07/02/2018

PhD in Computer Science and Informatics: Machine learning and artificial intelligence for situational understanding in contested (adversarial) environments

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  • ORGANISATION NAME
    Cardiff University
  • ORGANISATION COUNTRY
    United Kingdom
  • FUNDING TYPE
    Funding
  • DEADLINE DATE
    31/03/2018
  • RESEARCH FIELD
    Professions and applied sciences
  • CAREER STAGE
    First Stage Researcher (R1) (Up to the point of PhD)

Outline

The goal of this PhD is to provide a framework for enabling humans and machines to work together for situational understanding, by exploiting their respective strengths.

Situational understanding refers to the ability to relate relevant information, identify gaps in the available information, and form logical conclusions that enable decisions and actions. This is the case of many complex, dynamic areas, such as driving vehicles (including airplanes), health care, emergency response, military command and control operations, where the final human decision makers needs to be supported in their understanding of the environment for taking effective decisions.

To make decisions, humans typically rely on their experience with similar situations as well as their knowledge about the domain, which allows them to operate without full information, and to explain these decisions to others. On the other hand, processing large amounts of information is often beyond their skills.

This is where machines can help, as modern data analysis and machine learning algorithms are able to efficiently handle large quantities of information to support inductive reasoning, i.e., to infer general rules and patterns from specific observations.

However, many of these techniques are limited when it comes to taking into account the knowledge and experience accumulated by human experts, to identifying gaps in the available information, and to producing human- understandable explanations of their decisions, which are all crucial when humans and machines have to work together to make decisions that impact the real world. Furthermore, these techniques often require large amounts of training data, which may not be available in practice.

For this reason, we will investigate more logical-oriented approaches to machine learning, such as probabilistic logic programming and Bayesian networks.

This PhD will investigate techniques to bring together the strengths of humans and machines in a new framework that supports effective situational understanding, building upon and extending existing work in fields such as artificial intelligence, machine learning, and reasoning under uncertainty.

What is funded

Funding is provided to cover items such as research consumables, training and travel to conferences. Research students earn additional income by supporting the School’s teaching.

Full UK/EU tuition fees and a doctoral stipend matching UK Research Council National Minimum.

Duration

3 years

Eligibility