Decentralized Decision-Making Over Multi-Task Networks
This addresses coordination challenges in multi-agent systems, but appears incremental as it builds on existing distributed methods.
The paper tackles the problem of decentralized decision-making in multi-task networks where agents need to agree on a single objective, proposing a distributed algorithm that works for static and mobile networks, with simulations demonstrating performance.
In important applications involving multi-task networks with multiple objectives, agents in the network need to decide between these multiple objectives and reach an agreement about which single objective to follow for the network. In this work we propose a distributed decision-making algorithm. The agents are assumed to observe data that may be generated by different models. Through localized interactions, the agents reach agreement about which model to track and interact with each other in order to enhance the network performance. We investigate the approach for both static and mobile networks. The simulations illustrate the performance of the proposed strategies.