CS 498, Section EA
Knowledge Representation and Reasoning in Artificial Intelligence
University of Illinois, Urbana-Champaign
- Aug 18, 2004: The first class will be on Tuesday August 31 instead of August 26. There will be no class on August 26, and the instructor will arrange for a different date to fill in this class.
- Aug 31, 2004: The midterm will be on Monday, October 25, 6pm-8pm.
- Sep 9, 2004: Homework #1 is out. Due Sep 23 in class (11am).
- Oct 11, 2004: Homework #2 is out. Due Oct 26 in class (12pm).
- Oct 13, 2004: The midterm is moved to November 1, 6pm-8pm.
- Oct 25, 2004: The location of the midterm is Siebel Center 2124.
- Oct 29, 2004: Here is a sample midterm.
- Nov 9, 2004:Homework #3 is out. Due Nov 30 in class, 2004 (12pm).
- Dec 2, 2004: is out. Due Dec 16, 2004 (12pm).
- Dec 13, 2004: The will be Thursday, December 16, 1:30pm-4:30pm in SC 1109 (same as the usual classroom).
- Dec 13, 2004: Here is a .
Lecture: 3 or 4 hours credit, TuTh 11:00-12:15PM, 1109 Siebel Center
Professor: Eyal Amir
- Office: Siebel 3314
- Phone: (217) 333-8756
- email: email@example.com
- Office hours: Tue 3pm-4pm, Thu 2pm-3pm
Useful Information and Handouts
- Syllabus & Important Dates
- Frequently Asked Questions
The course covers knowledge representation and reasoning algorithms in
artificial intelligence. On the knowledge representation side it mixes
logical and probabilistic knowledge, and discusses representations
that involve time, space, and beliefs about self and other agents'
knowledge. On the inference side it discusses inference and decision
making with logical languages, probabilistic systems, and dynamic
systems. It covers both exact and approximate techniques for
reasoning, and emphasizes applications of these techniques in vision,
robotics, virtual worlds, and others.
- Familiarity with the basic concepts of logic and probability
theory is recommended.
- Knowledge of basic computer science principles and skills is required.
- Knowledge of basic artificial intelligence problems and principles (at the level of CS440) is recommended.
- Mathematical ability and the ability to understand and analyze fairly
complicated algorithms and data structures. (CS473 is sufficient but not necessary.)
- Some independence is recommended.
|Machinery: Tasks and Coursework
The course will consist of lectures by the teacher, homeworks,
a mid-term, and a final exam. In addition, students may choose to
conduct a final project for additional credit.
Due dates and recommended projects and papers for presentation
are indicated in the syllabus.
Assignments (including homeworks and projects) must be handed in
during the class on the date indicated in the syllabus. Recognizing
that students may face unusual circumstances and require some
flexibility in the course of the quarter, each student will have a
total of seven free late (calendar) days to use as s/he sees
fit. Once these late days are exhausted, any paper or proposal turned
in late will be penalized at the rate of 20% per late day (or fraction
thereof). Under no circumstances will an assignment be accepted more
than a week after its due date. Late days are from noon to noon.
Late assignments should be turned in at the turn-in box outside my
office, or under my office door. You must write the time
and date of submission on the assignment. Alternatively, you can fax
it to the course secretary (see the fax number above) or email it to
The final grade for 3 credit points will be calculated using the following formula:
0.5*homeworks + 0.2*mid-term + 0.3*final
The final grade for 4 credit points will be calculated using the following formula:
0.35*homeworks + 0.15*mid-term + 0.25*final+0.25*project
Up to 3% extra credit may be awarded for class participation.
Students who choose to do a project will be expected to submit an
initial project proposal and a more detailed proposal (after the first
proposal was approved), according to the dates in the
syllabus. Projects may have an implementation emphasis, a theoretical
emphasis, or a combination (preferred, but not mandatory). I urge
students to propose their own projects, but if they cannot find ones
by themselves, then they may select projects from the project list
provided in the
projects section of the syllabus.
In either case, the students should expect to meet with the instructor
to present their initial proposal and their detailed
proposal. Projects may be done by single students or in pairs. In the
case of a pair of students, the project must be appropriately larger
or more ambitious, and the contribution of each of the students
clearly identifiable. In the conclusion of the class the students
will present their projects in a poster session, and will also submit
a technical paper describing the project (see syllabus for dates).
I strongly encourage students to come to office hours instead
of emailing questions to me. Also, as explained above, late
assignments should be turned in at the turn-in box outside my
office. You can also email these assignments to firstname.lastname@example.org.
Please do not e-mail me with grading questions. If you want me to explain
why I took points off, you can talk to me after class
or during office hours. If you want a regrade, please write an
explanation and hand the assignment and the explanation to me
during office hours or after class.
Occasionally I may need to broadcast a message to entire class. I will do
so over the newsgroup for the class.
|Textbooks and Papers Information|
The main book for the class is
Stuart Russell and Peter Norvig,
Artificial Intelligence, a Modern Approach, Prentice Hall, 2nd ed., 2003.
Optional readings include
Ronald Brachman and Hector Levesque,
Knowledge Representation and Reasoning, Morgan Kaufmann, 2004.
Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, 1988.
Knowledge in Action, MIT Press, 2001.
Ronald Fagin, Joseph Y. Halpern, Yoram Moses and Moshe Y. Vardi,
Reasoning about Knowledge, MIT Press, 1995.
Other reading materials will be distributed in class or linked from