Postgraduate Course: Decision Making in Robots and Autonomous Agents (INFR11090)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||This course is intended as a specialized course on models and techniques for decision making in autonomous robots that must function in rich interactive settings involving interactions with a dynamic environment, and other agents (e.g., people). In the first part of the course, students will learn about formal models of decision making, and computational methods for automating these decisions within robots. In the second part of the course, we will consider issues arising in practical deployments of such autonomous robots, including problems of achieving safety, explainability and trust. Students will be exposed to current thinking on models and algorithmic methods for achieving these
attributes in autonomous robots.
The content of this course has connections to other courses within our existing curriculum, such as Reinforcement Learning and Algorithmic Game Theory. A noteworthy difference is that RL and AGTA are primarily focussed on broad coverage of algorithmic methods, whereas this course will emphasize issues of modelling, with some focus on problems arising in practical robotics applications.
The course will cover the following major themes, although specific topics could vary from year to year.
a. Problems involving interaction: Strategically rich human-robot
interaction; Multi-robot interactions
b. How have decisions been modelled in different disciplines: probability
theory, machine learning, psychology and cognitive science
II. Mathematics of decisions
a. The utility maximization framework, Bayesian choice models
b. Causality, Causal learning
c. Bandit problems, Markov Decision Processes, and associated analysis
d. Dynamic programming principle, and associated approximation and
e. Incomplete information, Game theoretic models and solution concepts
III. Computer science of decisions
a. Representations for planning - tradeoffs in modelling hierarchy,
b. Safety and trust in autonomous systems
c. Explainability in AI
d. Bounded rationality and cognitive biases
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Intelligent Information Systems Technologies.
Entry Requirements (not applicable to Visiting Students)
|| It is RECOMMENDED that students have passed
Robotics: Science and Systems (INFR11092) OR
Introduction to Vision and Robotics (INFR09019)
||Other requirements|| This course is open to all Informatics students including those on joint degrees. However, students will benefit from prior exposure to robotics at the level of the Robotics: Science and Systems or Intelligent Autonomous Robotics.
For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
Prior exposure to mathematical models; Multivariate Calculus (Jacobian), Probability (expectation, conditional probability), Stochastic Processes (Markov chains), Principles of Optimization (linear programming, gradient descent methods).
Ability to program in a high level language, such as C/C++ or Python.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2019/20, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||You should expect to spend approximately 25 hours on the coursework for this course.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
On completion of this course, the student will be able to:
- Formulate practical problems involving interaction (e.g., human-robot interaction) in the language of decision and game theory.
- Analyze and evaluate conceptual problems with decision models involving multiple agents.
- Analyze and implement selected learning algorithms that consider incomplete information and partial observability.
- Demonstrate understanding of key issues related to decision making in humans; identify when, why and how standard models fail to capture real behaviour.
|There is no single textbook for this course.|
The instructor will provide lecture notes/slides, which will be complemented by readings from books and research articles.
Readings indicative of the course content include:
- B. Christian, T. Griffiths, Algorithms to Live By, William Collins Press, 2016.
- W.B. Powell, Approximate Dynamic Programming, Wiley, 2011.
|Course organiser||Dr Subramanian Ramamoorthy
Tel: (0131 6)50 9969
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701