Survey of Explainable Answer Set Programming: Methods, Systems, and Research Gaps
Researchers have published a survey on arXiv examining explainability approaches for Answer Set Programming (ASP), a symbolic AI reasoning paradigm, through the lens of Explainable AI (XAI). ASP's rule-based structure makes it naturally suited to interpretable reasoning, but existing explanation tools often address only narrow scenarios and leave gaps in coverage. The survey identifies those gaps and outlines research directions, contributing to the broader effort to make AI systems more transparent and accountable.
A new survey paper posted to arXiv (cs.AI) provides a structured overview of explanation methods and tools developed for Answer Set Programming, a declarative symbolic AI approach used for reasoning and problem solving. Guided by an XAI framework, the authors categorize types of ASP explanations in relation to the questions users typically need answered, then assess how well current theory and tooling address those needs. While ASP's rule-based formalism is inherently more interpretable than many machine learning approaches, the survey finds that existing explanation systems tend to be narrowly scoped and do not cover the full range of explanatory scenarios practitioners encounter. The paper explicitly pinpoints gaps in the current landscape and proposes directions for future research to close them. The work sits at the intersection of symbolic AI, logic programming, and the growing XAI field, which has gained urgency as AI systems are deployed in high-stakes domains requiring justification of their outputs. The survey is ten pages and was submitted in January 2026, with a revised version appearing in June 2026.
What's missing
The survey identifies research gaps but does not detail empirical evaluations comparing the effectiveness of existing ASP explanation tools against one another or against XAI methods for non-symbolic AI.
What different sources said
- arXiv cs.AICenter
An XAI View on Explainable ASP: Methods, Systems, and Perspectives
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