Multiagent Grouping Quantitative Evaluation System
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Solution Overview
Problem
Conventional multiagent grouping technologies face issues such as reduced yield due to information inundation and starvation, reliance on qualitative evaluations, and inadequacy in handling dynamic role changes in distributed ubiquitous computing, as well as inefficiencies in grouping based on individual satisfactions.
Innovation Solution
A quantitative evaluation system that assesses agent characteristics using RQ and TR values to balance information production and consumption, forming communities that minimize inundation and starvation, and optimizing interactions based on Utility of Predicate, Utility of Agent, and Utility of Community calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agents are grouped based on qualitative evaluation, then agent characteristics can be assessed, but the evaluation precision is insufficient
Solution Approach 1:
The patent transforms qualitative agent characteristics into quantitative parameters by introducing specific metrics such as information production quantity, information consumption quantity, and interaction frequency. This parameter transformation enables precise measurement and comparison of agent characteristics, directly resolving the contradiction between evaluation precision and system complexity.
2Loss of information
If agents are grouped without quantitative evaluation, then system complexity is reduced, but information flow balance deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where quantitative evaluation results of agent characteristics are continuously used to adjust and optimize agent grouping. This feedback loop ensures that information production and consumption remain balanced across communities, preventing information loss while maintaining manageable system complexity through iterative improvement.
Solution Approach 2:
By introducing quantitative parameters to measure information production and consumption, the system can dynamically adjust grouping to maintain information balance. These parameter-based evaluations enable the system to prevent information loss without requiring overly complex qualitative assessment frameworks.
3Productivity
If agent grouping is based on individual satisfaction, then agent autonomy is maintained, but overall system productivity decreases
Solution Approach 1:
The patent merges individual agent satisfaction criteria with overall system productivity goals into a unified quantitative evaluation framework. By combining multiple evaluation dimensions (information production, consumption, interaction patterns) into a comprehensive scoring system, the patent achieves both agent autonomy and system-wide productivity improvement through coordinated optimization.
Data Source
AI summary
Disclosed is a quantitative evaluation system and method for multiagent grouping, which enables agents to be grouped in such a manner as to ensure efficient agent cooperation. The system comprises: a plurality of agents in a network-connected multiagent system, each of which agents has quantitative information on a predicate that is a type of information to be produced or consumed, and produces or consumes one or more types of information; an adjusting means for receiving the quantitative information from each of the agents, performing quantitative evaluation thereof, and then grouping the plurality of agents into one or more communities; and a message broker means for transferring the information gathered from the plurality of agents to the adjusting means.