About the project
The main goal is to improve the state-of-the-art mechanisms for the allocation of scare resources from different, and not always compatible, perspectives of efficiency, fairness and resilience. Multi-agent systems and machine learning techniques will be used to develop better and more sustainable mechanisms.
Allocation of scare resources is prevalent in our lives. From allocating drivers and vans for delivery of goods, to the allocation of paramedics and ambulances in disaster response. Take Emergency Services as an example. They play a critical role by dispatching vehicles and qualified professionals to emergency situations. While providing skilled and well-equipped staff in a short time is key in most scenarios, they operate with limited resources and hence it is crucial to ensure that right level of resources is used to deal with a situation, while maintaining the resilience of the system for upcoming emergencies.
The two highly desired and well-studied properties for allocations are ``efficiency’’ (making the best use of the limited sources available) and ``fairness’’. An allocation is ``Resilient’’ if, should a problem occur (e.g. an ambulance breaks down and becomes unavailable), it can be amended with minimal loss to efficiency and fairness.
Our main goal is to use multi-agent systems and machine learning techniques to design fair, efficient and resilient allocation of scarce, and possibly heavily constrained, resources in dynamic settings with uncertain preferences. The specifics and detailed objectives of the project can be adapted to your skills and interests.
The supervisory team are proud members of the Agents Interaction and Complexity research group. As a PhD student in AIC, you will benefit from engaging and collaborating with a vibrant and diverse group of academics and researchers from wide range of disciplines and expertise in or relevant to AI.
