Artificial Intelligence Syllabus

Instructor: Dr. John F. Santore
Phone: 508-531-2226
Office: Hart 220
Instructor Web Page:
Course Web Page:

Office Hours: I also will take appointments if you cannot make my other office hours, however, I generally have meetings and work prepared for a day or two ahead so plan on about 48 hours from the time I get your request to us being able to meet.

Course Description:

From the Catalog:
This course is an introduction to LISP or another AI programming language.Topics are chosen from pattern recognition, theorem proving, learning, cognitive science, and vision. It also presents introduction to the basic techniques of AI such as: heuristic search, semantic nets, production systems, frames, planning, and other AI topics.

We will be using the agent oriented approach for most of this class. It ties together the various and diverse aspects of artificial intelligence using agents as an underlying theme.

Artificial Intelligence: A Modern Approach  (Second Edition) by Stuart Russell and Peter Norvig

Class Requirements and grading:

This class will have both significant project and exam components. Students will have to pass both the project and exam portions of the class in order to pass the entire class.

Project related work: 45%
Exams (one midterm and one final): 50%

Projects will need to be done in either lisp or python. c like languages are not well suited for AI and will not use used in this course.

The Midterm will be scheduled for Thursday March 1. The final will be scheduled by the college.

Academic Integrity:

See for a complete description of the academic integrity procedure at Bridgewater.

Academic integrity will be taken very seriously in this class. All individual work must be your own. If you cheat or otherwise represent the work of others as your own. You will receive an F for the course.

Guidelines for proper academic integrity:

Discussing problems with your classmates can help you understand the problems and kinds of solutions to those problems that you will learn about in this class. In an effort to make in clear what sort of discussions are appropriate and encouraged in this class and which cross the line to academic dishonesty I use the following guidelines: You may discuss any out of class problem I assign in this class with your classmates or other so long as no one is using any sort of recording implement including, but not limited to, computers, pdas, pens, pencils, phones etc. This lets you talk about theoretical solutions without sharing the actual implementations. As soon as anyone in the group is typing, writing etc, all conversations must stop. You may look at someone else's program code only very briefly in order to spot a simple syntax error. As a rule of thumb, if you find yourself looking at someone else's code for more than about 30-45 seconds it is probably time to stop. If you are having trouble with your program, come to the instructors office hours for more help.

All in class exams and quizzes are closed book and closed neighbor. If you are found using a data storage device of any kind during one of these evaluations, you will be failed for the course.

Standards for in class behavior:

You are all adults and are expected to act as adults in this class. While questions are encouraged in this class, if a particular line of questioning is taking us too far afield, I will ask the student to come by my office hours or to see me after class.

Cell phones, pagers, electronic organizers and other devises should be silenced while in class. If you work of EMS or something similar, please turn your cell phones/ pagers etc to vibrate mode so that you are not disrupting others in the class.

In the unlikely case of trouble makers in the class, those who are simply attempting to disrupt the class will be asked to stop; those who will not, will be referred to the college for appropriate action.

Tentitive Schedule

Week Topic
Week 1 Introduction and basic python
Week 2 Python
Week 3 Introduction to agent issues
Week 4 Search.
Week 5 Logic and logical agents
Week 6 Knowledge representation
Week 7 Midterm
Week 8 Planning
Week 9 agent communication
Week 10 perception and action
Week 11 Learning
Week 12 Pattern recognition.
Week 13 TBA