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cs.AI2026
CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning
Xinrui Lin, Yangfan Wu, Huanyu Yang +3
Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planning in open-world scenarios. Howe…
cs.AI2026
A Four-Valued Normative Intermediate Representation for ASP-Oriented Compliance Reasoning
Huanyu Yang, Yangfan Wu, Jianmin Ji
Technical-standard compliance reasoning may involve incomplete evidence, inconsistent observations, exceptions, and derived normative outputs. This paper presents \textsc{Monir}, a…
cs.AI2025
Diminution: On Reducing the Size of Grounding ASP Programs
HuanYu Yang, Fengming Zhu, YangFan Wu +1
Answer Set Programming (ASP) is often hindered by the grounding bottleneck: large Herbrand universes generate ground programs so large that solving becomes difficult. Many methods…