Group Agent Coordinating Personal Agents for User Support
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Solution Overview
Problem
Existing agent systems lack the capability to provide optimized support for groups of users, as they are primarily designed for individual users and do not effectively integrate personal agent information to support collective needs and activities.
Innovation Solution
An information processing system that includes a personal agent personalized to each user and a group agent that collaborates with multiple personal agents to provide support tailored for groups, such as families or workplaces, by managing and integrating user information and determination criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a system uses only personal agents for individual users, then individual user support is optimized, but group support capability is insufficient
Solution Approach 1:
The personal agent is designed to perform multiple functions: it provides individual user support while also contributing to group support by sharing information and determination criteria with the group agent. This multi-functionality resolves the contradiction by enabling the same agent to serve both individual and group needs without requiring completely separate systems.
Solution Approach 2:
The personal agent is nested within the group agent structure, where multiple personal agents form a hierarchy under a group agent. The personal agent handles individual tasks while the group agent coordinates across multiple personal agents. This nested architecture enables both individual optimization and group coordination to coexist.
2Adaptability or versatility
If a system integrates multiple personal agents for group support, then group support capability is improved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional components: personal agents for individual tasks and a group agent for coordination. This segmentation reduces complexity by creating clear boundaries and responsibilities, where each component focuses on specific functions rather than requiring a monolithic complex system to handle all scenarios.
Solution Approach 2:
The group agent acts as an intermediary between multiple personal agents, mediating their interactions and coordinating their activities. This intermediary role simplifies the system architecture by providing a central coordination point, reducing the need for complex direct-to-direct communication protocols between all personal agents.
3Productivity
If personal agents share information with the group agent, then group optimization is achieved, but information privacy concerns increase
Solution Approach 1:
The system implements local quality by allowing different information sharing policies for different types of data. Personal agents can share certain determination criteria and aggregated information with the group agent while maintaining privacy controls over sensitive personal data. This selective sharing approach enables group optimization without requiring complete information disclosure.
Solution Approach 2:
The system changes the parameter of information granularity by sharing aggregated or processed information rather than raw personal data. Personal agents transform detailed personal information into group-relevant parameters that maintain utility for group optimization while reducing privacy risks through information abstraction and generalization.
Data Source
AI summary
An information processing system includes a control unit (200) that controls: a personal agent personalized to a user; and a group agent that provides support for a group made up of a plurality of users corresponding to a plurality of the personal agents.