An XAI View on Explainable ASP: Methods, Systems, and Perspectives
It addresses the need for more comprehensive explainability in ASP for symbolic AI users, but is incremental as it synthesizes existing work.
This survey reviews explanation methods and tools for Answer Set Programming (ASP) in the context of Explainable AI (XAI), identifying gaps and future research directions to improve coverage for user needs.
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.