I am currently looking for PhD students that want to work with me in one of the following topics:
Evolutionary Game Theory: analysis of bio-inspired systems with a focus on the impact of different network topologies.
Machine Learning and Control: assessing the robustness and scalability of multi-agent machine learning algorithms in control and game theory.
Game AI: multi-agent reinforcement learning in computer games and procedural content generation.
Robustness, convergence guarantees and scalability of multi-agent reinforcement learning
Efficient model-free multi-agent reinforcement learning: challenges and applications
Applicants for a PhD position should hold a BSc and/or a Master degree in relevant areas, have a strong motivation for research in one of the above topics and proved academic performance or relevant project experience.
We offer an excellent research environment within the School of Computer Science and a strong supervisory team, with great opportunities to grow.
If you are interested, please send me an email with your CV and transcripts. Also, a short project proposal would be an indication of your motivation to conduct research in one of these areas.
CURRENT PHD STUDENTS
Mr Tuo Zhang (Prospective)
Mr Tuo Zhang will be working on co-evolutionary games in the context of learning the optimal policies. Start date: January 2023.
MSc Projects - Summer 2023
MSc Projects - Summer 2022
Mr Jishag Azhikodan Chenarath
Title: Winter – The Medical Bot. A virtual buddy to keep you in company.
The aim of this project was to develop an intelligent system or a bot that could act as a virtual assistant and a front-line defence against growing mental health issues, focusing on user-based information.
Mr Daniel Fitzpatrick
Title: Applying Machine Learning to Analyse Poker Gameplay.
In this pro ject, a set of dynamically-tting Logistic Regression and Linear Regression models are compared, to see if we can predict other players' approximate hand qualities and aggression throughout a game in real-time. The mo dels are trained utilising historical data from each player on the table.
Mr Kishan Odedra
Title: Investigating pedagogical approaches for spatial and logical reasoning tasks in virtual reality environments.
The report aims to construct and compare a virtual reality application which teaches a learning task to conventional pedagogical mediums. Unity and Oculus Interaction SDK are the tools used for the development of the prototype. The learning task is primarily facilitated through a visualisation of an algorithm and subsequent testing of knowledge with immediate feedback.
Mr Heil Park
Title: PCG Dungeon generation with Answer Set Programming in Unity.
Procedural Content Generation (PCG) is the using of algorithmic systems to create data. It has been applied in many different ways and implementations, each with differing representation and generation methods. This project focuses on applying Answer Set Programming (ASP) to a generating program created in the Unity game engine.
Mr Conor Tansey
Title: EscapeNow A new social network built to connect aspiring travellers.
The goal of this project was to build a full stack social network web application that facilitated users in finding other people to travel and explore the world with as well as sharing their own adventures and being inspired by others travel experiences.
Mr Henry Taylor
Title: A Comparative Analysis of Q-Learning, Minmax and Monte Carlo Tree Search for Connect-4 Artificial Intelligence.
This dissertation aims to answer the question 'What is the strongest algorithm enabling AI to play a game Connect-4?'. Three popular game algorithms (Q-Learning, Minmax, and Monte Carlo Tree Search) are compared to more advanced versions.
Mr Rajesh Kanna Vaidyanathan
Title: Intelligent Connected Autonomous Vehicles Traffic Flow Regulation study based on Unity ML-Agents.
This study describes the process of designing, developing, simulating, and evaluating a Multi-Agent Reinforcement Learning method for an intelligent connected autonomous vehicles traffic flow regulation scenario using the Unity ML-Agents platform.
Ms Shangqing Wei
Title: A Scalable Approach For Cooperative Multi-Agent Reinforcement Learning in Minecraft.
Scalability issue is one of the bottlenecks of multi-agent reinforcement learning (MARL). As the number of agents increases, especially to a large-scale, the state-action space will grow exponentially, which leads to the curse of dimensionality and high computation complexity. Aiming to address the scalability issue of MARL, this project propose a scalable approach for a cooperative task in Minecraft.