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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Decision Making in Robots and Autonomous Agents (INFR11090)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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.
Course description The course will cover the following major themes, although specific topics could vary from year to year.

I. Motivation

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
learning algorithms

e. Incomplete information, Game theoretic models and solution concepts

III. Computer science of decisions

a. Representations for planning - tradeoffs in modelling hierarchy,
uncertainty, etc.

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)
Pre-requisites It is RECOMMENDED that students have passed Robotics: Science and Systems (INFR11092) AND Intelligent Autonomous Robotics (Level 10) (INFR10005)
Prohibited Combinations 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? Yes
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 78 )
Assessment (Further Info) Written Exam 60 %, Coursework 40 %, Practical Exam 0 %
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.
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Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Formulate practical problems involving interaction (e.g., human-robot interaction) in the language of decision and game theory.
  2. Analyze and evaluate conceptual problems with decision models involving multiple agents.
  3. Analyze and implement selected learning algorithms that consider incomplete information and partial observability.
  4. Demonstrate understanding of key issues related to decision making in humans; identify when, why and how standard models fail to capture real behaviour.
Reading List
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.
Additional Information
Course URL
Graduate Attributes and Skills Not entered
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Course organiserDr Subramanian Ramamoorthy
Tel: (0131 6)50 9969
Course secretaryMrs Sam Stewart
Tel: (0131 6)51 3266
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