Department of Computer Science
Worcester Polytechnic Institute


This is an introductory graduate AI course.

During the first part of the semester we will cover general knowledge representation techniques and problem solving strategies. Topics will include semantic nets, search, intelligent agents, game playing, rule-based systems, logic programming, frames and inheritance, planning, and uncertain reasoning.

During the second part of the semester we will discuss three important application areas in AI: machine learning, machine vision, and natural language processing. More time will be spent discussing machine learning than the other two application areas. Machine learning topics will include: inductive learning, neural nets, and genetic algorithms.

For the catalog description of this course see the WPI Graduate Catalog.


Mon, Wed 3:30 - 5:00 p.m.

Students are also encouraged to attend the AIRG Seminar Thursdays at 11 am.


Prof. Carolina Ruiz
Office: FL 232
Phone Number: (508) 831-5640
Office Hours: Mondays 2:30 - 3:30 pm. Wednesdays 1:30-2:30 pm, or by appointment.

Other speakers will occasionally be invited to lecture to the class.



Familiarity with data structures and a recursive high-level language. (Knowledge of Lisp or Prolog is an advantage.)


Exam 1 20%
Exam 2 20%
Project 1 10%
Project 2 15%
Project 3 15%
Project 4 20%
Class Participation Extra Points

Your final grade will reflect your own work and achievements during the course. Any type of cheating will be penalized with an F grade for the course and will be reported to the WPI Judicial Board in accordance with the Academic Honesty Policy.


There will be a total of 2 exams. Each exam will cover the material presented in class since the beginning of the semester. In particular, the final exam is cumulative. The midterm exam is scheduled for Wednesday March 4 and the final exam is scheduled for Monday, April 27. Both will be in-class exams.


There will be a total of 4 projects. These projects may be implemented using any high level programming language (
Lisp, Prolog, C, C++, ...) More detailed descriptions of the projects will be posted to the course webpage at the appropriate times during the semester. Although you may find similar programs/systems available online or in the references, the design and all code you use and submit for your projects MUST be your own original work.

Project 1

Design and implementation of a computer program that plays tic-tac-toe.

Project 2

Design and implementation of a rule-based system for planning.

Project 3

Design and implementation of a learning system using neural networks and the error back propagation procedure.

Project 4

For this project, each student is expected to: (1) select an AI topic (mutually agreed upon with the instructor) that will not be covered in class; (2) research the AI literature on this topic; (3) identify a concrete problem related to this topic, and design and implement a prototype system that solves the problem; (4) write a webpage summarizing the relevant background knowledge and project results; (5) and give a 30 minute, oral, in-class presentation describing the achievements of this project.


Students are expected to read the material assigned to each class in advance and to participate in class. Class participation will be taken into account when deciding students' final grades.


The mailing list for this class is: cs534@cs.wpi.edu
You MUST subscribe to the mailing list by sending the following one-line email message to majordomo@cs.wpi.edu:
subscribe cs534


The web pages for this class are located at http://www.cs.wpi.edu/~ruiz/Courses/cs534_S98/ Announcements will be posted on the web pages and/or the class mailing list, and so you are urged to check your email and the class web pages frequently.


(See also the list of assigned papers in the
Class Schedule.)

General AI

The following additional references complement and/or supplement the material contained in the required textbook. I have listed them in decreasing order of interest according to my preferences.

  1. T. Dean, J. Allen, Y. Aloimonos. "Artificial Intelligence: Theory and Practice" The Benjamin/Cummings Publishing Company, Inc. 1995.

  2. B. L. Webber, N. J. Nilsson, eds. "Readings in Artificial Intelligence" Tioga Publishing Company, 1981.

  3. Patrick H. Winston. "Artificial Intelligence" 3rd edition Addison Wesley.

  4. S. L. Tanimoto. "The Elements of Artificial Intelligence Using Common Lisp" Computer Science Press 1990.

  5. E. Rich and K. Knight. "Artificial Intelligence" Second edition McGraw Hill 1991.

  6. P. Norvig "Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp" Morgan Kaufmann Publishers, 1992.

  7. M. Ginsberg "Essentials of Artificial Intelligence" Morgan Kaufmann Publishers, 1993.

  8. G. F. Luger and W. A. Stubblefield "Artificial Intelligence Structures and Strategies for Complex Problem Solving" Third edition Addison-Wesley, 1998.

  9. M.R. Genesereth and N. Nilsson, "Logical Foundations of Artificial Intelligence" Morgan Kaufmann, 1987.

Machine Learning

  1. Tom M. Mitchell "Machine Learning" McGraw-Hill, 1997.

  2. P. Langley "Elements of Machine Learning" Morgan Kauffamann Publishers, Inc. 1996.

Lisp/Prolog Textbooks and Manuals

  1. G. L. Steele Jr. "Common Lisp: The language'' 2nd edition Digital Press, 1990. (ISBN 1-55558-041-6)
    This reference is online.

  2. Patrick H. Winston and Berthold K.P. Horn "Lisp" 3rd edition.

  3. L. Sterling, E. Shapiro "The Art of Prolog" MIT Press, 1986.


Small changes to this syllabus may be made during the course of the term.