An XAI View on Explainable ASP: Methods, Systems, and Perspectives

TL;DR

This survey explores how explainable AI methods apply to Answer Set Programming, reviewing existing explanation approaches, their coverage, and identifying gaps and future research directions in the field.

Contribution

It provides a comprehensive overview of ASP explanation methods from an XAI perspective, highlighting coverage gaps and proposing future research directions.

Findings

Existing ASP explanation tools cover specific scenarios but lack comprehensive coverage.

There are notable gaps in current explanation approaches for ASP.

The survey identifies key research directions for advancing ASP explainability.

Abstract

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.