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Social Learning in Artificial Evolutionary Systems

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Artificial evolutionary systems, be they simulated or grounded in physical robots, provide a novel and cutting-edge way to investigate the emergence and evolution of adaptive and intelligent behaviours. Social learning is ubiquitous is nature, being seen across the animal kingdom. Social learning is adaptive for obvious reasons; learning from others is a great way to learn about new things and keep track of changes in your environment without having to engage in risky trial and error learning. Artificial evolution systems provide us with an opportunity to explore the evolution and emergence of social learning in depth, to understand how social learning can help autonomous agents perform better in real world situations, and to potentially evolve truly robust artificial social intelligences.

The PhD research will involve the creation of artificial life or evolutionary robotics models (simulated or in simple robots) to explore issues associated with social learning. For example, these issues include social learning in artificial evolutionary system (e.g. the evolution of horizontal information transfer, the evolution of social learning under environment variability, the utility of different social learning biases, modelling a natural social system, understanding the transition from simple social learning to cultural evolution and the evolution of maladaptive culture.

Keele University is renowned for its exciting approach to higher education and research, beautiful campus, strong community spirit and excellent student life. The University has the UK’s largest campus with 617 acres of landscaped parkland, fields, woodlands and lakes. Keele University runs its own day nursery for infants from 3 months to 5 years and is committed to equality and diversity. Information for prospective postgraduate researchers can be found here: http://www.keele.ac.uk/pgresearch/

This PhD project will connect with on-going international collaborative research inspired by an ambition to evolve social artificial intelligences by using and developing theoretical and practical knowledge of evolutionary systems. The research will be supervised by Dr James Borg in the Centre for Computer Science Research at Keele University, in collaboration with other national and international project partners.

Eligibility Criteria

Applications are welcomed from science, technology, engineering or mathematics graduates with (or anticipating) at least a 2.1 honours degree or equivalent. Applications from social science or humanities graduates will be considered if evidence of good (object-oriented) programming skills are provided. Applicants will require good general computing skills and the confidence to approach difficult problems with creativity and tenacity.
Applicants should have an enthusiasm for design and experimentation as well as a willingness to acquire new skills.

This opportunity is open to UK/EU and overseas students. The collaborative and presentation aspects of the research require good English language and communication skills. Overseas applicants would therefore require an English IELTS (or equivalent) of 6.0 overall with no less than 5.5 in any subtest.

Informal enquiries about the project are very welcome by email to the Project Lead, Dr James Borg ().
Please go to https://www.keele.ac.uk/study/postgraduateresearch/researchareas/computerscience/ to apply.

Please quote FNS GS 2018-14 on your application.

Funding Notes

Open to fully self-funded students only.
Please note that self-funded applicants must provide funding for both tuition fees and living expenses for the 3-year duration of the research. There is a future possibility of competitive scholarship awards for outstanding applicants (1st class honours), however, none are currently available.
For information regarding University tuition fees please see: View Website

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