Agents, Knowledge and Data
Information on the specialist area Agents, Knowledge and Data.
Modern computer applications involve large numbers of autonomous human and machine agents interacting with each other over digital platforms such as the Web. Unlike traditional models of distributed computation, such systems involve diverse, heterogeneous data, and computational processes that represent the (often conflicting) objectives of human users or organisations with varying knowledge and skills. The “Agents, Knowledge, and Data” specialist area spans a range of topics from foundations of Artificial Intelligence (such as computational logic, knowledge representation and reasoning, decision and game theory) to practical real-world “collective intelligence” technologies (linked and open data, human-computer collaboration, big data applications), exposing students to a broad range of computational methods for dealing with next-generation large-scale intelligent systems. Students specialising in this area will be trained in:
- formal methods for modelling agents and their knowledge, and for extracting knowledge from data provided to them in informational environments;
- advanced computational methods for automating some of the reasoning and inference processes that drive intelligent behaviour in semantically rich environments; and in
- the use of up-to-date tools and technologies for building real-world systems that utilise such intelligent reasoning and decision making.
These skills are particularly relevant for jobs in pure and applied research on Artificial Intelligence, Semantic Web, Linked and Open Data, but also in a broad range of application domains that require the management of complex data and knowledge in human organisations and collectives.
Students registered in this Specialist Area are recommended to select at least fifty credit points from these courses, including all the core courses. Please note courses are subject to availability.
|Semester 1||Semester 2|
Automated Reasoning (level 9)
Database Systems (20 credits, level 10)
Introductory Applied Machine Learning (20 credits, level 10)
Text Technologies for Data Science (20 credits)
Advanced Topics in Foundations of Databases (20 credits)
Probabilistic Modelling and Reasoning (20 credits)