CSC2542: Topics in Knowledge Representation and Reasoning:

Algorithms for Sequential Decision Making
Fall 2016  

General information Instructors: Sheila McIlraith and Rick Valenzano
Email:csc2542profs -- at --
Offices: Pratt 398 (6 King's College Road)
Office Hours: By appointment
Course Timing: Thursday, 2:00 - 4:00 PM
Course Location: OI4410 (OISE, 252 Bloor St. West)
Extra Tutorial Hour: Thursday, 4:00 - 5:00 PM, OI4410 (OISE, 252 Bloor St. West)
Course Announcements: Read me often (Last updated on December 8)

Piazza Link: Click here for Piazza

Course Material: Timetable, Slides, Readings:

Assignment: Assignment 1 (posted on October 3)

Computational Resources: Resources (posted on October 24)

Class Paper Presentations Individual paper assignments and questions (Last updated November 20)

Course Description

CSC2542 is a seminar-style topics course that explores recent advances in knowledge representation and automated reasoning. In the fall of 2016, the topic being covered is "Algorithms for Sequential Decision Making." Sequential decision making is the task of deciding what to do in the context of an extended interaction with the environment. It is a core competency of most intelligent agents. Central to sequential decision making is the notion of making a good decision now with respect to both immediate and longer term consequences. A driver deciding on a particular route to work, a robot deciding what to say to a person it is conversing with, a bank electing to embark on a major advertising campaign, or a computer program deciding on a move in a game of Go are all examples of sequential decision making.

This course will examine select principles and algorithmic techniques that are exploited for sequential decision making. The course will not be exhaustive but rather will focus on two themes: advances in techniques for search including suboptimal search algorithms, real-time search, and Monte Carlo search; and techniques for sequential decision making under uncertainty, including Markov Decision Processes (MDPs) and Reinforcement Learning (RL).

The course will draw predominantly on research readings. The format of the course will be a mix of class lectures, seminars, videos, assigned readings, and student paper presentations. A background assignment and a course project will be major components of a student's course mark.

Students taking the course should have the equivalent of an undergraduate introductory course in AI, such as CSC384, and reasonable competency as a programmer.

This should be a fun course! If you're thinking of taking the course and have questions, feel free to conact us.

For further information about the course or to report problems with the web page, contact the instructors, Sheila McIlraith (sheila-at-cs) and/or Rick Valenzano (rvalenzano-at-cs).