Fall 2003 MCS 5503
01
Intelligent Systems
(Introduction to Artificial Intelligence)
Lawrence Technological University
Math and Computer Science Department
Day / Time: Thursday, 17:45-8:25pm
Credit Hours: 3
Prerequisite: MCS 2534 (Data Structures)
and (C++ or Java)
Lecture Room: S202
Lab.: CW21 (for some lectures, and if you choose robotics project)
Instructor: Chan-Jin Chung, Ph.D.
- Office Room: Science 112
- Phone: (248) 204-3504
- Fax: (248) 204-3518 (this fax number is for
the whole building and senders
should be sure to place instructor's name on the fax)
- Dept. Secretary: (248) 204-3560
- Math/CS Drop Box: in front of S120 door.
- Email: CHUNG@ltu.edu
- LTU webpage at http://www3.ltu.edu/~chung
(syllabus, announcements, lecture notes, etc.)
- my.ltu.edu - Black Board for the class
- Office Hours: check out
http://www3.ltu.edu/~chung and click on office hours
Required Text
- Title: Artificial Intelligence: A New Synthesis
- ISBN 1-55860-467-7, March 1998
- Author: Nils J. Nilsson
Recommended Text
- Artificial
Intelligence,
3/e, 1992, 750 p., Patrick
Henry Winston, Massachusetts Institute of Technology, ISBN: 0-201-53377-4
- Artificial Intelligence: A Guide to
Intelligent Systems, Michael Negnevitsky, S352 pages, 1st edition (September 15, 2001),
Addison Wesley Publishing; ISBN: 0201711591
Internet Resources
Course Objectives
- Introduction to Artificial Intelligence, Machine Learning,
and SoftComputing (simulation of intelligence in computers) (60%)
- Applying theories and techniques to the
development of practical Intelligent Systems
as class projects (40%)
- To
plan next classes such as MSCS capstone project I and II for MSCS students
Class Topics [15 weeks + 1 final]
Introduction: Introduction to Artificial Intelligence and Fundamental issues
in Intelligent Systems (Chap 1) [1 week]
Search and
Optimization methods (Chap 7, 8, 9, 10, 11, 12) [5 weeks]
-
Generate and Test and Problem Reduction
-
Nets and Space-State Search
-
Basic Search (Uninformed Search),
Heuristic Search and Optimal Search
-
Trees and Adversarial Search
-
Constrained Search, Constraint satisfaction
-
Nonlinear Numerical Function Optimization
- Combinatorial Optimization
Planning
- Introduction to multi-objective function optimization
Learning, Adaptation, and Reactive Machines (Chap. 2, 3, 4) [5 weeks]
-
Decision tree
learning
-
Introduction to
Version Space
-
Learning by Training
Perceptrons
-
Learning by Training
Artificial Neural Nets
-
Learning by Simulating
Evolution
State Machines, Autonomous Robotics and Robot Vision (chap. 5, 6, +) [3
week]
Introduction to
knowledge representation and reasoning methods (Chap. 13, 14, 15, 16, 17, 18, 19) [1
week]
- Knowledge-based systems
- Representing common sense knowledge, space, time,
events, and actions
- Reasoning: predicate calculus and resolution, rules and
rule chaining, logic, probabilistic reasoning, Bayes' theorem, reasoning
with uncertainty
- Introduction to Fuzzy Logic and Fuzzy Inference Systems
Tentative Schedule
|
Date |
Topics |
Note |
|
8/28 |
Introduction to AI and Intelligent Systems
|
First day
of Class |
|
10/30 |
Midterm
|
5:45-7:20pm
|
|
11/19 |
Last day to withdraw |
|
|
11/27 |
Thanksgiving |
No class |
| 12/6 |
LaptopBot project demo and competition (tentatively) |
Sat. Gym |
|
12/11
|
Project Demonstration
|
|
| 12/18 |
Final |
5:45-7:20pm |
Class Format and Grading: Total 200 points
- Homework assignments and project - 105 points
- 1 midterm: 40 points
- 1 final (everything covered in class) - 55 points
This score will be translated into a letter grade based upon the percentages
given below. (F will be given to Grad students, if under 69%)
| A |
90-100% |
C |
70-74% |
| A- |
89% |
C- |
69% |
| B+ |
85-88% |
D+ |
65-68% |
| B |
80-84% |
D |
60-64% |
| B- |
79% |
D- |
59% |
| C+ |
75-78% |
F |
00-58% |
Class Policies
- Attendance is essential to doing well in the course. The exams
will focus primarily (but not exclusively) on material presented in the
lectures.
- If you are unable to attend a meeting, it is your
responsibility to obtain the material from other students, instructor, or from the web.
- Class events may be photographed and/or video-taped.
Student should give permission for this material to be printed, published, posted on the
websites, and/or televised in the public forum.
Exam. Policies
- There will be no makeup exams will be given.
- Closed books, closed notes by default; and closed
neighbors.
Homework Policies
- Homework programming assignments must be done individually.
- Source codes must follow good programming standrads such as commenting, indentation,
and meaningful names.
- Must be submitted before or at the beginning of the class on the due date.
- Read the submission instruction carefully for each homework
- See the "Policy on late homework or project" below.
- Some homeworks are for class competions based on the results. Winners will be recognized
in variuos ways.
Class Projects
Students are supposed to select one project from the following table (A group project may be possible):
| Project Name |
Pre-requisites and Required Techniques (Common: At least two year's of
programming experience) Note that some topics cannot be covered in detail in regular
classes |
| Laptop Robotics for Obstacle Race |
C++ windows programming or Java |
| Laptop Robot and IGVC simulator |
Java preferred |
| RoboCup Soccer Simuation Divison for RoboCup 2004 Lisbon, portugal
http://www.robocup.org,
download |
Internet programming |
| Khepera Robot Soccer |
C++ |
| EANN for (Lego) Robots |
Java |
|
CEC 2003 Competitions |
denpend on each problem |
| Make 7 game |
Java Applets and Min-Max trees |
| Generalized Evolutionary Data Mining Engine |
Java and GP |
| Your own project or New Projects by the instructor |
Should be approved by the instructor |
Policy on late homework or project
- Full credit at the beginning of class on the due date
- 10% deduction per day (24 hours)
Intellectual Property and Copyrights
All the
deliverables may be reused/modified/upgraded by another students and/or
instructor later on for educational purposes. The instructor will make sure
to give appropriate credits and acknowledgements to the
student in that case.
The instructor believes that the student has the intellectual property rights
of the software student wrote. However, since it is done in a class at LTU, it is also requested that the
student should give appropriate credits and acknowledgements to the University as well as
the instructor, if the software is used or commercialized after the class.
Policy on Academic Misconduct
- Plagiarism is a serious academic offense. DO NOT COPY THE WORK OF
OTHERS. Failure to observe this will result in zero point for the
assignment and will be handled in accordance with University Policy.
- Cheating during exams is also a very serious academic
offense and will be handled in accordance with University Policy.
8/28/03