Year:
2006/2007
Course Title
Course Number
Instructor
Office Location
Office Phone
Office Hours
Email
Jordan University of Science and Technology
Faculty of Computer & Information Technology
Department of Computer Science
Semester: 1st semester (Fall)
Course Information
Advanced Artificial Intelligence
CS 762 (3H + 0L)
Dr. Hassan Najadat
Ph4 L0
7201000 Ext. 23405
Sunday 10:15-12:15, Tuesday: 10:15- 11:15.
You are always welcome at my office hours. Please contact me, if you are
unable to make my office hours.
najadat@just.edu.jo
Catalog Description
Introduction to the types of problems and techniques in Artificial Intelligence. Problem-Solving methods.
Major structures used in Artificial Intelligence programs. Study of knowledge representation techniques
such as predicate logic, non-monotonic logic, and probabilistic reasoning. Application areas of AI such as
game playing, expert systems, natural languages understanding and robotics. Project assignments in one of
the AI programming languages.
Title
Author
Publishers
Year
Edition
Book Website
References
Text Book
Artificial Intelligence structures and strategies for complex problem solving
George F. Luger
Addison-Wesley
2005
5
http://www.pearsoned.co.uk/HigherEducation/Booksby/Luger/
 Artificial Intelligence: A Modern Approach, 2nd Ed., Russell Stuart & Peter
Norvig, 2003.
 Expert Systems: Principles and Programming,4 th Ed., Joseph C. Giarratano,
Gary D. Riley, 2005.
 The ANSI Common Lisp, by Paul Graham (Prentice Hall, 1996)
Assessment Type
Midterm
Project (term paper)
Readings and assignments
Final Exam
Assessment Policy
Expected Due Date
TBA
TBA
TBA
Weight
25%
15%
10 %
50%
Course Objectives
This course provides students major areas of AI including theorem proving, heuristic search,
problem-solving, computer analysis of scenes, robotics, natural language understanding, and
knowledge-based systems:
Weights
(20%)
1.
The student will be able to solve problems using the AI techniques and the overall AI
philosophy.
2.
The student will be able to develop programs in LISP, PROLOG and other AI
programming notations.
3.
Introduce the foundations of Artificial Intelligence (including search, logical induction,
constraint satisfaction problem, natural language, and different approaches to automated
learning).
(20%)
4.
Understanding of knowledge-intensive system architectures, including rule-based, casebased, and model-based systems.
(10%)
5.
Understanding of issues underlying reasoning under uncertainty and knowledge of various
approaches, including logics for non-monotonic reasoning, truth maintenance systems,
and stochastic systems.
(10%)
6.
Understanding of the algorithms of various machine learning paradigms and their
capabilities.
(30 %)
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

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Teaching & Learning Methods
lecture notes, homework assignments, and projects are designed to achieve the course objectives.
You should read the assigned chapters before class, complete assignments on time, and participate in
class among other things to understand the material. You should ask questions, whether in class or
during office hours.
You are responsible for all material covered in class.
If you have any concerns, please communicate them to me either in class or by email.
Related
Objective
1
1
2
3
3
3
4
5
5
6
(10%)
Learning Outcomes
The expected achieved outcome
Provide a brief history of major areas of AI
Understand the predicate calculus to describe the essential features of
problem
Write intelligent programs using LISP and Prolog programming
language.
Ability to represent a problem solution as a path in a graph from a start
to a goal.
Know most search algorithms and data structures used to implement
search.
Define different problems in constraint satisfaction analysis and know
the main approaches of forward and backward checking
Identify different representations for AI such as conceptual
dependency, semantic nets, frames, rule-based expert system along
with case-based reasoning
Draw useful conclusions from incomplete and imprecise data with
unsound reasoning using truth maintenance and logic based abduction.
Build models for reasoning with uncertainty using Bayesian models,
belief networks, Dempster-Shafer, and the Stanford certainty algebra.
Ability to discover useful information using various algorithms for
Reference
Ch 1
Ch2
Ch 15, ch 16
3.1
3.2-3.4, ch 4
[Ch 5 Russel l]
Ch 6, 7, 8
9.1
9.2
Ch 10
symbol-base learning which includes induction, concept learning, and
ID3
Know neural networks architectures including perceptron learning, and
back propagation.
Understand genetic algorithms and evolutionary approaches.
Know different approaches of language understanding .
6
6
3
Week
1
2
3
4
5
6/7
7/8
8/ 9
9
10-11.5
11.5- 12
13
14-16
Readings
Office Hours
Homeworks
Drop Date
Midterm
Project Policies
Course Contents
Topics
AI: history and applications
The predicate calculus
An introduction to LISP
Structures and strategies for state space search
Heuristic search
Building control algorithms for state space search.
An introduction to PROLOG
Constraint Satisfaction Problem
Knowledge Representation [ Semantic Nets, Frames, Conceptual
Graphs,…]
Strong Method problem Solving [Rule-Based Expert System,
Case-Based,…]
Reasoning in uncertain Situations [Logic-Based Adductive Inference,
Abduction, Stochastic Approach]
Machine learning: symbol-Based
Midterm
Machine learning: Connectionist [Neural Network]
Machine learning: The Genetic Algorithms
Understanding Natural Language
Project/Research paper presentations
Ch 11
Ch 12
Ch 14
Readings
Chapter 1
Chapter 2
Chapter 16
Chapter 3
Chapter 4
Chapter 6
Chapter 15
Chapter 5 Ref1
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 14
Additional Notes
Readings in addition to the book will be required. Occasionally notices will be posted
to the class web page. You are responsible for checking this information weekly.
Feel free to send me questions by electronic mail. It will be usually answered the
same day. If you wish to see me in my office, please make an appointment by email. I
am generally available, and I think that such flexible office hours will work better for
you.
 Homeworks are due at the beginning of class
 Late homeworks will not be accepted
 All works have to be done independently
 Students handing in similar homeworks will receive a grade of 0 (ZERO) and
face possible disciplinary actions
 The last day to drop the course is the 14-12-2006
 You are expected to take the midterm.
 There will be no make-up unless you have a valid medical reason to have
missed the midterm confirmed officially.
 Each student will pick a topic, a new RESEARCH idea of yours, NOT A
PRESENTATION OF IDEAS FROM SOME PAPER.
 Students will research the topic, write a quality paper and give a quality
presentation on it.

Required
materials
Attendance
Code of Conduct
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
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Presentations will be judged on contribution, interestingness, depth, correctness,
clarity, and insight.
Text, email, and WWW access are required.
Students are expected to attend all classes
If a student misses 10% of the classes without an acceptable reason, the student
will be assigned a grade of 35, according to the rules of JUST.
The assignments, and of course the quizzes, and exams need to be done
individually. Copying of another student's work or code, even if changes are
subsequently made, is inappropriate, and such work or code will not be accepted.
The University has very clear guidelines for academic misconduct, and they will
be enforced in this class.