School of Computer Science and Statistics
This is a project for the M.Sc. in Statistics and Data Science This will look at a longitudinal case study of a Dublin feral/community-cat colony before and after a Trap-Neuter-Return (TNR) intervention, using CDPA data to investigate changes in population size and other factors that may influence the colony’s trajectory. The data for this project … Read more
This project would suit an undergraduate FYP, particularly if taking the 4th year Human Factors module. The aim of the project would be to replicate classic human performance experiments using computer games as a testbed, starting with simpler phenomena such as Fitts’ Law (representing the time taken to hit a screen target based on distance … Read more
Several qualitative research methodologies involve analysis of data such as interview transcripts and open questionnaire responses. For example, several forms of analysis involve coding of the transcripts and then identifying common patterns and themes within the codes. There are potential benefits as well as risks associated with the use of AI tools to support such … Read more
Learning and search are analyzed in hybrid automata that support continuous and differentiable change as in Lagrangian mechanics. Discrete approximations of that change are investigated in simple domains such as gridworlds where Legendre transforms and Lie algebras are considered.
Traffic networks can be significantly affected by unexpected events such as accidents, road closures or sudden congestion. Conventional route-planning approaches may struggle to adapt efficiently to these changes. This project investigates whether reinforcement learning can learn adaptive routing strategies that respond to unexpected disruptions. The learned approach will be evaluated in a simulated transport network … Read more
Vehicles approaching highway merges or lane-changing situations need to coordinate their actions to maintain safety and traffic efficiency. This project investigates whether multiple reinforcement learning agents can learn cooperative lane-changing strategies. Different training approaches will be compared with conventional rule-based strategies. The project will evaluate their impact on traffic safety, travel time and overall traffic … Read more
Choosing the best route through a city is challenging because traffic conditions change continuously. Reinforcement learning (RL) provides a way for an agent to learn effective route-selection strategies by interacting with a simulated transport network. This project will investigate whether an RL-based approach can learn to select routes that reduce travel time and congestion compared … Read more
Different travellers may have different preferences when choosing how and when to travel. While some may prioritise travel time, others may prefer cheaper, less crowded or more environmentally friendly options. This project investigates whether reinforcement learning can be used to learn personalised mobility recommendations based on individual preferences and feedback. The project will explore how … Read more
In cooperative multi-agent reinforcement learning, several agents have to learn how to work together. A challenge is that they can sometimes settle on a reasonably good way of cooperating, even though a much better joint solution exists. This project investigates whether the way learning experiences are selected and used during training could influence which solution … Read more