Decision Making in Robots and Autonomous Agents
Semester 2 2012/2013
Course organiser: Iain Murray
For administrative queries, your first point of contact is the ITO. Also see this
Lecturer: Subramanian Ramamoorthy
Lecture times: Tuesdays and Fridays 11:10 - 12:00,GF Adam House
Lecture topics and handouts
The course mark will be computed using the following weighting:
- Two Homework Assignments - 20% each
- Final Exam - 60%
This course is intended as a specialized course on models and techniques for decision making in autonomous
agents, such as intelligent robots, that must function in rich interactive settings involving
environments with other agents and people.
This course will cover decision theoretic algorithms, interactive decision making including game theoretic
techniques, learning in games and social settings, as well as selected topics involving decentralized
systems. We will also look at aspects of human decision making, both to ask what people actually do
and to consider what agents must do in light of this.
Background and Pre-requisites
This is a 'second course' in the sense that the student taking this course should have had some prior exposure to robotics (such as R:SS) or
autonomous agents requiring decision making.
The student should have some feel for the formulation and use of mathematical models and possess sufficient mathematical maturity in order to be able to
follow some readings from the research literature. Specific topical pre-requisites include Calculus & Probability at the level of
On the practical side, one of the assignments will require programming, in an environment such as Matlab. Students are expected to enter this course with
sufficient programming skill, or the capacity to learn what is required on the fly. However, this is not a 'programming course' - most of our classroom
discussion will focus on algorithmic and conceptual ideas.
- I. Gilboa, Theory of Decision Under Uncertainty, Cambridge University Press, 2009.
- H.P. Young, Strategic Learning and its Limits, Oxford University Press, 2004.
- N. Nisan, T. Roughgarden, E. Tardos, V.V. Vazirani, Algorithmic Game Theory, Cambridge University Press, 2007.
- P.W. Glimcher, Foundations of Neuroeconomic Analysis, Oxford University Press, 2011.
Last update: 15 January 2013.