CMSC 671: Introduction to Artificial Intelligence
Fall 2010

Last revised 8/19/10
Instructor: Marie desJardins
mariedj @ cs.umbc.edu
ITE 337
410-455-3967
Office Hours: Mondays/Wednesdays 1:30-2:30.

Teaching Assistant: Xianshu Zhu
xianshu1@umbc.edu
ITE 353
Office Hours: Mondays/Wednesdays 3:45-4:45.

Course Description

This course provides an introduction to artificial intelligence concepts and techniques. Specific topics we will cover include the agent paradigm in AI systems, search, game playing, knowledge representation and reasoning, logical reasoning, planning, uncertain reasoning and Bayes nets, multi-agent systems, machine learning, the history and philosophy of AI, and the Lisp programming language.

Prerequisite

The equivalent of an undergraduate degree in computer science. In particular, students should have completed CMSC 341 (or the equivalent) and have strong programming skills. CMSC 441 or exposure to the theory of complexity of algorithms will also be useful. You should know the fundamentals of propositional and first-order logic, probability theory, and big-O complexity analysis. A pretest will be given to assess your familiarity with this material.

Course Schedule

This course schedule is subject to change. We will follow the Russell and Norvig textbook fairly closely, with some additional background material on other topics of interest.

When and Where

Monday and Wednesday from 2:30-3:45 in Sondheim 110.

Textbooks

We will be using the following textbooks: I will provide a number of online resources for learning Lisp, in addition to the reference manual listed above, so you may choose whether you would like to purchase the Paul Graham textbook.

Communication

As you will learn, I am a strong believer in two-way communication. I expect all students to participate in classroom discussions, both by asking questions and by expressing opinions.

In return, I will make myself available to answer questions, listen to concerns, and talk to any student about topics related to the class (or not). I welcome your feedback throughout the semester about how the course is going.

In addition to regular office hours, I maintain an open-door policy: you should feel to stop by to ask questions, or just say hello, whenever my door is open (which it generally will be unless I am out of the office, in a meeting, or deep in thought). (I'm not that great at remembering names, so please don't be offended if I ask you several times to re-introduce yourself!) I will also make a concerted effort to answer e-mail within 24 hours.

Grading

Course grades will be based on the following work. The final weighting may be changed slightly.
 
Homework (five assignments) 25%
Mini-project 10%
Course project 25%
Midterm exam 10%
Final exam 20%
In-class research article presentation 5%
Class participation 5%

Please refer to the class grading policy.

Homework

There will be five written homework assignments and a "mini-project" that will have the same late policy as the homeworks. Each assignment will have a due date and is expected to be turned in on time. Extensions of up to one week may be granted on an individual basis, if requested in advance. Repeated requests for extensions, or requests for extensions at the last minute, will be denied other than in extraordinary circumstances.

Homeworks will be due at the beginning of class on the due date.  A penalty for late homework will be applied as follows:

Please do not walk into class 20 minutes late and expect your homework to be counted as on time. Homeworks must be handed in as hardcopy. (For the mini-project, you will need to print and turn in your documented code, along with the project writeup, as explained in the project handout.)

Course Project

Students will be expected to complete a course project that follows the structure of a research exploration of a particular topic in AI. These projects can be completed individually or in pairs. Students will (1) identify a concept or algorithm from the course to explore in more depth, (2) submit a proposal describing the planned project, (2) implement a working AI system based on their topic, (3) carry out an empirical analysis of their work, (4) demonstrate their implemented system to the course staff, and (5) write a formal research paper summarizing their findings.

Note that the mini-project (which must be completed individually) is designed to be a smaller-scale (and more structured) version of the course project, for students to get some practice in the expectations for the larger project.

Paper Presentation

Each student will be required to select a research paper from the recent AI literature and to give a short presentation on the paper at the end of the semester. The papers can be based on one of the course topics, or on an AI topic that is not covered in the main part of the course; students must have their selected paper approved by Dr. desJardins. The goals of this assignment are: (1) to give the students in the course a broader sense of the current directions of AI research, (2) to give you some practice in reading and understanding technical papers, and (3) to give you an opportunity to practice the skill of making formal research presentations.

Exams

There will be one in-class midterm examination and a final examination. The material covered by the exams will be drawn from assigned readings in the text, from lectures, and from the homework. Material from the readings that is not covered in class is fair game, so you are advised to keep up with the readings.

Academic Honesty

This course adheres to the Provost's statement on academic integrity:

"By enrolling in this course, each student assumes the responsibilities of an active participant in UMBC's scholarly community in which everyone's academic work and behavior are held to the highest standards of honesty. Cheating, fabrication, plagiarism, and helping others to commit these acts are all forms of academic dishonesty, and they are wrong. Academic misconduct could result in disciplinary action that may include, but is not limited to, suspension or dismissal. To read the full Student Academic Conduct Policy, consult the UMBC Student Handbook, the Faculty Handbook, or the UMBC Policies section of the UMBC Directory."

Cheating in any form will not be tolerated. All work submitted must be your own work, and use of any outside sources or help must be clearly documented. The penalty for violation of the class policy on academic honesty will be, at a minimum, a zero on the entire assignment. All students must read the course academic honesty policy and sign a statement saying that they have read and understood the policy.

Software

We will be using CLISP, a public-domain implementation of Common Lisp that is installed on the department and gl Unix machines, in /usr/local/bin/clisp. You can also download a version that will run on a PC (Linux or Windows) or a Mac.

671 Mailing List

You should subscribe to the class mailing list by visiting lists.umbc.edu and searching for the list "cmsc671". Class announcements, hints, and discussion of assignments will be posted on this list. You can also send messages to the list to ask questions of your fellow students and/or TA and professor.

General questions (i.e., anything that another student may also be wondering about) should be sent to the list, so that everyone will be able to benefit from the answers. Students are welcome to post answers to questions, even if the questions were directed at the instructor. However, the academic integrity policy above must be strictly adhered to. Clarifications of homework questions and pointers to useful resources are fine; answers (or even hints at answers) to homework questions are not. When in doubt, ask the professor.

Individual concerns, requests for extensions, questions about individual grades, and the like should be sent directly to Prof. desJardins.

Acknowledgements

Thanks to Tim Finin (UMBC), Berthe Choueiry (University of Nebraska - Lincoln), Daphne Koller (Stanford University), Eric Eaton (formerly of UMBC), and Don Miner (formerly of UMBC) for making their course materials publicly available on the web. Some of the course materials (slides and homeworks) have been adapted from those sources.