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Machine Learning and Cognitive Modelling Applied to Video Games

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  • Full or part time
    Dr K Chen
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Video games have been viewed as an ideal test bed for the study of AI. However, most of the academic work in this area focused on traditional board and card games where limited AI techniques have been tested. On the other hand, interactive vedio game development, particularly video games, has grown up to be an industry of a huge market between $35 billion and $50 billion. Interactive video games provide a forum for interaction between agent and human in cyberspace and are argued to be of educational value apart from entertainment. Recent studies revealed that most of exiting interactive games lack innovation (e.g., most of existing games have only predefined, static and predictable game agent responses) and fail to consider player satisfaction (e.g., frustration caused by failures in performing some actions), and the next generation interactive games demand improving the player experience in fantasy, innovation, curiosity, challenge and imitation of human intelligence. Thus, there is an unexplored opportunity for cognition-aware machine learning to make interactive games more interesting and realistic. Machine learning would provide a new way to improve behavioural dynamics for automatic generation and selection of behaviours, which offer opportunities to create more engaging and entertaining game-play experience. Furthermore, computational cognitive modelling techniques along with machine learning allow for modelling player/agent behaviours and creating vivid cognition-aware environments.

This project is going to investigate machine learning and cognitive modelling techniques for developing next generation video games. The main issues in this project include autonomous learnable agents for generic video game AI, novel learning algorithms for game content space exploration and exploitation, novel cognition-aware learning algorithms for real-time adaptation mechanisms, player-experience driven automatic game content generation and player behaviour modelling as well as new game-genre framework via deploying psychological and cognitive theories. As a part of this project, normally, a prototype with an appropriate genre will be developed with the proposed learning algorithms under the new game-genre framework to demonstrate the novelty of the proposed methodology.

In order to take this project, it is absolutely essential or a prerequisite to have video gaming programming experience and excellent programming skills in C++ and/or other common gaming programming languages (Please refrain from making any inquiry if one does not meet this condition). In addition, it also requires decent machine learning and, ideally, basic cognitive science knowledge. If you are interested in this project, please first visit my research student page: for the required materials and information prior to contacting me.

Funding Notes

This School has two PhD programmes: the Centre for Doctoral Training (CDT) 4-year programme and a conventional 3-year PhD programme.

School and University funding is available on a competitive basis.

For further details, please see our funding pages here:


The minimum requirements to get a place in our PhD programme are available from:

How good is research at University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

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