Glad to say that my thesis has passed and been accepted by Kings College London, and I am now a Doctor of Philosophy in Multi-agent Systems! For anyone interested in “Agent-based self-organisation for task allocation: reinforcement learning for emergent multi-agent systems”, here is the final work. This brings together research work and new algorithms for tackling task and resource allocation in multi-agent systems using reinforcement learning, as well as the emergence of roles…

This is the second paper (in review) in the Multi-agent learning in dynamic systems series. This work looks at how agents can learn to allocate restricted resources to handle incoming tasks, adapting to meet the goals of other agents as they gain knowledge on those goals.

This is the first paper (in review) in the Multi-agent learning in dynamic systems series. Developing new algorithms to optimise task allocation in multi-agent systems. Q-learning, historical reward convolution, and dynamically adaptable risk-based system exploration approaches are developed.

This is the introductory poster for the Multi-agent learning in dynamic systems series. In systems with a large number of agents there are fundamental pressures on the centralised coordination techniques used to provide inter-communication, task orchestration, and routing of messages. As the scale of interacting components expands, we reach resource constraint plateaus, where computation, storage, or communication pathways become saturated. At these points we must decompose each agents…