This project explores Turing Learning , 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 . 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) , and can be used to actively infer the behaviour of agents through interrogation .
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.
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
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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