CENTRALIZED DISTRIBUTED AGENTIC AI
卷宗概要
申请人
DUO ZHANG
发明人
DUO ZHANG
IPC 分类
CPC 分类
A centralized distributed agentic artificial intelligence system has a central brain agent. The central brain agent is configured to act as a coordinator agent. The coordinator agent receives input from an operator. The coordinator agent has a coordinator agent large language model that updates knowledge to a knowledge graph. The coordinator agent receives historical data from the knowledge graph to a reinforcement learning policy optimization. The reinforcement learning policy optimization sends model optimizing policy to the coordinator agent large language model. Tentacle agents are configured to act as interface agents. The interface agents have a third-party system integration interface to a third-party system. The plurality of tentacle agents each have an interface agent large language model, local decision making model, and a goal setting and task delegation model. The interface agent large language model receives feedback from an interface agent reinforcement learning policy optimization.
原文(中文)
A centralized distributed agentic artificial intelligence system has a central brain agent. The central brain agent is configured to act as a coordinator agent. The coordinator agent receives input from an operator. The coordinator agent has a coordinator agent large language model that updates knowledge to a knowledge graph. The coordinator agent receives historical data from the knowledge graph to a reinforcement learning policy optimization. The reinforcement learning policy optimization sends model optimizing policy to the coordinator agent large language model. Tentacle agents are configured to act as interface agents. The interface agents have a third-party system integration interface to a third-party system. The plurality of tentacle agents each have an interface agent large language model, local decision making model, and a goal setting and task delegation model. The interface agent large language model receives feedback from an interface agent reinforcement learning policy optimization.