ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909432715149312 |
|---|---|
| author | Huang, Yi Cheng, Fangyin Zhou, Fan Li, Jiahui Gong, Jian Yang, Hongjun Fan, Zhidong Jiang, Caigao Xue, Siqiao Chen, Faqiang |
| author_facet | Huang, Yi Cheng, Fangyin Zhou, Fan Li, Jiahui Gong, Jian Yang, Hongjun Fan, Zhidong Jiang, Caigao Xue, Siqiao Chen, Faqiang |
| contents | In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in data analytics when integrated with Multi-Agent Systems (MAS). However, these systems often struggle with complex tasks that involve diverse functional requirements and intricate data processing challenges, necessitating customized solutions that lack broad applicability. Furthermore, current MAS fail to emulate essential human-like traits such as self-planning, self-monitoring, and collaborative work in dynamic environments, leading to inefficiencies and resource wastage. To address these limitations, we propose ROMAS, a novel Role-Based M ulti-A gent System designed to adapt to various scenarios while enabling low code development and one-click deployment. ROMAS has been effectively deployed in DB-GPT [Xue et al., 2023a, 2024b], a well-known project utilizing LLM-powered database analytics, showcasing its practical utility in real-world scenarios. By integrating role-based collaborative mechanisms for self-monitoring and self-planning, and leveraging existing MAS capabilities to enhance database interactions, ROMAS offers a more effective and versatile solution. Experimental evaluations of ROMAS demonstrate its superiority across multiple scenarios, highlighting its potential to advance the field of multi-agent data analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13520 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning Huang, Yi Cheng, Fangyin Zhou, Fan Li, Jiahui Gong, Jian Yang, Hongjun Fan, Zhidong Jiang, Caigao Xue, Siqiao Chen, Faqiang Artificial Intelligence Databases Multiagent Systems In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in data analytics when integrated with Multi-Agent Systems (MAS). However, these systems often struggle with complex tasks that involve diverse functional requirements and intricate data processing challenges, necessitating customized solutions that lack broad applicability. Furthermore, current MAS fail to emulate essential human-like traits such as self-planning, self-monitoring, and collaborative work in dynamic environments, leading to inefficiencies and resource wastage. To address these limitations, we propose ROMAS, a novel Role-Based M ulti-A gent System designed to adapt to various scenarios while enabling low code development and one-click deployment. ROMAS has been effectively deployed in DB-GPT [Xue et al., 2023a, 2024b], a well-known project utilizing LLM-powered database analytics, showcasing its practical utility in real-world scenarios. By integrating role-based collaborative mechanisms for self-monitoring and self-planning, and leveraging existing MAS capabilities to enhance database interactions, ROMAS offers a more effective and versatile solution. Experimental evaluations of ROMAS demonstrate its superiority across multiple scenarios, highlighting its potential to advance the field of multi-agent data analytics. |
| title | ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning |
| topic | Artificial Intelligence Databases Multiagent Systems |
| url | https://arxiv.org/abs/2412.13520 |