Machine learning and reasoning
Machine learning is a fundamental branch of artificial intelligence (AI) which investigates methods for modelling and performing predictions. It is also the building block for nearly all the research activities of our group.
Integrating learning and reasoning constitutes one of the key open questions in AI and holds the potential of addressing many of the shortcomings of contemporary AI approaches, including the black-box nature and the brittleness of deep learning.
We regularly design and implement machine learning methods and investigate effective ways to train and evaluate these, with techniques including neural, evolutionary, reinforcement, statistical and logic-based machine learning and reasoning methods, and the integration of these approaches.
Some of our more specialist methods include Monte Carlo sampling methods and the optimal transport theory, as well as novel logic-based and relational machine learning algorithms such as meta-interpretive learning, and statistical relational learning algorithms such as MetaBayes.
The real-world problems we have applied these techniques to are in areas including healthcare, natural language processing, optimisation in transport networks, and metadata extraction from longitudinal social science questionnaires.
Get in touch
Contact us at firstname.lastname@example.org if you'd like to find out more about our research in machine learning and reasoning.