Jay Young

Background

  • BSc Computer Science (Hons), First Class.
    My Dissertation was a 3D simulation framework for real-time construction and experimentation with biologically-inspired AI systems. Something like Netlogo but a lot more work! I was supervised by Professor Jonathan Rowe.
  • MSc Advanced Computer Science, with Distinction.
    - First Project
    Looking at how AI systems can store previously calculated plans in order to be accessed later, rather than acquiring them again through re-planning. This led on to thinking about how plans can be pruned based on the situational context of a particular problem that is being faced, in order to help navigate a library of learned plans. I was supervised by Dr. Nick Hawes.
    - Second Project
    An experimental framework for the construction of swarm-robotic control systems based on the biological idea of morphogenesis exhibited by embryonic stem cells. Essentially we were looking at how self-organising systems can be constructed based around the idea of limited, local signaling. Stem cells are interesting because they use such mechanisms to form all of the different parts of the human body, so we were trying to build something similar that could be used by our own artificial self-organising systems. We made a website with some cool videos of our results. I was supervised again by Professor Jonathan Rowe.
    - Summer Project
    Using Genetic Algorithms as a basis for learning goal priorities towards autonomous, self-directed AI systems. Essentially we looked at applying some of the ideas of self-motivation used on our robot Dora the Explorer to an AI for playing the Real-Time Strategy game Starcraft. The system grew much more complex than expected, and necessitated the addition of an Evolutionary Learning approach in order for it to determine how to configure itself to perform certain tasks (which, in this case, consisted of the need to defeat opponents in the game). We then required mechanisms to allow the system to autonomously navigate it's own learned space of possible configurations. I was again supervised by Dr. Nick Hawes.