CS 480 / 001

to Artificial Intelligence


Sean Luke


Tuesday, 4:30 PM to 7:10 PM, in Robinson Hall B203.


CS330 and CS310, no exceptions.

Relationship with CS580

CS480 and CS580 will be taught in the same semester and by the same instructor this year, and so will share a common website. While the courses will have similar topics and to some degree assignments, they will have quite different levels of difficulty.


CS480 does not require a textbook, but it is very strongly recommended that you pick up the following text: ANSI Common Lisp by Paul Graham. We may refer to that text in class. (BTW, if you're way too much into Common Lisp, you might also pick up this too).

Ordinarily CS 480 would also require this text, generally considered the best AI book, but this year I'm going to try going only with lecture notes. If you're into AI for the long haul, you should get this book anyway.

About the Class

This course will begin by covering the basics of Lisp and the philosophy of Artificial Intelligence, plus discussion of simple systems, architectures, and platforms (robotics, etc.). From there we will discuss methods in learning (neural networks, decision trees, optimization, and time permitting, reinforcement learning). Then the course will turn to issues in problem solving and search, game design, and representation.

This course will be very challenging but (I hope!) interesting and eye-opening. Artificial Intelligence is a broad interdisciplinary field with a strong tradition in exploratory programming. You are expected to know the material in CS310 and CS330 well, and be able to get up to speed rapidly doing software development with strange new programming languages. Learning Lisp is a nontrivial endeavor. You should also be prepared to discuss and think about philosophical issues and be able to draw ideas from areas outside of computer science.

Course Web Page



Grading will be divided roughly as follows: 25% Midterm, 25% Non-cumulative Final Exam, 50% Course Assignments.

Honor Code

The class enforces the GMU Honor Code, and the more specific honor code policy special to the Department of Computer Science. You will be expected to adhere to this code and policy.


If you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and come talk to me about accommodations.

Course Outcomes

1. A knowledge of basic uninformed and heuristic search techniques. 2. A knowledge of basic logic or probabilistic reasoning techniques. 3. A knowledge of basic machine learning techniques. 4. An ability to implement basic AI methods in Lisp or in Prolog. 5. An ability to identify and apply an appropriate AI method to a given problem.