CS 289, Fall 2001
Knowledge Representation and Reasoning
Instructor Stuart Russell
727 Soda Hall,
russell@cs.berkeley.edu,
(510) 642 4964
Office hours Monday 10.00-12.00.
Lecture: MW 2.30-4.00
Location: 405 Soda
Units: 3.
Suggested prerequisites: CS188 or equivalent, or permission of
instructor.
Description
This class will look at formal representations of knowledge
and at reasoning methods that use them. The first half of the course
covers logical methods for inference and decision making,
while the second half covers probabilistic methods.
Topics will include
- Representation and reasoning in propositional logic, including efficient model-finding algorithms.
- Representation and reasoning in first-order logic, including logic programming and resolution.
- Reasoning about action, time, and knowledge.
- Planning and logical agents; applications to intelligent internet systems.
- Probabilistic representation and reasoning in Bayesian networks.
- Probabilistic temporal reasoning, including hidden Markov models, Kalman filters,
dynamic Bayesian networks; applications to object identification and robotics.
- Combining logic and probability.
- Decision theory, including multiattribute utility theory, and game theory.
- Probabilistic planning in Markov decision processes.
- Information value, control of reasoning, and bounded rationality.
In most cases we will be concerned with expressiveness, complexity, and completeness
as well as implementations and applications.
What will actually happen
The class will meet twice a week; discussion will focus on the
readings given in the accompanying reading
list. There will be three assignments (20% each) combining written
work with simple implementations, and a term project (40%)
consisting of a substantial project or analytical paper.
Books: Russell and Norvig, Artificial
Intelligence: A Modern Approach, Prentice Hall, 1995 (to be supplemented with preprints from the second edition).
Course reader, available soon, containing papers not available online.
Handouts
Slides