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.
In the latter case, agents need to decide between the multiple objectives and reach agreement on following a single objective for the entire network. In the earlier works @cite_7 @cite_4 , a scenario was considered where agents were assumed to sense data arising from two models, and a diffusion strategy was developed to enable all agents to agree on estimating a single model. The algorithm developed in @cite_7 relied on binary labeling and is applicable only to situations involving two models. In this work, we propose an approach for more than two models.
@cite_4 @cite_7
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In this paper, we study distributed decision-making over mobile adaptive networks where nodes in the network collect data generated by two different models. The nodes need to decide which model to estimate and track. However, they do not know beforehand which model they observe. Therefore, an effective clustering technique is needed. We apply a clustering technique that reduces the clustering error. Furthermore, introduce an additional term to the motion model to ensure that the nodes move coherently without fragmentation in the network during the decision-making process. Once the network reaches agreement on the desired model, the cooperation among nodes enhances the performance of the estimation task by relaying data throughout the network. In distributed processing, agents generally collect data generated by the same underlying unknown model (represented by a vector of parameters) and then solve an estimation or inference task cooperatively. In this paper, we consider the situation in which the data observed by the agents may have risen from two different models. Agents do not know beforehand which model accounts for their data and the data of their neighbors. The objective for the network is for all agents to reach agreement on which model to track and to estimate this model cooperatively. In these situations, where agents are subject to data from unknown different sources, conventional distributed estimation strategies would lead to biased estimates relative to any of the underlying models. We first show how to modify existing strategies to guarantee unbiasedness. We then develop a classification scheme for the agents to identify the models that generated the data, and propose a procedure by which the entire network can be made to converge towards the same model through a collaborative decision-making process. The resulting algorithm is applied to model fish foraging behavior in the presence of two food sources.