梳理可解释的逻辑编程方法，帮用户理解AI决策过程。
An XAI View on Explainable ASP: Methods, Systems, and Perspectives
- 从用户提问出发，分类整理ASP的解释类型。
- 评估现有工具对不同解释场景的覆盖程度。
- 指出现有方法短板，提出未来研究方向。
答案集编程（ASP）是符号AI中一种流行的声明式推理与问题求解方法。其基于规则的形式化使它天然适合可解释与可解释推理，这在可解释人工智能（XAI）兴起背景下愈发重要。已有多种针对ASP的解释方法与工具被开发，但往往针对特定解释场景，难以覆盖用户遇到的所有情况。本文从XAI视角出发，概述了与用户解释问题相关的ASP解释类型，并分析了现有理论与工具在解释覆盖方面的表现。此外，本文还指出现有解释方法的不足之处，并为未来研究提出方向。
原文摘要 · Abstract (English)
Answer Set Programming (ASP) is a popular declarative reasoning and problem solving approach in symbolic AI. Its rule-based formalism makes it inherently attractive for explainable and interpretive reasoning, which is gaining importance with the surge of Explainable AI (XAI). A number of explanation approaches and tools for ASP have been developed, which often tackle specific explanatory settings and may not cover all scenarios that ASP users encounter. In this survey, we provide, guided by an XAI perspective, an overview of types of ASP explanations in connection with user questions for explanation, and describe their coverage by current theory and tools. Furthermore, we pinpoint gaps in existing ASP explanations approaches and identify research directions for future work.
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