Multi-agent information loss rate optimization selection method

By combining expert experience base with trial and error, the selection of information loss rate in multi-agent systems is optimized, solving the problem of reliance on human experience, realizing the automation and dynamic adjustment of information loss rate, and improving the system's adaptability and training efficiency.

CN122287683APending Publication Date: 2026-06-26NORTHWESTERN POLYTECHNICAL UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NORTHWESTERN POLYTECHNICAL UNIV
Filing Date
2026-03-18
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

In existing technologies, the selection of information loss rate in multi-agent systems relies on human experience, lacks systematicity and adaptability, and leads to blindness in the training process, making it impossible to obtain optimal performance in different task scenarios and dynamic environments.

Method used

This paper adopts a method that combines an expert experience base with a trial-and-error approach. By constructing an expert experience base, based on historical environmental metadata and performance evaluation records, a feasible range of information loss rate is derived, and a candidate set is generated within the feasible range. The performance index is evaluated using a trial-and-error approach, and the information loss rate is optimized by combining a comprehensive objective function. Finally, a compensation mechanism is introduced for dynamic adjustment.

Benefits of technology

It achieves automated, intelligent selection and dynamic adjustment of information loss rate, improves the adaptability and robustness of multi-agent systems in different tasks and environments, and enhances training efficiency and operational performance.

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Abstract

This application discloses a method for optimizing the selection of information loss rate in a multi-agent system, comprising the following steps: S1, constructing an expert experience base to form expert rules for the feasible space of information loss rate; S2, deriving the feasible range of information loss rate in the current task environment based on real-time environmental metadata and expert rules, and generating a set of candidate information loss rates; S3, obtaining performance evaluation indicators for candidate information loss rates in the candidate information loss rate set based on a trial-and-error method; S4, obtaining the optimal information loss rate based on the performance evaluation indicators through a predefined comprehensive objective function; S5, dynamically adjusting the information loss rate according to the real-time environmental feedback of the multi-agent collaborative navigation task to adapt to different task and environmental requirements. This application provides a method for optimizing the selection of information loss rate in a multi-agent system that solves the problem that the selection of information loss rate in the prior art relies on human experience and cannot achieve adaptive optimization.
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