Summary of Re-thinking Process Mining in the Ai-based Agents Era, by Alessandro Berti et al.
Re-Thinking Process Mining in the AI-Based Agents Era
by Alessandro Berti, Mayssa Maatallah, Urszula Jessen, Michal Sroka, Sonia Ayachi Ghannouchi
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel approach to enhancing the effectiveness of Process Mining (PM) on Large Language Models (LLMs). By utilizing the AI-Based Agents Workflow (AgWf) paradigm, the authors aim to decompose complex tasks into simpler workflows and integrate deterministic tools with the domain knowledge of LLMs. The approach is explored through various implementations of AgWf and the types of AI-based tasks involved. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes process mining more effective on large language models by breaking down complicated tasks into smaller steps and combining them with the model’s understanding. It shows how to use this idea in different ways and what kinds of jobs it can do. |
