On-the-fly adaptation of patrolling strategies in changing environments

@InProceedings{pmlr-v180-brazdil22a,
  title = 	 {On-the-fly adaptation of patrolling strategies in changing environments},
  author =       {Br{\'a}zdil, Tom{\'a}{\v{s}} and Kla{\v{s}}ka, David and Ku{\v{c}}era, Anton{\'{\i}}n and Musil, V{\'{\i}}t and Novotn{\'y}, Petr and {\v{R}}eh{\'a}k, Vojt{\v{e}}ch},
  booktitle = 	 {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {244--254},
  year = 	 {2022},
  editor = 	 {Cussens, James and Zhang, Kun},
  volume = 	 {180},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {01--05 Aug},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v180/brazdil22a/brazdil22a.pdf},
  url = 	 {https://proceedings.mlr.press/v180/brazdil22a.html},
  abstract = 	 { We consider the problem of efficient patrolling strategy adaptation in a changing environment where the topology of Defender’s moves and the importance of guarded targets change unpredictably. The Defender must instantly switch to a new strategy optimized for the new environment, not disrupting the ongoing patrolling task, and the new strategy must be computed promptly under all circumstances. Since strategy switching may cause unintended security risks compromising the achieved protection, our solution includes mechanisms for detecting and mitigating this problem. The efficiency of our framework is evaluated experimentally.}
}