Decentralized Edge Unit Collaboration for Multi-Agent Task Execution
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Solution Overview
Problem
Traditional AI/ML algorithms and multi-agent systems lack decentralized execution capabilities, failing to consider the interactions and collective goals among decision-making entities, and existing Federated Learning methods do not effectively enable collaborative task execution among diverse devices.
Innovation Solution
A decentralized collaborative intelligence system utilizing edge units with embedded CoIN modules for peer-to-peer communication and task planning, allowing edge devices to dynamically collaborate and share sensor data to execute tasks efficiently without centralized control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional AI/ML algorithms and centralized multi-agent systems are used, then decision-making can be achieved, but decentralized execution capabilities and consideration of interactions among decision-making entities are lacking
Solution Approach 1:
The system segments the multi-agent system into autonomous edge units, each capable of independent decision-making. Each edge unit is divided into functional modules (sensor module, communication module, collaborative intelligence module, actuator module) that can operate independently yet cooperatively, enabling decentralized execution while managing complexity through modular design.
Solution Approach 2:
The patent introduces a new dimension of collaboration by enabling peer-to-peer communication and joint workspace sharing among edge units. This adds a collaborative dimension to traditional autonomous systems, allowing agents to share information and coordinate actions without centralized control, thus achieving decentralized execution.
2Productivity
If Federated Learning methods are used, then private data security is maintained, but effective collaborative task execution among diverse devices is not achieved
Solution Approach 1:
The joint workspace is designed as a universal interface that can accommodate different data formats, sensor types, and communication protocols from diverse devices. The workspace structure allows heterogeneous edge units to contribute their specific capabilities while maintaining a common collaboration framework, enabling both high productivity and broad adaptability.
Solution Approach 2:
The system dynamically adjusts communication parameters, data exchange formats, and collaboration protocols based on the specific capabilities and requirements of participating edge units. This parameter adaptation allows diverse devices to collaborate effectively while maintaining their individual characteristics and data security.
3Reliability
If edge units exchange sensor data and plans peer-to-peer, then collaborative task execution is enabled, but communication overhead and coordination complexity increase
Solution Approach 1:
Edge units perform preliminary local processing and filtering of sensor data before exchange, preparing only relevant and processed information for communication. The collaborative intelligence module pre-coordinates task allocation and data sharing requirements, reducing the volume and complexity of actual peer-to-peer exchanges while maintaining collaborative reliability.
Data Source
AI summary
A system for collaborative execution of a task includes a plurality of edge units in communication with a principal processor. Each edge unit includes a collaborative intelligence module configured to establish a communication link amongst at least two of the edge units. The communication intelligence module is further configured to communicate task attributes of the task with at least one other edge unit, and to exchange sensor data from the set of sensors and a plan with the at least one other edge unit to create a joint workspace followed by execution of the task in collaboration with the at least one other edge unit.