Year:
2007/2008
Course Title
Course Number
Prerequisite
Instructor
Office Location
Office Phone
Office Hours
Email
Teaching Assistant
Jordan University of Science and Technology
Faculty of Computer & Information Technology
Department of Computer Science
Semester: 1st semester (Fall)
Course Information
Artificial Intelligence
CS 362 (3C=3H+0L)
CS 213 (Advanced Data Structures) + CS 284 (Algorithms)
Dr. Hassan Najadat
Ph4 L0
7201000 Ext. 23405
Sun, Tue,Thu 10:15 – 11:15 or by appointment
najadat@just.edu.jo
TBA
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(s)
Publishers
Year
Edition
Book Website
LISP Language
References
Text Book
Artificial Intelligence A Modern Approach
Stuart Russell & Peter Norvig
Prentice Hall
2003
2nd edition
www.aima.cs.berkeley.edu
www.lispworks.com
1. Artificial Intelligence: structures and strategies for complex problem solving (2 nd
ed), by George F. Luger and William A. Stubblefield, Addison Wesley, 1998.
2. Common Lisp: The Language; Steele, G. L.; 2nd edition, Bedford MA, Digital Press
1990.
3. Essentials of Artificial Intelligence, by Matt Ginsberg, Morgan Kaufmann
Assessment Type
First Exam
Second Exam
Final Exam
Quizzes and Homeworks
Total
Assessment Policy
Expected Due Date
TBA
TBA
TBA
TBA
Weight
20%
20%
40%
20%
100%
Course Objectives
1) Gain a historical perspective of AI and its foundations and establish the cultural background
against which it has developed.
2) Know characteristics of programs that can be considered "intelligent".
3) Provide a thorough understanding of the types of problems solved using AI techniques and
understand the different strategies for state space search.
4) Write LISP programs to solve AI problems.
5) Know a thorough treatment of the different types of heuristic search
6) Explore constraint satisfaction problems whose states and goal test conform to a standard,
structured, and very simple representation.
7) Know classical examples of artificial intelligence such as game playing.
8) Provide a thorough treatment of the knowledge representation languages, which includes
propositional calculus, predicate calculus, and first order logic.
9) The specification of different architectures for AI problem solving and inductive learning.




Weights
(5 %)
(5 %)
(15 %)
(10 %)
(15 %)
(10 %)
(10 %)
(20 %)
(10%)
Teaching & Learning Methods
Class lectures, 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, 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 in class, or by phone or email.
Related
Objective
1.1
2.1
2.2
3.1
3.2
3.3
3.4
4.1
5.1
5.2
5.3
6.1
6.2
6.3
7.1
7.2
8.1
8.2
Learning Outcomes
The expected achieved outcome
Know the definition of AI, the foundation of AI, and the different application.
Ability to define the rational agents and its environment
Distinguish the characteristics and structure of each intelligent agent
environment.
Know how to describe goal-based agent
Define the main elements of that constitute a problem and its solution with
different examples
Provide search techniques that use search tree and blind search tools
Ability to provide search techniques under partial information with ability to
avoid repeated states
Ability to write intelligent agent programs using LISP
Provide informed search strategy that uses problem specific knowledge such as
best first search, greedy best first search, A* search and others.
Examine the nature of heuristics in 8-puzzle and explore local search algorithms
Explore search spaces systematically and optimization problems in both discrete
and continues spaces using online and offline searches
Know the main features of CSP and apply backtracking search for CSP
Apply the local search for CSP
Apply the constraint graph using connected components and tree decomposition
Explain the state of games and defining the different optimal decisions
strategies such as minimax algorithm.
Use pruning search strategies to reach the goal quickly such as Alpha-Beta
pruning.
Provide an overview of all the fundamental concepts of logical representation
and reasoning.
Provide the concepts of propositional logic PL and its semantics with depth
reasoning patterns in PL
Reference
1.1, 1.2, 1.3
2.1, 2.2
2.3, 2.4
3.1
3.2
3.3, 3.4
3.5 , 3.4
Lisp notes
4.1
4.2, 4.3
4.4, 4.5
5.1, 5.2
5.3
5.4
6.1 , 6.2
6.3, 6.4, .5,.6
7.1-7.3
7.4 -7.7
8.3
Examine the syntax of the first-order logic and its semantics to represent a good
deal of knowledge.
Introduce inference rules for quantifiers and shows how to reduce first order
inference to propositional inference
Examine the forward chaining and backward chaining and its resolutions.
Define different areas in learning from observations.
8.4
8.5
9.1
Week
1
2
3-6












7/8
8/9
9
10
11
12
13/14
14/15
Homeworks






Exams
Cheating


Attendance


Course Content
Topics
Introduction
Intelligent Agents
Problem Solving
LISP programming
Informed search methods
Constraint Satisfaction Problems
Adversial Search
Logical Agents
First-Order Logic
Inference in First-Order Logic
Knowledge Representation
Learning from Observations
8.1- 8.4
9.1-9.2
9.3-9.5, 10.
18.
Chapter in Text
1
2
3
Lisp Notes
4
5
6
7
8
9
10
18
Additional Notes
Homeworks are due at the beginning of class
Late homeworks will not be accepted
All work has to be done independently
Submit a hardcopy of your homework with your name, ID# and homework# clearly
written
E-mail submissions will not be allowed under any circumstances
Students handing in similar homeworks will receive a grade of 0 (ZERO) and face
possible disciplinary actions
There will be NO MAKEUP assignments or quizzes
Cheating is illegal at JUST, and any student caught cheating will be dealt with
according to the rules and regulations of the university.
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.