重新思考人工智能代理时代的流程挖掘

Alessandro Berti, Mayssa Maatallah, Urszula Jessen, Michal Sroka, Sonia Ayachi Ghannouchi
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引用次数: 0

摘要

大型语言模型(LLMs)已成为强大的对话界面,其在流程挖掘(PM)任务中的应用已显示出良好的效果。然而,最先进的 LLM 在应对需要高级推理能力的复杂场景时显得力不从心。文献中提出了利用 LLM 实现 PM 的两种主要方法:基于流程挖掘工件的文本抽象提供文本见解,以及生成可在原始工件上执行的代码。本文建议利用基于人工智能的代理工作流(AgWf)范例来提高 PM 在 LLM 上的有效性。这种方法可以:i)将复杂的任务分解为更简单的工作流;ii)将确定性工具与 LLM 的领域知识相结合。我们研究了 AgWf 的各种实现方式以及所涉及的基于人工智能的任务类型。此外,我们还讨论了 CrewAI 实现框架,并介绍了与流程挖掘相关的示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Re-Thinking Process Mining in the AI-Based Agents Era
Large Language Models (LLMs) have emerged as powerful conversational interfaces, and their application in process mining (PM) tasks has shown promising results. However, state-of-the-art LLMs struggle with complex scenarios that demand advanced reasoning capabilities. In the literature, two primary approaches have been proposed for implementing PM using LLMs: providing textual insights based on a textual abstraction of the process mining artifact, and generating code executable on the original artifact. This paper proposes utilizing the AI-Based Agents Workflow (AgWf) paradigm to enhance the effectiveness of PM on LLMs. This approach allows for: i) the decomposition of complex tasks into simpler workflows, and ii) the integration of deterministic tools with the domain knowledge of LLMs. We examine various implementations of AgWf and the types of AI-based tasks involved. Additionally, we discuss the CrewAI implementation framework and present examples related to process mining.
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