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.

翻译：本文提出了一种创新方法，用于增强多智能体系统中基于高斯过程回归的分布式协作学习。本研究的关键贡献在于开发了一种选择性学习算法，即先验感知选择性分布式高斯过程（Pri-GP），该算法使智能体具备基于邻居可信度选择性请求预测的能力。所提出的Pri-GP有效提升了单个智能体的预测准确性，特别在智能体先验知识存在错误的情况下表现显著。此外，该算法消除了分布式高斯过程中确定聚合权重所需的计算密集型方差计算。我们在Pri-GP框架内建立了预测误差界，确保预测的可靠性，这被视为安全关键型多智能体系统应用中的重要属性。