This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs). The key contribution of this work is the development of an elective learning algorithm, namely prior-aware elective distributed GP (Pri-GP), which empowers agents with the capability to selectively request predictions from neighboring agents based on their trustworthiness. The proposed Pri-GP effectively improves individual prediction accuracy, especially in cases where the prior knowledge of an agent is incorrect. Moreover, it eliminates the need for computationally intensive variance calculations for determining aggregation weights in distributed GP. Furthermore, we establish a prediction error bound within the Pri-GP framework, ensuring the reliability of predictions, which is regarded as a crucial property in safety-critical MAS applications.

创新地使用高斯过程回归(GP)在多智能体系统(MASs)中增强分布式合作学习的方法被介绍。该方法的关键贡献是开发了一种选择性学习算法，即先验感知式选择性分布式GP(Pri-GP)，它赋予智能体有能力根据其可信度有选择地请求邻居智能体的预测结果。所提出的Pri-GP在个体预测准确性方面有效提高，特别是在智能体的先验知识不正确的情况下。此外，它消除了在分布式GP中计算密集的方差计算来确定聚合权重的需求。此外，在Pri-GP框架内确立了预测误差界限，确保了预测的可靠性，这在安全关键型MAS应用中被认为是至关重要的属性。

信任何人？分布式高斯过程回归的选修学习