Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
This work addresses the challenge of reliable and efficient cooperative learning in safety-critical multi-agent systems, representing an incremental improvement over existing distributed GP methods.
The paper tackles the problem of improving prediction accuracy in distributed Gaussian process regression for multi-agent systems by introducing an elective learning algorithm that allows agents to selectively trust neighbors based on prior knowledge, resulting in enhanced individual prediction accuracy, especially when prior knowledge is incorrect, and eliminating the need for intensive variance calculations.
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.