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Learning what to Monitor: using Machine Learning to Improve Past STL Monitoring

Paper

Graphical abstract

Description

This repository contains the full paper presented at IJCAI 2024 (including the Appendix) and the supplementary material related to our paper "Learning what to Monitor: using Machine Learning to Improve Past STL Monitoring", authored by Andrea Brunello, Luca Geatti, Angelo Montanari, and Nicola Saccomanno.

Citation

If you use anything from our paper or code, please cite our work using the following format:

@inproceedings{ijcai2024p362,
  title     = {Learning What to Monitor: Using Machine Learning to Improve past STL Monitoring},
  author    = {Brunello, Andrea and Geatti, Luca and Montanari, Angelo and Saccomanno, Nicola},
  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     = {3270--3280},
  year      = {2024},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2024/362},
  url       = {https://doi.org/10.24963/ijcai.2024/362},
}