用逻辑+大模型实现可解释的课程调度，回答自然语言问题。
TRACE-CS: A Hybrid Logic-LLM System for Explainable Course Scheduling
- 结合符号推理与大模型处理调度约束和查询
- 生成可证明正确的解释，并转为易懂的自然语言
- 适合需要透明决策的教育系统或调度平台
我们提出 TRACE-CS，一种新型混合系统，将符号推理与大型语言模型（LLMs）结合，用于解决课程调度中的对比性查询。该系统利用基于逻辑的技术编码调度约束并生成可证明正确的解释，同时借助 LLM 处理自然语言查询，并将逻辑解释转化为用户友好的响应。此方法展示了如何通过融合符号知识表示与大模型能力，构建既逻辑严谨又自然语言可访问的可解释 AI 代理，解决了部署中调度系统面临的核心挑战。
原文摘要 · Abstract (English)
We present TRACE-CS, a novel hybrid system that combines symbolic reasoning with large language models (LLMs)to address contrastive queries in course scheduling problems. TRACE-CS leverages logic-based techniques to encode scheduling constraints and generate provably correct explanations, while utilizing an LLM to process natural language queries and refine logical explanations into user friendly responses. This system showcases how combining symbolic KR methods with LLMs creates explainable AI agents that balance logical correctness with natural language accessibility, addressing a fundamental challenge in deployed scheduling systems.
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