arXiv:2601.14764v2 Announce Type: replace 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 expl
As AI systems become more complex and integrated into critical decision-making processes, the demand for explainability and interpretability in AI, especially in symbolic AI, has surged.
This survey highlights a critical development in making advanced AI systems like Answer Set Programming (ASP) more transparent, addressing the growing need for trust and accountability in AI applications.
The focus on Explainable ASP from an XAI perspective indicates a maturation in the field, moving towards more comprehensive tools and methods for understanding AI reasoning.
- · AI developers
- · Auditors
- · Regulatory bodies
- · Sectors using complex AI
- · Black box AI systems
- · Users distrustful of AI
Increased adoption of symbolic AI techniques due to improved explainability.
Development of new regulatory frameworks that require explainable AI elements for deployment.
Enhanced human-AI collaboration in complex problem-solving, leading to novel applications in critical sectors.
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Read at arXiv cs.AI