Survey of Explainable Answer Set Programming: Methods, Systems, and Research Gaps
Researchers have published a survey examining explainability approaches in Answer Set Programming (ASP), a symbolic AI method based on rule-based reasoning. The survey maps existing explanation methods and tools against user needs and identifies gaps in current approaches. This work is relevant as explainability becomes increasingly important in AI systems, particularly for rule-based symbolic reasoning.
A new survey paper examines the state of explainable Answer Set Programming (ASP), a declarative reasoning approach in symbolic AI that uses rule-based formalism. The authors, guided by principles from Explainable AI (XAI), provide an overview of explanation types in ASP and how they address different user questions about system reasoning. The survey catalogs existing explanation approaches and tools while identifying which explanatory scenarios they cover and which remain unaddressed. The work pinpoints specific gaps in current ASP explanation methods and outlines research directions for future development. This survey is timely given the growing emphasis on explainability and interpretability in AI systems across both academic and practical applications.
What different sources said
- arXiv cs.AICenter
An XAI View on Explainable ASP: Methods, Systems, and Perspectives
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