PEOPLE

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Title: Developing a simulation framework for coverage control using MARL in swarm robotics. 

Title: Agent-based modelling for collective decision-making in honeybees. 

Title: Physics-based NNs for accurate measurements in additive manufacturing. 

Title: A web application for process parameter optimisation in additive manufacturing. 

Title: A reinforcement learning approach for the characterisation of dross in additive manufacturing. 

Title: A game theoretic framework to solve the changing lane problem in autonomous driving. 

Title: Cascading failures in financial markets via mean-field games. 

Title: A multi-agent reinforcement learning approach to minimise cascading failures in financial markets. 

Title: A reinforcement learning framework with enthalpy-based reward functions for process parameter optimisation in additive manufacturing. 

Title: Using Machine Learning to optimise Laser Powder Bed Fusion process parameters for Dimensional Tolerance. 

In this dissertation, machine learning combined with image analysis is used to obtain predictions for optimum values for two parameters of the laser powder bed fusion process, beam compensation (BC) and contour distance (CD), when A20X powder is used. The main impact of these parameters is on the dimensional tolerance and accuracy of the smaller sections of the resulting product.

Title: Using Machine Learning to Optimise Process Parameters in Additive Manufacturing. 

This project aims to reduce the need for lengthy experimentation in additive manufacturing by producing a machine learning (ML) algorithm that utilises a limited amount of training data and is consequently able to predict the relative density of a print, as a measure of its quality.

Title: Process Parameter Optimisation in Additive Manufacturing: A Reinforcement Learning Approach. 

The aim of this project was to develop a reinforcement learning approach to optimise process parameters for metal additive manufacturing, in collaboration with Francesco Careri and Prof Moataz Attallah.

Title: Optimal Financial Investments to Mitigate Cascading Failures. 

This study enlightens the broader effects of using artificial intelligence in the financial context. We use a model-free Q-learning approach to mitigate against cascading failures. Our objective is to develop investment strategies in the form of portfolio holdings that would be optimised through a thorough analysis of past performance and potential rewards or penalties.

Title: Multi-Agent Reinforcement Learning for Mitigating Cascading Failures in Financial Networks through Adjusting Equity Goals. 

This project aims to provide a solution to mitigate systemic financial collapse by offering an automated approach for defining the equity goals within a network of interconnected institutions. We introduce a novel cooperative multi-agent reinforcement learning model designed to imitate the role of a central governing body, which sets the equity requirements of each company at different time periods.

Title: Network Model Analysis in Financial Contagion. 

Financial contagion is a crucial issue and its consequences can lead from the bankruptcy of a single institution to the onset of a financial crisis. This project studies the properties of the Watts-Strogatz network model and the Barabási-Albert network model. This project primarily focuses on the discussion and analysis of the effects of varying share-holding weights among multiple organisations in different network models.

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.

Title: Applying Machine Learning to Analyse Poker Gameplay. 

In this project, a set of dynamical 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 models are trained utilising historical data from each player on the table.

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.

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.

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.

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.

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.