Probabilistic Reasoning in AI (308-526B)

Winter 2002

General Information

Where: Leacock 14

When: Mondays and Wednesdays, 1:00-2:30pm.

What: One of the primary goals of AI is the design, control and analysis of agents or systems that behave appropriately in various circumstances. Such intelligent agents require the ability to decide how to act as circumstances vary. In turn, good decision making requires that the agent have knowledge or beliefs about its environment and its dynamics, about its own abilities to observe and change the environment, and about its own goals and preferences. In this course we will examine some of the techniques for modeling decision problems of various types and the computational methods used to solve them. We will focus mainly on probabilistic models of reasoning, and on sequential decision making.
The course is intended for advanced undergraduate students and for graduate students, and will provide an introduction to the on-going research in the field of reasoning under uncertainty, which has been very active during the last decade.
The course will cover both the theoretical basis of decision making under uncertainty, and the practical applications of these algorithms. We will cover the following topics:


Doina Precup
School of Computer Science

Office: McConnell 326

Office Hours:

  • Mondays and Wednesdays, 2:30-3:00
  • Tuesdays, 2:00-3:00
  • Meetings at other times by appointment only

    Phone: 398-6443


    IMPORTANT: E-mail is the quickest way to reach me and get your questions answered.

    Teaching assistant

    Will Renner

    Office Hours: Thursdays, 2:00-3:00, McConnell 231



    1. Textbooks:
    2. Lecture notes and other relevant materials are available on this web page.
    3. Other reference materials will be distributed in class as needed.

    Doina PRECUP
    Last modified: Tue Jan 22 21:27:41 EST 2002