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Autonomous learning with Artificial Intelligence - (studied entirely by Distance Learning)


School of Engineering

About the Project

Autonomous learning has become one of the latest buzzwords in today’s artificial intelligence and robotics. All the same, to realise the behaviour of even the simplest creature like a unicellular organism in an artificial agent remains extremely challenging with current artificial intelligence techniques. This is because nature has equipped biological agents with specialised abilities to organise and survive in highly unstructured and ambiguous environments while pursuing their self-identified goals; but the mechanisms that guide this sophisticated natural intelligence are too complicated and are nontrivial to model. The popular machine learning and deep learning techniques, especially supervised algorithms, are not suitable for autonomous learning due to their overdependence on large amount of labelled data that are not always available or are expensive to acquire.

In addition, popular methods in Artificial Intelligence and reinforcement learning techniques that are used in robotics applications are not explainable and are not transparent in their learning, resulting in the AI being unable to adapt to new situations that it should encounter. This research will focus in particular on this aspect of autonomy, with some significant progress in this area already.

This project will look at the development of autonomous learning techniques based on biological learning processes, and will build on a track record of success in this area and will work with a team of other researchers in the area of autonomous and automated AI (refer to cited references for more information).

Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in Engineering, Physics, Computing Science with an essential background in computer coding, algorithms, mathematics, data analysis.

APPLICATION PROCEDURE:

• Apply for Degree of Doctor of Philosophy in Engineering
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV

Informal inquiries can be made to Dr A Starkey (), with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School (.

It is possible to undertake this project at a distance. Interested parties should contact Dr Starkey to discuss this.

Funding Notes

This project is advertised in relation to the research areas of the discipline of Engineering. The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website. THERE IS NO FUNDING ATTACHED TO THIS PROJECT.

References

• Ezenkwu, CP & Starkey, A 2019, Machine Autonomy: Definition, Approaches, Challenges and Research Gaps. in K Arai, R Bhatia & S Kapoor (eds), Intelligent Computing: CompCom 2019, Proceedings. Advances in Intelligent Systems and Computing, Springer , Cham, pp. 335-358, Computing Conference 2019, London, United Kingdom, 16/07/19.[ONLINE] DOI: HTTPS://DOI.ORG/10.1007/978-3-030-22871-2_24
• Ezenkwu, CP & Starkey, A 2019, 'Unsupervised Temporospatial Neural Architecture for Sensorimotor Map Learning', IEEE Transactions on Cognitive and Developmental Systems.[ONLINE] DOI: HTTPS://DOI.ORG/10.1109/TCDS.2019.2934643

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