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PhD Studentship: Multi-agent AI for Coordinating a Hardware Gig Economy

The University of Edinburgh - School of Engineering

Qualification Type: PhD
Location: Edinburgh
Funding for: UK Students, EU Students
Funding amount: Tuition fees + stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate)
Hours: Full Time
Placed On: 14th May 2019
Expires: 14th August 2019
 

Gig economies have turned many traditional business models on their heads, and are poised to do the same yet again, but for one of the most traditional economies: undergraduate education. In particular, universities are including ever more activity in their courses so as to enhance the attributes of their graduates. Yet many of the most traditional campuses face strict estates constraints that are opening the way for online, remote laboratory alternatives to flourish. Using a browser to access camera feeds, live data, and control remote hardware that could be across town, or half a world away, is already a proven concept in the distance learning sector, where the lead supervisor’s work at the Open University attracted awards from Times Higher Education, The Guardian, The Global Online Labs Consortium and National Instruments. Educational literatures on non-traditional laboratories (online real, virtual, and simulated labs) are unanimous in pointing out that a mix of traditional and non-traditional laboratories offers better overall education than traditional laboratories alone. Now at the University of Edinburgh, the challenge is to build organically-growable remote laboratory installations that can occupy small portions of otherwise unusable estate, and federate together across multiple campuses in the University of Edinburgh. Subsequently, the federation will be extended to similar forthcoming installations by collaborators at the Universities of Manchester and Cardiff. Unlike human-driven gig economies such as providing transport or services, the remote laboratory experiments are not attended to by humans. Hence, there is a need for the experiments to be self-aware, self-sufficient, and able to earn their keep by servicing students from both their local university, as well as other universities. This requires a complex multi-agent system with accounting for usage (micro-payments), trust (reliability, data provenance), and efficacy (evaluation). The research work will encompass developing /contributing to a new open-source multi-agent infrastructure code-base. Developing and refining algorithms for multi-agent interactions in both simulation and in real networks of remote experiments, generating research outputs in theoretical and applied aspects ranging from reliability in distributed systems to dynamic pricing in time-sensitive markets, with potential applications in the our forthcoming networks of remote laboratories as well as more diverse areas such as networks of renewable energy generators, and autonomous transport systems amongst others. In this way the work will inspire new approaches to multi-agent AI systems as well as provide a test-bed for exploring them.

   
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