Multi-Agent Machine Control Using Object-Neighborhood Graph Neural Networks
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Solution Overview
Problem
Existing methods for coordinating control agents in complex machine systems, such as robots and logistics systems, face challenges in optimizing global performance due to increased state and action space dimensions in central control and potential lack of coordination in decentralized systems.
Innovation Solution
A method utilizing a graphical neural network to link control agents based on object neighborhoods, optimizing communication and coordination by training the neural network to enhance global machine performance, allowing for efficient manipulation and control of multiple objects without the need for central control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a central controller manages all control agents, then coordination between agents is improved, but the dimensions of state spaces and action spaces increase significantly
Solution Approach 1:
The system segments the control architecture by assigning individual control agents to specific objects rather than using a single central controller. Each control agent operates independently with its own state space and action space, preventing the exponential growth of dimensions that would occur in a centralized system while maintaining coordination through inter-agent communication.
Solution Approach 2:
The patent introduces an intermediary communication mechanism where control agents exchange information about their observations and actions. This mediator layer enables coordination between decentralized agents without requiring them to all communicate with a central controller, thus maintaining coordination benefits while avoiding the dimensional explosion of state spaces.
2Ease of operation
If control agents operate independently in a decentralized manner, then control agent simplicity is improved, but coordination between agents deteriorates leading to performance losses
Solution Approach 1:
The system implements feedback mechanisms where control agents observe the states of other objects and adjust their actions accordingly. Each agent receives feedback about the global system state through observations of neighboring objects, enabling decentralized agents to make coordinated decisions without direct communication overhead, thus maintaining both independence and overall performance.
Solution Approach 2:
The patent extends the control paradigm from individual object-level decisions to a higher dimension by considering neighborhood relationships and spatial configurations. Control agents operate in an expanded state space that includes not only their own object's state but also the states of neighboring objects, enabling coordinated behavior through local observations without requiring centralized control.
3Reliability
If all control agents communicate with each other, then coordination is improved, but communication overhead and training complexity increase
Solution Approach 1:
The system applies local quality by limiting communication and observation to spatially proximal objects rather than requiring all agents to communicate with all other agents. Control agents only need to observe and communicate with objects in their local neighborhood, reducing the quantity of communication data while maintaining effective coordination through localized interactions.
Data Source
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Figure 3
AI summary
According to the invention, a plurality of objects (O1, O2) are fed into the machine (M), either in reality or in dynamic simulation. Furthermore, the machine continuously detects which of the objects (O1, O2) are currently adjacent to each other. In addition, each object (O1, O2) is assigned a control agent (P1, P2) into which current state data (S1, S2) of the respective object (O1, O2) are fed. Neurons of a neural network (GNN) are continuously linked according to the currently detected proximity of the objects (O1, O2). The neural network (GNN) controls the transmission of transfer data sets (TD) between the control agents (P1, P2). Furthermore, the control agents (P1, P2) use the input state data (S1, S2) and the transmitted transfer data sets (TD) to control the manipulation of the objects (O1, O2) in real life or in simulation, whereby the machine's (M) performance (RET) in this regard is determined.This trains the neural network (GNN) to optimize performance (RET). Finally, the machine (M) is controlled by the trained neural network (GNN) and the control agents (P1, P2).