MSc Machine Learning

Knowledge Representation and Reasoning (KRR) has been fundamental to the study of Artificial Intelligence, Autonomous Systems and Information Systems since the inception of the field. It is founded on concepts of logical reasoning drawn from mathematics and philosophy. This unit draws on a foundation in logic to show how these KRR techniques can be applied when information is uncertain, where contradictory knowledge and probabilistic reasoning are required to be considered, or changing, where it must be used to plan a series of actions over-time, and how it can be used to manage interactions between multiple agents.

This unit aims to introduce students to state-of-the-art techniques for representing and reasoning about knowledge, as well as important computational paradigms for using such knowledge to achieve tasks.

1. Describe and discuss the applicability of emerging Knowledge Representation and Reasoning topics.

2. Represent uncertain and contradictory information for computational processing.

3. Represent problems as AI planning problems.

4. Apply reasoning algorithms for uncertain knowledge.

5. Apply the agent paradigm to the design and implementation of computational systems.

6. Apply and evaluate planning algorithms.

• Uncertain and Probabilistic Reasoning
• Non-monotonic Reasoning
• Multi-Agent Communication and Coordination
• AI Planning
• Reasoning about Agent Programs

Weekly asynchronous resources in the form of video lectures and assigned reading.

Weekly formative coursework in the form of quizzes and lab exercises.

Weekly optional drop-in labs or tutorials for assistance with coursework.

Weekly synchronous lectures to summarise the material and provide an opportunity for Q&A.

Cohort-level feedback after marking.

Individualised feedback via marking rubrics.

Individualised feedback on request in Labs.

Autograded weekly quizzes.

Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson, 2021.

Gerhard Weiss, Multiagent Systems, MIT Press, 2016.

Summative Coursework 16 hours

Formative Coursework 20 hours

Formative Quizzes 6 hours

Videos 10 hours

Directed reading 10 hours

Independent study, consolidation and revision 66 hours