科学数据集的多智能体发现与资源感知自主探索
Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets
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中文总结 AI 辅助
针对单个研究者难以发现探索大规模科学数据集的问题，提出WebVisus多智能体系统，可基于自然语言问题启动自主智能体，适配资源探索数据集并完成案例验证。
中文摘要 AI 辅助
现代科学设施和仪器生成的数据集规模庞大，单个研究人员难以发现、访问和探索。尽管许多数据集是公开的，但使用它们通常需要熟悉仓库组织、数据格式、多分辨率结构和可视化参数。我们提出WebVisus，这是一个受约束且资源感知的多智能体系统，用于发现和自主探索远程多分辨率科学数据集。给定自然语言研究问题，WebVisus会识别用户意图并启动自主探索智能体，该智能体会检查切片、体积和时间步长，同时根据可用的客户端内存和计算资源调整数据分辨率和检索质量。这种设计支持渐进式探索，无需完整下载数据集或手动配置低级可视化参数。我们报告了系统架构、受约束的智能体协议、资源感知访问机制，以及评估自主视觉探索和跨科学数据集的资源感知智能体访问的案例研究。
英文摘要
Modern scientific facilities and instruments generate datasets at scales that are difficult for individual researchers to discover, access, and explore. Although many datasets are publicly available, using them often requires familiarity with repository organization, data formats, multiresolution structures, and visualization parameters. We present WebVisus, a constrained and resource-aware multi-agent system for discovering and autonomously exploring remote, multiresolution scientific datasets. Given a natural-language research question, WebVisus identifies the user's intent and launches an autonomous exploration agent that examines slices, volumes, and timesteps while adapting data resolution and retrieval quality to available client memory and computational resources. This design supports progressive exploration without complete dataset downloads or manual configuration of low-level visualization parameters using natural languages. We report the system architecture, constrained agent protocol, resource-aware access mechanism, and case studies evaluating autonomous visual exploration and resource-aware agentic access across scientific datasets.
发表机构
- University of Utah（犹他大学）
- Jet Propulsion Laboratory, Caltech（加州理工学院喷气推进实验室）
机构由 AI 辅助整理，请以论文原文为准。
