用分层语言表示实验知识，让AI更高效设计自动驾驶实验室新流程。
Hierarchically Encapsulated Representation for Protocol Design in Self-Driving Labs
- 用领域语言分层封装操作、流程与动作，构建可复用的实验知识结构。
- 结合非参数化算法自动适配特定领域，提升知识表示的灵活性。
- 能辅助大模型规划、修改实验流程，适合智能科研自动化场景。
自动驾驶实验室正逐步取代人工执行单一实验技能或预设流程。然而，随着人工智能加速科学探索的迭代速度，快速设计新实验协议的需求日益突出。尽管已开展自动化协议设计研究，但基于知识的机器设计者（如大语言模型）的能力尚未被充分激发，可能源于缺乏系统化的实验知识表示，仅依赖孤立的碎片化信息。为此，我们提出一种多维度、多尺度的知识表示方法，通过领域专用语言将具体操作、通用动作和产品流模型进行分层封装。进一步开发了基于非参数建模的数据驱动算法，可自主为特定领域定制这些表示。所提表示支持多种机器设计者完成协议设计任务，包括规划、修改与调整。实验表明，该方法能有效补充大语言模型在协议设计中的能力，作为机器辅助科学探索中的辅助模块。
原文摘要 · Abstract (English)
Self-driving laboratories have begun to replace human experimenters in performing single experimental skills or predetermined experimental protocols. However, as the pace of idea iteration in scientific research has been intensified by Artificial Intelligence, the demand for rapid design of new protocols for new discoveries become evident. Efforts to automate protocol design have been initiated, but the capabilities of knowledge-based machine designers, such as Large Language Models, have not been fully elicited, probably for the absence of a systematic representation of experimental knowledge, as opposed to isolated, flatten pieces of information. To tackle this issue, we propose a multi-faceted, multi-scale representation, where instance actions, generalized operations, and product flow models are hierarchically encapsulated using Domain-Specific Languages. We further develop a data-driven algorithm based on non-parametric modeling that autonomously customizes these representations for specific domains. The proposed representation is equipped with various machine designers to manage protocol design tasks, including planning, modification, and adjustment. The results demonstrate that the proposed method could effectively complement Large Language Models in the protocol design process, serving as an auxiliary module in the realm of machine-assisted scientific exploration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。