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 how their coverage by current theory and tools. Furthermore, we pinpoint gaps in existing ASP explanations approaches and identify research directions for future work.

翻译：回答集编程（ASP）是符号人工智能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特性使其天生适用于可解释与可诠释推理，随着可解释人工智能（XAI）的兴起，这一特性正变得日益重要。目前已开发出多种针对ASP的解释方法与工具，但这些方法通常针对特定解释场景，可能无法覆盖ASP用户遇到的所有情况。本综述从XAI视角出发，系统梳理了与用户解释需求相关联的ASP解释类型，阐述了当前理论与工具对这些类型的覆盖情况。进一步地，我们指出现有ASP解释方法存在的不足，并明确了未来工作的研究方向。