AI 中文总结
该研究基于FastLAS2框架，从模拟气象数据和气象公报提取数据生成ILP示例，进而推断解释性假设，能解释人类专家发布的天气预报及背后原理，方法通用，可用于不同地区的气象公报。
AI 中文摘要
归纳逻辑编程（ILP）起源于20世纪90年代的逻辑编程社区，是一种将符号学习与声明性知识表示相结合的框架。如今，成熟的ILP框架已经存在，能够学习复杂的非单调假设。这项工作主要基于FastLAS2框架，旨在生成简单、可解释的假设，以帮助阐明意大利弗留利-威尼斯朱利亚地区的区域气象观测站OSMER FVG发布的气象公报。本文提出了一个管道，从模拟气象原始数据和OSMER的公报（用作地面真值）开始，提取数据作为ASP事实并生成ILP示例。然后通过FastLAS2从这些示例中推断出一个解释性假设。这样的假设（翻译成自然语言）解释了人类专家发布的天气预报，特别是专家在公报象形图（预测的符号注释气象图）中选择特定符号背后的基本原理。所提出的方法是通用的，不限于任何特定地区，并且同样可以应用于来自其他来源和不同地区的公报。
英文摘要
Inductive Logic Programming (ILP) originated within the Logic Programming community in the Nineties as a framework for combining symbolic learning with declarative knowledge representation. Nowadays, mature ILP frameworks exist and they are capable of learning complex, non-monotonic hypotheses, thus broadening both the modeling capabilities and the scope of real-world applications of ILP. This work is primarily based on the FastLAS2 framework and aims to generate simple, interpretable hypotheses to help clarify the weather bulletins issued by OSMER FVG, the Regional Meteorological Observatory of the Italian region of Friuli Venezia-Giulia. In this paper we present a pipeline that, starting from simulated meteorological raw data and from OSMERs' bulletins (used as ground truth), extracts data as ASP facts and generates ILP examples. From such examples an explanatory hypothesis is then inferred via FastLAS2. Such a hypothesis (translated into natural language) explains the weather forecast issued by human experts, and in particular the rationale behind experts' choices of specific symbols in the bulletin pictogram (the symbol-annotated meteorological map of the forecast). The proposed approach is general, not specific to any particular region and it can equally be applied to bulletins from other sources and to different regions.
CommentsIn Proceedings ICLP 2026, arXiv:2607.17707
Journal refEPTCS 450, 2026, pp. 15-28
