CSC261 Artificial Intelligence
Fall 2009

Synopsis: This class introduces the fundamentals of automated intelligence. Through the "eyes" of an intelligent agent, we will learn about searching for problem solutions, exhibiting rational behavior, handling uncertainty, and learning from experience.
MWF9:00-9:50 amScience 3819
Instructor:           Jerod Weinman
Office:Noyce 3825
Office hours:
Monday1:00-2:00 PM
Tuesday3:30-4:30 PM
Wednesday4:00-5:00 PM
Friday2:00-3:00 PM
or by appointment.

Course web page:


1  Overview
2  Textbook
3  Class Attendance
4  Schedule of Topics
5  Assignments and Activities
    5.1  Reading
        5.1.1  Preparation
        5.1.2  Reading Journal
    5.2  Participation
    5.3  Programming Assignments
    5.4  Exams
6  Grading
7  Academic Honesty
8  Deadlines
9  Contacting Me
10  Accommodations

1  Overview

What is intelligence? Can it be automated? These are deep questions whose algorithm roots go back thousands of years. In this course, we will examine computational approaches to problem solving, rational behavior, and learning. The field of artificial intelligence (AI) has gone through many ups and downs. AI has branched into many sub-areas that are beginning to be reunified with great success for many practical applications. We will study the foundations and theories behind the "big ideas" in AI, and spend some time exploring and implementing some real models for real problems.
Our major objectives for this course include:
*  Why take it?
Rational, learning, computer-based agents are being used to solve more, bigger, and often very important problems. If you have any interest in how this increasingly visible sub-field of computer science operates, this course is for you. Even if your occupation is not typically thought of as "AI work," exposure to these ideas may influence how you approach problem-solving and will definitely influence how you interpret future developments in the field of AI that you may learn about.
*  What do I need to know?
This course assumes you have experience with functional programming in Scheme. You should also be fluent in some basic computational ideas like encapsulation and code re-use with procedures and recursion. Familiarity with some basic data organization principles (i.e., linear and recursive data structures) are also necessary. Some mathematical maturity (e.g., familiarity with multivariable calculus, linear algebra, or combinatorics) will be helpful, but not absolutely necessary.

2  Textbook

Our course will be based on the following text:
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, Second Edition. Prentice Hall, 2002. ISBN: 978-0137903955
Our programming exercises will be (mostly) done in Scheme. An excellent Scheme reference/textbook is available free online, but a print edition is also available (you may also find an older copy in the CS Learning Center/MathLAN Library). We'll be using R5RS for this course, but a newer edition incorporating R6RS is also available.
R. Kent Dyvbig. The Scheme Programming Language, Third Edition. MIT Press, 2003. ISBN: 978-0262541480.

3  Class Attendance

Class meetings will involve a mix of discussions, collaborative activities, and the occasional mini-lecture. In short: You are expected to attend and actively participate in class. I am expected to make class worth attending.
I know that sometimes "things happen." Therefore, you will be granted one unexcused absence from class without penalty. However, this is a collaborative, discussion-based course, so your presence is integral to your learning. Thus, the following will be deducted from your participation grade in the case of absences:
2-3 absences20%
4-5 absences40%
6 or more absences80%
If you wish me to acknowledge your absence as excused, you must either:
  1. Notify me at least 7 days in advance to make arrangements for your absence, or
  2. Ensure I have received documentation of your absence's circumstances post hoc from Health Services or Student Affairs.
Our discussions benefit from your contributions. If you do miss a class, you must first talk to a classmate about any material that you may have missed. After that, you may follow up with the instructor about any further questions or concerns.

4  Schedule of Topics

The following is an approximate schedule of topics to be discussed during the course. See the web page schedule for daily details.
1Intelligent Agents8Probability
2Search9Bayesian Networks
3Constraint Satisfaction10Decision Theory
4Adversarial Search11Learning
5Propositional Logic12Classification
6First-Order Logic13Reinforcement Learning

5  Assignments and Activities

Under a normal 16 credit load, I expect that you will spend at least 40 hours per week on your studies (class time, homework, and studying).1 Thus, you should plan to spend a minimum of 10 hours/week on work for this course (but hopefully more). With class time clocking in at 2[1/2] hours, you'll have 7[1/2] hours/week left for the following:

5.1  Reading

Our class meetings will be heavily discussion-based, and this will require a significant amount of preparation on your part. Most of this will consist of careful reading and reflection on the material through the use of a reading journal.

5.1.1  Preparation

You should check the class schedule for updates and read any material that has been assigned before coming to class. Reading the textbook entails the following:
You should quickly skim through the reading once to get an overview of the material to be covered, paying particular attention to subject headings and topic introductions. This first "reading" can (and should) be very quick. (Expected time: 5 to 10 minutes.)
Next, read the material closely. Try to understand what individual steps of algorithms or mathematical proofs are accomplishing. Not everything will make sense at this point, but hopefully many things will. (Expected time: 40 to 50 minutes.)
Final Notes
After carefully reading the material, mentally review and try making a few notes to yourself about what you think are the most important concepts being covered, as well as any questions you have. (Expected time: 5 to 10 minutes.)
Many of the readings are fairly short (about 136 pages, or roughly 30-40 pages per week), but can contain mathematics or algorithms that require a moderate amount of study. While I realize not everyone learns best by reading, you are asked to make your best effort and come to class with any questions you may have. Then we can proceed with discussion, examples, and exercises that enhance and clarify the material in class.

