Decision Making in Robots and Autonomous Agents
Semester 2 2013/2014
Course organiser: Iain Murray
For administrative queries, your first point of contact is the ITO. Also see this
: Subramanian Ramamoorthy
: Tuesdays and Fridays 11:10 - 12:00, G.8 Gaddum Lecture Theatre, 1 George Square
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,
with special emphasis on how they learn to interact with other agents and people. This course will cover four major themes:
Note regarding changes to 2013/14 content
- Decision theory and models
- Human choice behaviour
: Unlike in the previous offering, we will be more focussed on issues of learning, user modelling and adaptation, and less on algorithmic issues arising within game theory or decision theory.
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 an application domain requiring the design of autonomous decision making systems. This could be robotics (e.g., through the R:SS course) or a course on agent design in a software setting, e.g., recommender systems.
The student should be proficient in 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.
- R.D. Luce, H. Raiffa, Games and Decisions, Dover Publications, 1989.
- I. Gilboa, Theory of Decision Under Uncertainty, Cambridge University Press, 2009.
- H.P. Young, Strategic Learning and its Limits, Oxford University Press, 2004.
- P.W. Glimcher, Foundations of Neuroeconomic Analysis, Oxford University Press, 2011.
- R.H. Thaler, C.R. Sunstein, Nudge: Improving Decisions About Health, Wealth, and Happiness, Penguin Books, 2009.
Last update: 22 January 2014.