自动从船务报告中提炼出简洁有效的信息模板。
ASMR: Agentic Schema Generation for Ship Maintenance Report Writing
- 分两步生成：先提取语义概念，再用强化学习优化结构
- 生成的模板能提升报告完整性与一致性，减少冗余
- 适合需要规范报告流程的航运、工业维护领域
本文研究自动模式生成问题：给定多类别的历史船舶维护与运营报告，自动发现紧凑且信息丰富的模式，以捕捉每种报告类型的必要信息需求。为此，我们提出ASMR，一个模块化智能体框架，包含两个专用智能体：字段生成智能体通过自适应多粒度聚类从历史文本中提取语义概念并生成候选字段；结构优化智能体采用强化学习识别紧凑、信息丰富且无冗余的模式表示。生成的模式可引导报告撰写者产出更完整、一致且可操作的报告。初步结果验证了该方法的有效性，并揭示了数据管理、智能体人工智能与以人为本的AI交叉领域的若干开放挑战。
原文摘要 · Abstract (English)
In this paper, we study the automatic schema generation problem: given a collection of historical ship maintenance and operational reports across multiple form categories, automatically discover compact and informative schemas that capture the essential information requirements of each report type. To address this challenge, we propose ASMR, a modular agentic framework consisting of two specialized agents. A Field Generation Agent extracts semantic concepts from historical narratives and generates candidate schema fields through adaptive multi-granularity clustering, while a Structural Optimizer Agent employs reinforcement learning to identify compact, informative, and non-redundant schema representations. The resulting schemas can guide report authors toward producing more complete, consistent, and actionable reports. Preliminary results demonstrate the promise of the proposed approach and highlight several open research challenges at the intersection of data management, agentic AI, and human-centered AI.
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