CS 498ea: Knowledge Representation and Reasoning

The course covers knowledge representation and reasoning algorithms in artificial intelligence. The focus is on logical and probabilistic knowledge and the use of structure in automated reasoning. The course will cover logical languages, probabilistic systems, dynamic systems, and decision making with these languages and systems. It will cover both exact and approximate techniques for reasoning, and will emphasize applications of these techniques in vision, robotics, virtual worlds, and others.

Prerequisites

Familiarity with the basic concepts of logic and probability theory.
Knowledge of basic computer science principles and skills.
Knowledge of basic artificial intelligence problems and principles (at the level of CS348).
Mathematical ability and the ability to understand and analyze fairly complicated algorithms and data structures. (CS373 is sufficient but not necessary.)
Some independence.

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 eyal@cs.uiuc.edu.

The final grade will be calculated using the following formula:
0.5*homeworks + 0.25*mid-term + 0.25*final
Up to 3% extra credit may be awarded for class participation.

Final project

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).

Communication

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 eyal@cs.uiuc.edu.

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.

Other reading materials will be distributed in class or linked from the syllabus.

Comments to Eyal Amir