Southern Methodist University
School of Engineering
Department of Computer Science and Engineering

CSE 5320/7320: Artificial Intelligence
Spring 2003

Location Junkins Bldg, Rm 203
Time Mon 6:30-9:20pm
Office hours Wed 6:45-7:45pm SIC Bldg, 3rd floor (CSE Dept), Rm 300A or open computer area
...also Mon 9:25pm-10pm in class after the lecture

Instructor Marius Pasca
Email mars at (preferred contact method)
Phone 972-231-0052

Course description:
Introduction to basic principles and current research topics in artificial intelligence (AI). Introduction to AI programming languages (Lisp, Prolog). Formal representation of real-world problems, search of problem spaces for solutions. Representation and deduction of knowledge in predicate logic and first-order logic. Planning of a sequence of actions to achieve a goal. Handling of uncertain knowledge, learning and neural networks.

Prerequisites: A grade of C- or better in both CSE 3342 and CSE 3358.

Course materials:
Textbook (required) "Artificial Intelligence: A Modern Approach", Second edition, Stuart Russell and Peter Norvig, Prentice Hall, ISBN 0-13-790395-2, Published Dec 2002.
Lisp (required) "Ansi Common Lisp", Paul Graham, Prentice Hall, ISBN 0-13-370875-6, Published 1996.
"Common Lisp the Language", Second edition, Guy Steele, Butterworth-Heinemann, ISBN 1-55-558041-6, Published 1990.
"LISP", Third edition, Patrick Winston and Berthold Horn, Addison-Wesley, ISBN 0-201-08319-1, Published 1989.
Prolog (required) "Programming in Prolog", Fourth edition, W. Clocksin and C. Mellish, Springer Verlag, ISBN 3-540-58350-5 and 0-387-58350-5, Published 1994.
Prolog is accessible on the engineering machines (engr.smu) at /users/project/aiclass_prolog/speed/ ; start by reading the readme.txt file.

Class mailing list:
A mailing list has been created for class discussions. Every student in the class is expected to subscribe and be an active participant. Anything related to the course and potentially of interest to a significant percentage of the class constitutes acceptable material for the mailing list.
Discussions may include homework issues. Feel free to post your questions on the mailing list. Also, try to answer questions submitted by other students. This sort of feedback is important for achieving the overall objectives of the class. In addition, it will be considered class participation and rewarded according to the grading scheme.
The address for the mailing list is .
To subscribe, send an email to from your preferred email account. The body of the email (not the subject line) should be "subscribe ai" (no quotes).

Class notes in postscript format (use ghostview or ghostscript/gsview or equivalent to view and/or print; ghostview is available in Unix on engineering machines):
Lecture 1
Lecture 2 Lisp commented code and examples of usage
Lecture 3
Lecture 4 Lisp commented code
Lecture 5
Midterm review: starting with a short quiz Solution to Quiz 1
Midterm solution (also in pdf format)
Lecture 6
Lecture 7
Lecture 8
Lecture 9
Lecture 10
Lecture 11
Final review

Homework assignments (tentative schedule):
Homework 1
Solution to Homework 1
issued Jan 18 due Jan 27, 6:30pm
extension: due Jan 30 (Thu), 11:59pm (electronic);
Jan 31, 6:30pm (hardcopy)
Homework 2
Solution to Homework 2
issued Jan 31 due Feb 13, 6:30pm
Homework 3
Solution to Homework 3 plus figures
issued Feb 19 due Feb 27, 6:30pm
extension: due Feb 28 (Fri), 6:30pm
Homework 4
Solution to Homework 4
issued Mar 3 due Mar 17, 6:30pm
Homework 5
Solution to Homework 5
issued Mar 31 due Apr 7, 6:30pm
Homework 6 issued Apr 9 due Apr 21, 6:30pm
extension: due Apr 23, 6:30pm

Projects (for graduate students - CSE7320)

Tentative grading scheme for CSE5320 (undergraduate):



Class participation


Midterm exam


Final exam


Tentative grading scheme for CSE7320 (graduate):



Class participation


Midterm exam




Final exam


Tentative syllabus:


Lecture description


Jan 13

Intro to AI; Lisp

Chap 1

Jan 20

University holiday


Jan 27

Lisp; Agents

Chap 2

Feb 3

Uninformed search

Chap 3

Feb 10

Informed search

Chap 4

Feb 17

Constraint satisfaction; games

Chap 5, 6

Feb 24

Class cancelled (university closed)

Chap 7

Mar 3

Midterm review

Mar 10

Spring break


Mar 17

Midterm exam; Representation and reasoning in propositional logic

Chap 7

Mar 24

Representation and reasoning in first-order logic

Chap 8, 9

Mar 31

Prolog; Knowledge representation

Chap 10

Apr 7

Prolog; Planning

Chap 11

Apr 14

Prolog; Uncertainty

Chap 13

Apr 21

Learning and neural networks

Chap 18, 20

Apr 28

Project presentation; Final exam review


May 5

Final exam Monday, May 5, 6:30pm in Junkins Bldg, Rm 101.


Disability accommodations:
If you need academic accommodations for a disability, you must first contact Ms. Rebecca Marin, Coordinator, Services for Students with Disabilities (214-768-4563) to verify the disability and establish eligibility for accommodations. Then you should schedule an appointment with the professor to make appropriate arrangements.

Religious accommodations:
Religiously observant students wishing to be absent on holidays that require missing class should notify the instructor in writing at the beginning of the semester, and should discuss in advance with the professor acceptable ways of making up any work missed because of the absence.

Academic dishonesty:
May be defined broadly as a student's misrepresentation of his or her academic work or of the circumstances under which the work is done. This includes plagiarism in all papers, projects, take-home exams, or any other assignments in which the student represents work as being his or her own. It also includes cheating on examinations, unauthorized access to test materials, and aiding another student to cheat or participate in an act of academic dishonesty. Failure to prevent cheating by another may be considered as participation in the dishonest act.

University honor code:
Intellectual integrity and academic honesty are fundamental to the processes of learning and evaluating academic performance; maintaining them is the responsibility of all members of an educational institution. The inculcation of personal standards of honesty and integrity is a goal of education in all the disciplines of the University. The faculty has the responsibility of encouraging and maintaining an atmosphere of academic honesty by being certain that students are aware of the value of it, that they understand the regulations defining it, and that they know the penalties for departing from it. The faculty should, as far as is reasonably possible, assist students in avoiding the temptation to cheat. Faculty must be aware that permitting dishonesty is not open to personal choice. A professor or instructor who is unwilling to act upon offenses is an accessory with the student offender in deteriorating the integrity of the University. Students must share the responsibility for creating and maintaining an atmosphere of honesty and integrity. Students should be aware that personal experience in completing assigned work is essential to learning. Permitting others to prepare their work, using published or unpublished summaries as a substitute for studying required materials, or giving or receiving unauthorized assistance in the preparation of work to be submitted are directly contrary to the honest process of learning. Students who are aware that others in a course are cheating or otherwise acting dishonestly have the responsibility to inform the professor and/or bring an accusation to the Honor Council. Students and faculty must mutually share the knowledge that any dishonest practices permitted will make it more difficult for the honest students to be evaluated and graded fairly, and will damage the integrity of the whole University. Students should recognize that their own interest, and their integrity as individuals, suffer if they condone dishonesty in others.

Marius Pasca mars at