This paper studies the operation of multi-agent networks engaged in multi-task decision problems under the paradigm of simultaneous learning and adaptation. Two scenarios are considered:one in which a decision must be taken among multiple states of nature that are known but can vary over time and space, and another in which there exists a known “normal” state of nature and the task is to detect unpredictable and unknown deviations from it. In both cases the network learns from the past and adapts to changes in real time in a multi-task scenario with different clusters of agents addressing different decision problems. The system design takes care of challenging situations with clusters of complicated structure, and the performance assessment is conducted by computer simulations. A theoretical analysis is developed to obtain a statistical characterization of the agents' status at steady-state, under the simplifying assumption that clustering is made without errors. This provides approximate bounds for the steady-state decision performance of the agents. Insights are provided for deriving accurate performance prediction by exploiting the derived theoretical results.

中文翻译：

---

多任务网络的决策学习和适应


本文研究了同时学习和适应范式下处理多任务决策问题的多智能体网络的运行情况。考虑两种情况：一种是必须在已知但可能随时间和空间变化的多种自然状态中做出决定，另一种是存在已知的“正常”自然状态，任务是检测不可预测的情况以及与它的未知偏差。在这两种情况下，网络都会从过去学习并适应多任务场景中的实时变化，不同的代理集群解决不同的决策问题。系统设计考虑了结构复杂的集群的挑战性情况，并通过计算机模拟进行性能评估。在聚类无错误的简化假设下，进行了理论分析，以获得稳态时代理状态的统计特征。这为智能体的稳态决策性能提供了近似界限。通过利用导出的理论结果来提供准确的性能预测的见解。