excuse is approved by the Dean, the student shall be
considered to have withdrawn from the course.
Module references
Books

Artificial Intellegence G.Luger , Addison Wesley, 5th Edition, 2005

Artificial Intelligence a Modern Approach 2nd Edition, Stuart Russell & Peter Norvig,
Printice Hall, 2003.

Artificial Intelligence: A Guide to intelligent systems, 1 st Edition, Michael Negnevitsky,
Addison Wesley, 2002.
 Decision Support Systems and Intelligent Systems, 6th Edition, Efraim Turban, and Jay E.
Aronson, Prentice Hall, 2001.
Journals
www.ACM.org
www.IEEE.org
Websites
http://www.learnprolognow.org