ProMoAI: Process Modeling with Generative AI
- URL: http://arxiv.org/abs/2403.04327v2
- Date: Mon, 29 Apr 2024 12:24:24 GMT
- Title: ProMoAI: Process Modeling with Generative AI
- Authors: Humam Kourani, Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst,
- Abstract summary: ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions.
The tool also incorporates advanced prompt engineering, error handling, and code generation techniques.
- Score: 42.0652924091318
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, error handling, and code generation techniques. Beyond automating the generation of complex process models, ProMoAI also supports process model optimization. Users can interact with the tool by providing feedback on the generated model, which is then used for refining the process model. ProMoAI utilizes the capabilities LLMs to offer a novel, AI-driven approach to process modeling, significantly reducing the barrier to entry for users without deep technical knowledge in process modeling.
Related papers
- Model Callers for Transforming Predictive and Generative AI Applications [2.7195102129095003]
We introduce a novel software abstraction termed "model caller"
Model callers act as an intermediary for AI and ML model calling.
We have released a prototype Python library for model callers, accessible for installation via pip or for download from GitHub.
arXiv Detail & Related papers (2024-04-17T12:21:06Z) - Process Modeling With Large Language Models [42.0652924091318]
This paper explores the integration of Large Language Models (LLMs) into process modeling.
We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models.
Preliminary results demonstrate the framework's ability to streamline process modeling tasks.
arXiv Detail & Related papers (2024-03-12T11:27:47Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - On Augmenting Scenario-Based Modeling with Generative AI [1.4501446815590895]
We outline a method for the safer and more structured use of chatbots as part of the modeling process.
We propose leveraging scenario-based modeling techniques, which are known to facilitate the automated analysis of models.
We describe favorable preliminary results, which highlight the potential of this approach.
arXiv Detail & Related papers (2024-01-04T12:58:25Z) - Capturing Dependencies within Machine Learning via a Formal Process
Model [11.91042044893791]
Development of Machine Learning models is more than just a special case of software development (SD)
We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way.
We provide various interaction points with standard SD processes in which ML often is an encapsulated task.
arXiv Detail & Related papers (2022-08-10T08:45:37Z) - Extending Process Discovery with Model Complexity Optimization and
Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
The paper presents an approach to process mining providing semi-automatic support to model optimization.
A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity.
We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
arXiv Detail & Related papers (2022-06-10T16:20:59Z) - Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering.
In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
arXiv Detail & Related papers (2022-03-06T10:12:56Z) - A Learned Performance Model for Tensor Processing Units [5.733911161090224]
We demonstrate a method of learning performance models from a corpus of graph programs for Processing Unit (TPU) instances.
We show that our learned model outperforms a heavily-optimized analytical performance model on two tasks.
It helps an autotuner discover faster programs in a setting where access to TPUs is limited or expensive.
arXiv Detail & Related papers (2020-08-03T17:24:52Z) - VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven
Model Interpretability Applied to the Ironmaking Industry [70.10343492784465]
It is necessary to expose to the process engineer, not solely the model predictions, but also their interpretability.
Model-agnostic local interpretability solutions based on LIME have recently emerged to improve the original method.
We present in this paper a novel approach, VAE-LIME, for local interpretability of data-driven models forecasting the temperature of the hot metal produced by a blast furnace.
arXiv Detail & Related papers (2020-07-15T07:07:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.