AI Agent for Education: von Neumann Multi-Agent System Framework
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915086723973120 |
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| author | Jiang, Yuan-Hao Li, Ruijia Zhou, Yizhou Qi, Changyong Hu, Hanglei Wei, Yuang Jiang, Bo Wu, Yonghe |
| author_facet | Jiang, Yuan-Hao Li, Ruijia Zhou, Yizhou Qi, Changyong Hu, Hanglei Wei, Yuang Jiang, Bo Wu, Yonghe |
| contents | The development of large language models has ushered in new paradigms for education. This paper centers on the multi-Agent system in education and proposes the von Neumann multi-Agent system framework. It breaks down each AI Agent into four modules: control unit, logic unit, storage unit, and input-output devices, defining four types of operations: task deconstruction, self-reflection, memory processing, and tool invocation. Furthermore, it introduces related technologies such as Chain-of-Thought, Reson+Act, and Multi-Agent Debate associated with these four types of operations. The paper also discusses the ability enhancement cycle of a multi-Agent system for education, including the outer circulation for human learners to promote knowledge construction and the inner circulation for LLM-based-Agents to enhance swarm intelligence. Through collaboration and reflection, the multi-Agent system can better facilitate human learners' learning and enhance their teaching abilities in this process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00083 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI Agent for Education: von Neumann Multi-Agent System Framework Jiang, Yuan-Hao Li, Ruijia Zhou, Yizhou Qi, Changyong Hu, Hanglei Wei, Yuang Jiang, Bo Wu, Yonghe Multiagent Systems Artificial Intelligence Computers and Society The development of large language models has ushered in new paradigms for education. This paper centers on the multi-Agent system in education and proposes the von Neumann multi-Agent system framework. It breaks down each AI Agent into four modules: control unit, logic unit, storage unit, and input-output devices, defining four types of operations: task deconstruction, self-reflection, memory processing, and tool invocation. Furthermore, it introduces related technologies such as Chain-of-Thought, Reson+Act, and Multi-Agent Debate associated with these four types of operations. The paper also discusses the ability enhancement cycle of a multi-Agent system for education, including the outer circulation for human learners to promote knowledge construction and the inner circulation for LLM-based-Agents to enhance swarm intelligence. Through collaboration and reflection, the multi-Agent system can better facilitate human learners' learning and enhance their teaching abilities in this process. |
| title | AI Agent for Education: von Neumann Multi-Agent System Framework |
| topic | Multiagent Systems Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2501.00083 |