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Knowledge Representation and Reasoning in AI
Instructor: Lenhart Schubert
Classes: Tuesdays and Thursdays, 9:40am-10:55am, Hylan 102
Office Hours: Tuesdays and Thursdays, 5:00-6:30pm or by appointment
Office: WEGMN 3003
e-mail: my-last-name at cs dot rochester dot edu
TAs: Lane Lawley, Sid(dharth) Vashishta,
Office hours (Lane): WEGMN 3209, Wed. and Fri. ?? TBA
Goals of the course
The main goal in Knowledge Representation and Reasoning in AI is to learn
about techniques for representing factual knowledge (typically, the sort of
knowledge that we can easily express in ordinary language, though also some
kinds of knowledge calling for specialized representations, such as temporal
& taxonomic relations), and using such knowledge for making inferences
(deductive or uncertain), and for planning courses of action to achieve
goals. Students will thereby gain an appreciation of the fundamental
role that knowledge representation and reasoning (KR&R) play in intelligent
Practically speaking, students will reach the point where they can write
programs for basic KR&R tasks, and make sense of the current research
literature in this area; CSC 444 students will also learn about a subtopic
of their choice in greater depth, by completing a project based on reading
some recent papers or books and (perhaps) writing programs that implement
some ideas they have.
The standard prerequisites are CSC 173 (computation & formal systems)
and CSC 242 (a general course on AI). A solid math background and basic
knowledge of logic and programming may suffice as well (check with instructor).
Some prior exposure to functional programming (esp. Scheme or Lisp) can be
The grade for the course will be based on quizzes, several homework
assignments, including some Lisp programming assignments, a midterm and
final test, and, for 444 students, a research/essay project. For grading
details, see the TA's homepage for 244/444 (when it becomes available).
Here is a link to a pdf file providing (not entirely up-to-date)
guidelines for the graduate project
The text and supplementary texts are
- Brachman & Levesque, Knowledge Representation and Reasoning
(Morgan Kaufmann/\,Elsevier, 2004); this is required for grads, a reference for UGs
- Genesereth & Nilsson, Logical Foundations of Artificial Intelligence
(Morgan Kaufmann, 1987); This is a useful occasional reference for grads and UGs,
elaborating on some formal topics.
- Russell & Norvig, Artificial Intelligence (Prentice-Hall, 3rd ed. 2010).
This is supplementary for grads and recommended for UGs as a very readable,
general book on AI, often used in our department in CSC 242.
- Wilensky, Common LISPcraft (Norton); or Peter Seibel,
Practical COMMON LISP, Apress, 2005. These are a recommended
texts for learning Lisp. But see also the excellent text by Peter
Norvig, Paradigms of Artificial
Intelligence Programming; the
Lisp Primer; the collaboratively developed
Common Lisp Cookbook;
and "Cliki", the Common Lisp
Tutorial Wiki for some useful quick introductions.
Printed notes will be provided for a majority of the topics in the class.
However, the following Lisp intros are left for you to print, if you like:
Lisp Intro (LKS),
Lisp Tutorial (GK)
A lecture-by-lecture course schedule
is available at this link (and will also be handed out in the first class.)
Here is a link to some lecture notes.
Some supplementary notes and interesting links: