English(EN) Neuro-Symbolic Predictive Process Monitoring

神经符号AI通过逻辑改进业务流程监控

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-01 04:00

研究人员开发了一种新的神经符号预测过程监控方法，该方法将数据驱动的学习与时序逻辑相结合，以改进业务流程管理中的后缀预测。该方法将有限迹上的线性时序逻辑（LTLf）通过可微分逻辑损失函数整合到自回归序列预测器的训练中。在真实数据集上的实验表明，生成的后缀在准确性和逻辑一致性方面得到了增强，并且除了BPM之外，还可以应用于任何符号序列生成任务。 AI

影响引入了一种新颖的神经符号框架，用于通过逻辑一致性增强序列生成任务。

排序理由这是一篇详细介绍序列生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Axel Mezini, Elena Umili, Ivan Donadello, Fabrizio Maria Maggi, Matteo Mancanelli, Fabio Patrizi · 2026-06-01 04:00

神经符号预测过程监控

arXiv:2509.00834v2 Announce Type: replace Abstract: This paper addresses the problem of suffix prediction in Business Process Management (BPM) by proposing a Neuro-Symbolic Predictive Process Monitoring (PPM) approach that integrates data-driven learning with temporal logic-based…

本文如何被排名

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神经符号预测过程监控

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