5.1.2  Reading Journal

To help focus your efforts and give us a basis for discussion, you will be provided short a list of questions to answer for each day's reading. Reflecting upon your responses to the questions will help to give you a deeper understanding of the most important concepts surrounding each topic. See the accompanying "Reading Journal" document on the course web page.
Your responses are due by 7 a.m. the day of class. No late responses will be accepted. You will submit your responses electronically to a private journal via our PioneerWeb course page, where I will be able to give you feedback on your writing.
While these low-stakes writing assignments are technically "informal," they must reflect a certain level of engagement and evidence of thinking seriously about the material.
Responses will be graded using the following ternary scale:
\checkmark+Exhibits exceptional clarity, insight and/or creativity.
\checkmarkExhibits evidence of processing and studying concepts.
\checkmark-Superficial response or insufficient evidence of engagement.
Since I expect most entries will receive a check, I will comment on your journal to report a plus or minus.

5.2  Participation

Because much of our work in this course involves collaboration and discussion, you will be evaluated on your participation.
Participating in class involves:
Students who regularly meet these criteria can expect to earn 90% (i.e., an A-) for their participation grade. I will reward students who regularly provide significant insights or guide discussion in productive ways with a higher participation score. Students who fail to participate regularly or who participate in counterproductive ways (e.g., by dominating the conversation or making inappropriate comments) can expect to earn a lower score.
One unexcused absence will have no effect on your participation score. (See the Attendance Policy, Section below.)

5.3  Programming Assignments

Over the course of the term, there will be approximately seven programming assignments using algorithms from our reading to address interesting problems. These will be due at various times throughout the semester. Instructions regarding collaboration will be given with each assignment. Unless the assignment specifies that group work is allowed, you are welcome to discuss material with others, but any work you do and submit should be your own. (One good rule of thumb is that you should not leave a discussion with written material regarding the assignment.)

Discussion of concepts and approaches with other classmates is encouraged. Debugging programs can be difficult and is often helped by explaining your code to someone else (which will also frequently help you to explain the bug to yourself). All such contributions by others (not in your group) must be properly attributed in your report. Furthermore, all the work submitted (code, experimental data, write-ups, etc.) must be your own. Code provided by the instructor should be attributed, but no other code or written work (from any source) may be shared with others or copied for your own use.
You are highly encouraged to use the PioneerWeb Discussion Board for questions related to the course. If a post is related to an assignment, it must adhere to the standards of collaboration for that particular assignment. If you are debugging, one good rule of thumb is to not post code. (I often find that when I am in the process of explaining some error by writing up a post or email, I typically find its source.)
Below is a tentative schedule for homework assignments; it is subject to change
1Search9 Sep18 Sep
2Adversarial Search21 Sep28 Sep
3Logic Programming9 Oct16 Oct
4Bayesian Networks4 Nov11 Nov
5Markov Models16 Nov23 Nov
6Supervised Learning23 Nov2 Dec
7Reinforcement Learning2 Dec11 Dec

5.4  Exams

As opportunities for you to demonstrate your agent design prowess and grasp of artificial intelligence principles there will be two hour exams and a cumulative final exam.
Hour Exam 1Friday, October 2
Hour Exam 2Monday, November 16
Final ExamTuesday, December 15 (9 AM)
Do not make airline reservations that will conflict with your final exam schedule.

6  Grading

My goal is for everyone taking this course to be able to demonstrate familiarity and fluency with the course concepts. I would be very happy if you all met the goals above and received "A"s. The following weighting will provide a basis for evalution
Programming Assignments40%
Reading Journal15%
Final Exam15%
with the caveat that you must pass the final exam to pass the course.

7  Academic Honesty

You, as students, are members of the academic community. Both the College and I expect the highest standards of academic honesty. (See the Grinnell College Student Handbook, e.g.,
Among other things, this means clearly distinguishing between work that is your own, and work that should be attributed to others. It is expected that the collaboration policies given in this syllabus and on particular assignments will be followed. Furthermore, any program results or output must be faithfully recorded, not forged. (A thoughtful explanation of unexpected behavior can often be a worthwhile submission and is much better than the alternative.)
In your homework assignments, you must give specific attribution for any assistance you receive. For example, one possible acknowledegment format is "[Person X] helped me to do [thing Y] by explaining [Z]."
As an instructor, I will meet my obligation to bring any work suspected to be in violation of the College's Academic Honesty Policy to the attention of the Committee on Academic Standing, after which there is no recourse with me.

8  Deadlines

Assignments are due at the beginning of class on the specified date. Work submitted more than ten minutes after the beginning of class will be considered late. Assignments due on days for which you have a prior excused absence must still be submitted by the deadline.
A late penalty of 33.33% will be deducted at each subsequent class meeting. Thus, you have at most two additional meetings to submit your work for a non-negative grade.
Exception: Deadlines for MathLAN computer-based assignments will automatically be extended by at least one class period if MathLAN is down for an unscheduled period of 3 or more hours during the week preceding the assignment due date.

9  Contacting Me

Please come by during my office hours to discuss the course content, get any extra assistance, or just talk about how the course is going. Note that if multiple students have similar questions or issues, we may work together as a group. If you cannot attend a scheduled office hour, you may also email me to schedule an appointment; please include 3-4 possible meeting times so that I can pick one that works for me, too.
I enjoy getting to know my students, but I prefer to reserve office hours for academic matters. If you would like to have a more informal conversation, I would be delighted to accept an invitation to lunch
Email is also a reliable way to contact me, but please allow 24 hours for a response (except on weekends, when I do not regularly read email). You may also call me in my office (x9812).

10  Accommodations

If you have any disability that requires accommodations, please meet with me right away so that we can work together to find accommodations that meet your learning needs. You will also need to provide documentation of your disability to the Dean for Student Academic Support and Advising, Joyce Stern, located on the 3rd floor of the Rosenfield Center (x3702).

With thanks to Janet Davis for the "Reading Suggestions" and other key policies.


1This is a minimum recommendation for achieving "satisfactory" (i.e., C-level) results. "Good" or "excellent" results may require a greater investment.