# Answering the "why" in Answer Set Programming - A Survey of Explanation Approaches

arXiv: https://arxiv.org/abs/1809.08034 · HTML: https://mlsanity.com/paper/1809.08034/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [Argumentative XAI: A Survey](https://mlsanity.com/paper/2105.11266/) (2021)
- [An ASP Framework for the Refinement of Authorization and Obligation Policies](https://mlsanity.com/paper/2305.13190/) (2023)
- [Explainable Answer-set Programming](https://mlsanity.com/paper/2308.15901/) (2023)
- [Learning logic programs by explaining their failures](https://mlsanity.com/paper/2102.12551/) (2021)
- [ValAsp: a tool for data validation in Answer Set Programming](https://mlsanity.com/paper/2202.09626/) (2022)
- [Counting Answer Sets of Disjunctive Answer Set Programs](https://mlsanity.com/paper/2507.11655/) (2025)
- [Interpolation in Knowledge Representation](https://mlsanity.com/paper/2512.08833/) (2025)
- [xDNN(ASP): Explanation Generation System for Deep Neural Networks powered by Answer Set Programming](https://mlsanity.com/paper/2601.03847/) (2026)
- [An XAI View on Explainable ASP: Methods, Systems, and Perspectives](https://mlsanity.com/paper/2601.14764/) (2026)
- [The Dual Role of Abstracting over the Irrelevant in Symbolic Explanations: Cognitive Effort vs. Understanding](https://mlsanity.com/paper/2602.03467/) (2026)
- [Explainable Belief Harmonization under Dynamic Epistemic Partitions](https://mlsanity.com/paper/2607.21210/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union's new General Data Protection Regulation tries to tackle this problem by stipulating a "right to explanation" for decisions made by AI systems. One of the AI paradigms that may be affected by this new regulation is Answer Set Programming (ASP). Thanks to the emergence of efficient solvers, ASP has recently been used for problem-solving in a variety of domains, including medicine, cryptography, and biology. To ensure the successful application of ASP as a problem-solving paradigm in the future, explanations of ASP solutions are crucial. In this survey, we give an overview of approaches that provide an answer to the question of why an answer set is a solution to a given problem, notably off-line justifications, causal graphs, argumentative explanations and why-not provenance, and highlight their similarities and differences. Moreover, we review methods explaining why a set of literals is not an answer set or why no solution exists at all.