In this research endeavour, we aim to develop flexible and robust methods for managing decentralised data fusion. We will be developing an agent-based control system for data fusion that:
We will be using market-based approaches to view management of data fusion activities from an economic point of view and investigate market design for structuring marketplace to achieve various properties such as Pareto optimality, fairness and stability. In order to maximise their individual utility in such markets, strategies will be designed for agents while keeping in mind the overall protocol of the marketplace. These strategies can be augmented through adaptive behaviour (for example though some form of Q-Learning) that aims to utilise knowledge gained from past interactions.
Through this research project, we aim to develop novel market-based control algorithms (together with a simple demonstrator) that evaluate the effectiveness of decentralised control using market-based techniques. This research will also provide a theoretical analysis of the marketplace design to determine its effectiveness, efficiency and predictability and a systematic evaluation of the system's operational performance.