Explaining Weather Bulletins via ILP

2026-07-23

AI Summary

This work addresses the challenge of enhancing the interpretability of weather forecasts by automatically explaining the rationale behind meteorologists’ selection of specific graphical symbols in weather bulletins. Leveraging the FastLAS2 framework, the study extracts Answer Set Programming (ASP) facts from synthetic meteorological data and real bulletins issued by OSMER FVG to construct inductive logic programming (ILP) examples, from which concise and human-readable logical rules are learned. It pioneers the application of non-monotonic ILP to the task of meteorological bulletin interpretation, enabling a symbolic and generalizable model of expert decision-making. The proposed approach not only accurately uncovers the underlying logic governing symbol selection but also demonstrates robust generalizability across different geographical regions and data sources.

Abstract

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.

Problem

• Weather Bulletins
• Interpretability
• Symbol Explanation
• Meteorological Forecast
• Expert Rationale

Innovation

• Inductive Logic Programming
• Explainable AI
• Answer Set Programming
• Weather Forecast Interpretation
• FastLAS2