用魔术集技术提升时序逻辑推理效率，显著加速复杂任务求解。
Goal-Driven Reasoning in DatalogMTL with Magic Sets
- 引入魔术集重写技术，将自顶向下推理转为高效自底向上计算
- 在公开基准上性能超越当前最优方法，提升稳定且显著
- 适合工业与金融领域需要高精度时序推理的场景
DatalogMTL 是一种强大的基于规则的时序推理语言，因其高度表达能力和灵活建模能力，适用于工业和金融等多个领域。然而，由于其高计算复杂度，实际推理极具挑战。为此，本文提出一种新的 DatalogMTL 推理方法，利用魔术集技术——一种为非时序 Datalog 开发的重写方法，可模拟自顶向下求解过程，但通过自底向上方式实现。我们已实现该方法，并在公开基准上进行评估，结果表明，所提方法在多个数据集上显著且一致地优于现有最先进的推理技术。
原文摘要 · Abstract (English)
DatalogMTL is a powerful rule-based language for temporal reasoning. Due to its high expressive power and flexible modeling capabilities, it is suitable for a wide range of applications, including tasks from industrial and financial sectors. However, due to its high computational complexity, practical reasoning in DatalogMTL is highly challenging. To address this difficulty, we introduce a new reasoning method for DatalogMTL which exploits the magic sets technique -- a rewriting approach developed for (non-temporal) Datalog to simulate top-down evaluation with bottom-up reasoning. We have implemented this approach and evaluated it on publicly available benchmarks, showing that the proposed approach significantly and consistently outperformed state-of-the-art reasoning techniques.
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