# CS 289, Fall 2004

Knowledge Representation and Reasoning

**Instructor** Stuart Russell

727 Soda Hall,
russell@cs.berkeley.edu,
(510) 642 4964

**Office hours** Tuesday 1-3pm in 727 Soda Hall.

**Lecture**: MW 2.30-4.00
**Location**: 310 Soda

**Units**: 3.
**Suggested prerequisites**: CS188 or equivalent, or permission of
instructor. The course requires both mathematical ability and good programming skills.

#### Description

This class will look at formal representations of knowledge
and at reasoning methods that use them. Roughly speaking, 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.
- Large-scale ontologies, including ontologies for biology.
- Probabilistic representation and reasoning in Bayesian networks.
- Probabilistic temporal reasoning, including hidden Markov models, Kalman filters,
dynamic Bayesian networks; applications to activity monitoring and robotics.
- Combining logic and probability.

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 (under construction)**. The readings for each week will be accompanied
by a set of **questions** designed to elicit short answers (roughly one
or two pages in all). These answers are due at the beginning of the week in
which the corresponding readings are to be discussed, and constitute
20% of the course grade. There will be two assignments (20% each) combining written
work with simple implementations, and a **final project** (40%),
which may be a substantial implementation effort or an analytical paper.
**Textbook**: the basic material for each topic is in Russell and Norvig, *Artificial
Intelligence: A Modern Approach*, **second edition**, Prentice Hall, 2003.

## Handouts

## Slides