Related papers: S-Agents: Self-organizing Agents in Open-ended Environments

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

URL: http://arxiv.org/abs/2402.04578v3
Date: Mon, 18 Mar 2024 05:56:42 GMT
Title: S-Agents: Self-organizing Agents in Open-ended Environments
Authors: Jiaqi Chen, Yuxian Jiang, Jiachen Lu, Li Zhang,
Abstract summary: We introduce a self-organizing agent system (S-Agents) with a "tree of agents" structure for dynamic workflow. This structure can autonomously coordinate a group of agents, efficiently addressing the challenges of open and dynamic environments. Our experiments demonstrate that S-Agents proficiently execute collaborative building tasks and resource collection in the Minecraft environment.
Score: 15.700383873385892
License: http://creativecommons.org/licenses/by/4.0/
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.

Related papers

MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration [8.078098082305575]
This paper introduces MorphAgent, a novel framework for decentralized multi-agent collaboration. MorphAgent employs self-evolving agent profiles, optimized through three key metrics. Our experimental results show that MorphAgent outperforms traditional static-role MAS in terms of task performance and adaptability to changing requirements.
arXiv Detail & Related papers (2024-10-19T09:10:49Z)
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [117.94654815220404]
G"odel Agent is a self-evolving framework inspired by the G"odel machine. G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
arXiv Detail & Related papers (2024-10-06T10:49:40Z)
GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI [64.57616646552869]
This paper explores collaborative AI systems that use to enhance performance to integrate models, data sources, and pipelines to solve complex and diverse tasks. We introduce GenAgent, an LLM-based framework that automatically generates complex, offering greater flexibility and scalability compared to monolithic models. The results demonstrate that GenAgent outperforms baseline approaches in both run-level and task-level evaluations.
arXiv Detail & Related papers (2024-09-02T17:44:10Z)
Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
arXiv Detail & Related papers (2024-07-09T17:33:24Z)
Agents meet OKR: An Object and Key Results Driven Agent System with Hierarchical Self-Collaboration and Self-Evaluation [25.308341461293857]
OKR-Agent is designed to enhance the capabilities of Large Language Models (LLMs) in task-solving. Our framework includes two novel modules: hierarchical Objects and Key Results generation and multi-level evaluation.
arXiv Detail & Related papers (2023-11-28T06:16:30Z)
Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization [59.39113350538332]
Large language model (LLM) agents have been shown effective on a wide range of tasks, and by ensembling multiple LLM agents, their performances could be further improved. Existing approaches employ a fixed set of agents to interact with each other in a static architecture. We build a framework named Dynamic LLM-Agent Network ($textbfDyLAN$) for LLM-agent collaboration on complicated tasks like reasoning and code generation.
arXiv Detail & Related papers (2023-10-03T16:05:48Z)
ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
arXiv Detail & Related papers (2023-08-22T10:36:56Z)
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors [93.38830440346783]
We propose a multi-agent framework framework that can collaboratively adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that framework framework can effectively deploy multi-agent groups that outperform a single agent. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups.
arXiv Detail & Related papers (2023-08-21T16:47:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.