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Anglia Ruskin University ARU Featured PhD Programmes
Anglia Ruskin University ARU Featured PhD Programmes

Inferring models for collaboration using Turing Learning


Department of Automatic Control and Systems Engineering

About the Project

This project explores Turing Learning [1], a method that infers the behaviour of a given system without needing pre-defined performance metrics. Turing Learning enables robots to model themselves, other robots, and the environment.

Turing Learning refers to a family of machine learning algorithms that generate models and discriminators in a competitive setting [1]. A training agent, T, provides genuine data samples, while a model agent, M, provides counterfeit data samples. A discriminator agent, D, labels the data samples as either genuine or counterfeit. Agents M and D are being optimised: D is rewarded for labelling data samples correctly, whereas M is rewarded for misleading D (to label its data samples as genuine). Turing Learning is inspired by the Turing test. It is a generalisation of generative adversarial networks (GAN) [2], and can be used to actively infer the behaviour of agents through interrogation [3].

The project is expected to improve a robot’s ability to model itself or others and to leverage such models in collaborative settings. Collaborative tasks involving swarms of robots or mixed human-robot teams could be considered. Experimental validation with real robots is encouraged.

The applicant would join a vibrant group with excellent facilities (http://naturalrobotics.group.shef.ac.uk).

Funding Notes

Prospective candidates should have a strong background in applied mathematics, computer science, engineering, physics or another related discipline, and be highly experienced in programming. We require applicants to have either an undergraduate honours degree (2:1) or MSc (Merit or Distinction) from a reputable institution.

Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive: View Website

References

[1] http://dx.doi.org/10.1007/s11721-016-0126-1
[2] https://en.wikipedia.org/wiki/Generative_adversarial_network
[3] https://nips.cc/Conferences/2017/Schedule?showEvent=9402

Full details of how to apply can be found at the following link: https://www.sheffield.ac.uk/acse/research-degrees/applyphd

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