Historical Data Transfer for Faster Multi-Agent Onboarding
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Solution Overview
Problem
Existing multi-agent systems face challenges in quickly and cost-effectively onboarding new agents without detrimental impact on system performance, as current training and quality control processes are costly and time-consuming.
Innovation Solution
A method for transferring historical data from operating agents to new agents using reinforcement learning and transfer learning, combined with bootstrapping from heterogeneous agents, to facilitate faster and more efficient onboarding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional training and quality control processes are used for onboarding new agents, then system performance and reliability are maintained, but onboarding time and costs increase significantly
Solution Approach 1:
The system performs preliminary training and quality control actions by transferring historical data and experience from operating agents to new agents before they are fully onboarded. This allows new agents to acquire necessary knowledge and skills in advance, reducing the time required for traditional training processes while maintaining system performance standards.
Solution Approach 2:
The system creates copies of historical data, experience, and knowledge from operating agents and transfers them to new agents. Instead of training new agents from scratch, the system replicates proven successful patterns and behaviors, significantly accelerating the onboarding process while preserving reliability through validated historical performance data.
2Reliability
If traditional training processes are used for onboarding new agents, then quality control is ensured, but training costs and resource requirements increase
Solution Approach 1:
The system merges the training processes of multiple operating agents by consolidating their historical data and experience into a shared knowledge base. New agents access this combined repository, eliminating the need for separate, resource-intensive training processes for each agent while maintaining comprehensive quality control through aggregated historical performance data.
Solution Approach 2:
The system copies validated historical data and performance patterns from operating agents to new agents, replacing costly traditional training processes. This approach transfers proven knowledge efficiently, reducing resource consumption while ensuring quality control through the use of historically validated performance standards.
3Productivity
If historical data is transferred from operating agents to new agents, then onboarding speed increases, but data selection complexity increases
Solution Approach 1:
The system implements feedback mechanisms that monitor the performance of new agents after data transfer and adjust the selection of historical data accordingly. This iterative process refines the data selection criteria over time, managing complexity by using performance feedback to guide and optimize which historical data is transferred, rather than requiring complex upfront selection processes.
Solution Approach 2:
The system changes parameters related to data selection by dynamically adjusting which historical data attributes are transferred based on the specific needs and context of each new agent. Rather than using a fixed complex selection process, the system adapts data transfer parameters to optimize onboarding speed while managing selection complexity through flexible, context-aware parameter adjustment.
Data Source
AI summary
A computer-implemented method performed in a multi-agent system by a first network node is provided for transferring historical data from an operating agent to a second agent for an action controlling a performance of the multi-agent system. The method includes selecting at least one operating agent for transfer of historical data to the second agent. The historical data acquired from executions of an action by the at least one operating agent that at least partially fulfills an input parameter. The selecting is based on one or more criteria including (i) a performance of the at least one operating agent on the parameter or on a related parameter; (ii) an availability of the at least one operating agent; and (iii) an identity of an actuation target system for receipt of the action. The method further includes transferring the historical data to the second agent.