Devices and methods for controlling hardware agents in control scenarios with multiple hardware agents.

By combining neural networks and game theory layers, the problem of determining the common action sequence in multi-agent control was solved, and efficient and robust control was achieved in autonomous driving and multi-robot systems.

CN114077242BActive Publication Date: 2026-05-26ROBERT BOSCH GMBH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ROBERT BOSCH GMBH
Filing Date
2021-08-13
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

In the case of control by multiple hardware agents, existing technologies struggle to efficiently determine the common action sequence of each agent, take into account the goals and behaviors of other agents, and achieve robust control that maximizes benefits.

Method used

By using neural networks to determine the potential function and game theory layers, the common trajectory of multiple hardware agents is predicted. The first neural network determines the potential function parameters, the second neural network determines the control scenario, and the common action sequence is determined by searching for local optima. Control is then performed by combining sensor data and prior knowledge.

Benefits of technology

It achieves efficient and robust control of multiple hardware agents, can predict and optimize their common action sequence in real time, adapts to complex control situations, and is suitable for autonomous driving and multi-robot systems.

✦ Generated by Eureka AI based on patent content.

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Abstract

An apparatus and method for controlling hardware agents in a control situation having multiple hardware agents. According to various embodiments, a method is described, comprising: determining a potential function via a first neural network; determining a control scenario for the control situation from a plurality of possible control scenarios via a second neural network; determining a common action sequence of the plurality of hardware agents by searching for the optimal value of the potential function determined with respect to possible common action sequences of the determined control scenario; and controlling at least one of the plurality of hardware agents according to the determined common action sequence.
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