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Computer Science > Artificial Intelligence

arXiv:2402.04578 (cs)
[Submitted on 7 Feb 2024 (v1), last revised 13 Sep 2024 (this version, v4)]

Title:S-Agents: Self-organizing Agents in Open-ended Environments

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Abstract:Leveraging large language models (LLMs), autonomous agents have significantly improved, gaining the ability to handle a variety of tasks. In open-ended settings, optimizing collaboration for efficiency and effectiveness demands flexible adjustments. Despite this, current research mainly emphasizes fixed, task-oriented workflows and overlooks agent-centric organizational structures. Drawing inspiration from human organizational behavior, we introduce a self-organizing agent system (S-Agents) with a "tree of agents" structure for dynamic workflow, an "hourglass agent architecture" for balancing information priorities, and a "non-obstructive collaboration" method to allow asynchronous task execution among agents. This structure can autonomously coordinate a group of agents, efficiently addressing the challenges of open and dynamic environments without human intervention. Our experiments demonstrate that S-Agents proficiently execute collaborative building tasks and resource collection in the Minecraft environment, validating their effectiveness.
Comments:ICLR 2024 Workshop on Large Language Model (LLM) Agents
Subjects:Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as:arXiv:2402.04578 [cs.AI]
 (orarXiv:2402.04578v4 [cs.AI] for this version)
 https://doi.org/10.48550/arXiv.2402.04578
arXiv-issued DOI via DataCite

Submission history

From: Jiaqi Chen [view email]
[v1] Wed, 7 Feb 2024 04:36:31 UTC (33,284 KB)
[v2] Thu, 8 Feb 2024 17:01:00 UTC (33,188 KB)
[v3] Mon, 18 Mar 2024 05:56:42 UTC (4,977 KB)
[v4] Fri, 13 Sep 2024 19:40:12 UTC (5,197 KB)
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