Abstraction-Based Monte Carlo Tree Search. (Industry placement at PROWLER.io)
Monte Carlo Tree Search is a popular artificial intelligence technique amongst researchers due to the remarkable strength by which it can play many games. This technique was prominently used as the basis for AlphaGo, the AI by Google DeepMind that became the first of its kind to beat professional human players at the game Go. But despite lots of interest from academics into Monte Carlo Tree Search, the technique has seen little use in the games industry - due in part to how it is not fully understood, and due to how complex it is to implement into large games.
Matthew’s research is looking into how game abstractions can be used to help implement and optimise Monte Carlo Tree Search into existing commercial video games. Semi-automated methods for domain abstraction are being investigated, with the aim of making it fast and easy for game developers to be able to implement Monte Carlo Tree Search into their products, and to exploit the wealth of academic research into this technique.
Matthew is currently studying towards his PhD at the University of York, having previously graduated for the Department of Computer Science with a MEng in Computer Science with Artificial Intelligence. Before starting his PhD, Matthew spent a year at BAE Systems Advanced Technology Centre working on contracts with the European Space Agency, and has performed research into vertebrae models of Parkinson's disease with York Centre for Complex Systems Analysis.