Uncertain multi-agent MILPs: A data-driven decentralized solution with probabilistic feasibility guarantees

Alessandro Falsone, Federico Molinari, Maria Prandini. Uncertain multi-agent MILPs: A data-driven decentralized solution with probabilistic feasibility guarantees. In Alexandre M. Bayen, Ali Jadbabaie, George J. Pappas, Pablo A. Parrilo, Benjamin Recht, Claire J. Tomlin, Melanie N. Zeilinger, editors, Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, L4DC 2020, Online Event, Berkeley, CA, USA, 11-12 June 2020. Volume 120 of Proceedings of Machine Learning Research, pages 1000-1009, PMLR, 2020. [doi]

@inproceedings{FalsoneMP20,
  title = {Uncertain multi-agent MILPs: A data-driven decentralized solution with probabilistic feasibility guarantees},
  author = {Alessandro Falsone and Federico Molinari and Maria Prandini},
  year = {2020},
  url = {http://proceedings.mlr.press/v120/falsone20a.html},
  researchr = {https://researchr.org/publication/FalsoneMP20},
  cites = {0},
  citedby = {0},
  pages = {1000-1009},
  booktitle = {Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, L4DC 2020, Online Event, Berkeley, CA, USA, 11-12 June 2020},
  editor = {Alexandre M. Bayen and Ali Jadbabaie and George J. Pappas and Pablo A. Parrilo and Benjamin Recht and Claire J. Tomlin and Melanie N. Zeilinger},
  volume = {120},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
}