Survey maps XAI methods to Answer Set Programming explanations

A new survey paper examines Explainable AI (XAI) methods within Answer Set Programming (ASP), a symbolic AI approach. The paper categorizes different types of ASP explanations and maps them to user queries, evaluating the coverage provided by existing theories and tools. It also identifies current limitations and suggests future research directions in this area. AI

IMPACT Provides a structured overview of explainability techniques in symbolic AI, potentially guiding future research and development in interpretable AI systems.

RANK_REASON This is a survey paper on explainability methods within a specific AI subfield. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Answer Set Programming
• Explainable AI
• Tobias Geibinger

• paper
• safety

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →