ProMoAI: Process Modeling with Generative AI
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. Demo Track. Pages 8708-8712.
https://doi.org/10.24963/ijcai.2024/1014
Keywords
- Natural Language Processing: NLP: Language models
- Humans and AI: General
- Natural Language Processing: NLP: Applications
- Natural Language Processing: NLP: Dialogue and interactive systems
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.
@inproceedings{ijcai2024p1014,
title = {ProMoAI: Process Modeling with Generative AI},
author = {Kourani, Humam and Berti, Alessandro and Schuster, Daniel and van der Aalst, Wil M.P.},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8708--8712},
year = {2024},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2024/1014},
url = {https://doi.org/10.24963/ijcai.2024/1014},
}
TY - CPAPER TI - ProMoAI: Process Modeling with Generative AI AU - Kourani, Humam AU - Berti, Alessandro AU - Schuster, Daniel AU - van der Aalst, Wil M.P. T2 - Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2024 SP - 8708 EP - 8712 DO - 10.24963/ijcai.2024/1014 UR - https://doi.org/10.24963/ijcai.2024/1014 ER -Copied
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