Course Syllabus

810:161

Artificial Intelligence

Fall Semester 2001

Basics

Instructor: Eugene Wallingford

• Office: 327 Wright
• Phone: 273-5919
• E-Mail: wallingf@cs.uni.edu
• WWW: http://www.cs.uni.edu/~wallingf/

• Click here for my my office hours and schedule

Resources

• Required text:
  • Essentials of Artificial Intelligence, by Matt Ginsberg

• The course web page: http://www.cs.uni.edu/~wallingf/teaching/161/

• The course mailing list, 810-161-01@uni.edu

  Note that to send messages to the course mailing list, you must send from the mailing address from which you are subscribed. By default, that is your acad.uni.edu e-mail address. If you'd like to be subscribed from some other address, just let me know.

• Resources on library reserve:
  • Essentials Artificial Intelligence, by Matt Ginsberg
  • Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig
  • The Elements of Artificial Intelligence, by Steven Tanimoto
  • Artificial Intelligence, by Patrick Henry Winston

Course Goals

Artificial Intelligence (AI) is devoted to the computational study of intelligent behavior. This course introduces the fundamental issues in the field of AI. Intelligent behavior includes a wide range of phenomena, such as perception, problem solving, use of knowledge, planning, learning, and communication. Scientists in the field have developed a wide range of techniques for modeling these phenomena, including state-space search, several knowledge representation schemes, and task-specific methods. The bulk of this course reflects this diversity. We will examine the fundamental questions and issues of AI and will explore the essential techniques. By the end of the first part of the course, you should feel comfortable:

• discussing a broad range of issues in the field of AI,
• using and discussing the basic techniques of the field, and
• evaluating potential applications of AI technology.

This course also serves as preparation for the study and development of knowledge-based systems (KBS), systems that use knowledge to produce intelligent behavior. The final component of the course focuses on KBS: techniques for problem selection, analysis, design, and implementation.

In addition to studying intelligent phenomena and the techniques of AI and KBS, though, I hope that this course will also help you to appreciate the excitement of AI, its possibilities and limitations, its big questions and its big successes. I hope that by the end of the semester you understand why AI still captivates so many people with its promise -- and what this promise means for you as a practicing computer scientist.

Course Requirements

Class sessions -- Our class meetings will consist of a mixture of lecture, discussion, and in-class exercises. Much of our discussion will go beyond what you find in your textbook, so attendance is essential. You are expected to read assigned topics prior to the class session and to participate actively in class.

Homework assignments -- Over the course of the semester, you will complete six to ten homework assignments. These assignments will, at various times, involve applying techniques learned in class, writing about issues in the field, and doing experiments with AI programs.

Research paper -- You will write one research paper during the semester. This paper will give you an opportunity to explore a topic in some detail and to go through the process of preparing a research document. Writing is an indispensable skill for any computing professional. I hope that you will use this paper as an opportunity to improve your professional writing skills.

Exams -- Finally, there will be two examinations during the semester and a final exam at the end. The final exam will be comprehensive but will focus on material since the second in-class exam.

Course Evaluation

Grades will be determined on the basis of your performance on homework assignments, your research paper, and examinations. Final grades will be based on the following distribution:

Grades will be assigned using an absolute scale:

• 90% or above for an A,
• 80% or above for a B,
• 70% or above for a C,
• 60% or above for a D, and
• below 60% for an F.

This means that there is no curve. The only exception to the direct relationship between the score you earn and the grade you earn is this: You must complete and submit all assignments in order to earn a passing grade.

Notice that 50% of your final grade is earned from homework assignments and your research paper. I hope that this serves as an incentive for you to put substantial effort into writing clear and concise homework solutions and a convincing research paper.

For students taking the AI lab: Laboratory assignments will account for one-quarter (25%) of your total grade.

Course Policies

I try to accommodate student needs whenever possible, but I can only do so if I know about them. If you ever have to make alternate arrangements for a class session, an assignment, or an exam, contact me in advance. The safest way to make such arrangements is by notifying me via e-mail or phone of your circumstances and of how you can be reached.

My regularly-scheduled office hours are times when I am committed to provide assistance to you. No matter how busy I may appear when you arrive, the office hours are for you. You are welcome -- and encouraged -- to make use of that time. I am also available by appointment at other times if you cannot make an office hour.

All assignments are due at their assigned date and time. In order to receive partial credit, always submit your best effort at that time. Late work will not be accepted for a grade.

I encourage you to work together on homework assignments, to help you understand the problems and to encounter different points of view. However, any work you submit must be your own. Discuss ideas, but write your own answers. You should acknowledge any collaborations in the work you submit. Undocumented or unacceptable collaboration will be considered forms of academic dishonesty.

UNI has an established policy of academic integrity. Plagiarism and other forms of academic dishonesty will not be tolerated in this course. See the UNI catalog for details on the university's policy.

Computing Facilities

Most course materials will be made available via the World Wide Web during the semester. I also frequently send e-mail to inform you of breaking news and to answer common questions. E-mail and the web are, of course, accessible from all university computer laboratories. I leave you to choose and use web browsing and e-mail tools on any platform you like.

We may also use computing resources for this course to run existing AI applications as demonstrations. If we do, the CNS file computing system will be our home base. If you do not already have an account in CNS, you should apply for one during the first week of class.

Tentative Schedule

The following schedule gives a rough sketch of the topics we will cover and the distinguished dates this semester. If we need to re-schedule an exam, I will notify you at least one week prior to the exam date.

The FINAL EXAM is Wednesday, December 19, 2001, from 1:00 PM - 2:50 PM.