Machine learning for behavioural modelling

This project is a an ICASE studentship supported by Dyson Technology Ltd supervised by Dr Michael Osborne.

We are studying the extraction of behavioural patterns from corpora of past interaction histories with unknown agents via machine learning methods in general and Bayesian reasoning in particular. Based on such learned patterns we will then explore new means of optimising future interactions with these agents.

A second point of interest, is research regarding mechanisms for correcting derived models based on specific evidence which suggests that the behaviours in the set do not approximate the real agent types well enough.
To ensure the robustness of the developed algorithms, we will conduct eventual experiments on data sets provided by Dyson, build from real-world customer data. As a consequence, our models will need to be able to adapt to uncontrollable and changing environments such as for instance domestic settings.

By pursuing this research, we hope to contribute to the development of machines capable of truly autonomous behaviour, able to assess their environment, make decisions, and learn from the results which is a major challenge in practical robotics.
This project falls within the EPSRC information and communication technologies research area.