Eiter, T., Geibinger, T., & Saribatur, Z. G. (2026). An XAI View on Explainable ASP: Methods, Systems, and Perspectives. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (pp. 7835–7844). https://doi.org/10.24963/ijcai.2026/870
E192-03 - Forschungsbereich Knowledge Based Systems E056-13 - Fachbereich LogiCS E056-17 - Fachbereich Trustworthy Autonomous Cyber-Physical Systems
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Published in:
Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence
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ISBN:
978-1-956792-09-6
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Date (published):
2026
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Event name:
35th International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026)
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Event date:
15-Aug-2026 - 21-Aug-2026
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Event place:
Bremen, Germany
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Number of Pages:
10
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Peer reviewed:
Yes
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Keywords:
Answer Set Programming; Explainable AI; Survey
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Abstract:
Answer Set Programming (ASP) is a popular declar- ative reasoning and problem solving approach in symbolic AI. Its rule-based formalism makes it in- herently attractive for explainable and interpretive reasoning, which is gaining importance with the surge of Explainable AI (XAI). A number of ex- planation 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 explanations in connection with user questions for explanation, and describe their coverage by current theory and tools. Furthermore, we pinpoint gaps in existing ASP explanations approaches and identify research directions for future work.
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Project title:
Bilateral Artificial Intelligence: COE 12 (FWF - Österr. Wissenschaftsfonds) Abstraktion für verständliches Schließen in der KI: T 1315-N (FWF - Österr. Wissenschaftsfonds) Learning Abstractions for Generalized Reasoning in AI: ICT25-044 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds)
