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.

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。其基于规则的表达形式使其天然适用于可解释与可推导的推理，随着可解释人工智能（XAI）的兴起，这一特性日益重要。目前已有多种针对ASP的解释方法与工具被开发出来，但这些方法往往针对特定的解释场景，可能无法覆盖ASP用户面临的所有情境。本综述从XAI视角出发，系统地概述了与用户解释需求相关的ASP解释类型，并描述了当前理论与工具对这些解释类型的覆盖范围。此外，我们指出了现有ASP解释方法中的空白，并为未来工作指明了研究方向。