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

Tomáš Brázdil, David Klaška, Antonı́n Kučera, Vı́t Musil, Petr Novotný, Vojtěch Řehák
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:244-254, 2022.

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.

Cite this Paper

BibTeX
@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.} }
Endnote
%0 Conference Paper %T On-the-fly adaptation of patrolling strategies in changing environments %A Tomáš Brázdil %A David Klaška %A Antonı́n Kučera %A Vı́t Musil %A Petr Novotný %A Vojtěch Řehák %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-brazdil22a %I PMLR %P 244--254 %U https://proceedings.mlr.press/v180/brazdil22a.html %V 180 %X 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.
APA
Brázdil, T., Klaška, D., Kučera, A., Musil, V., Novotný, P. & Řehák, V.. (2022). On-the-fly adaptation of patrolling strategies in changing environments. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:244-254 Available from https://proceedings.mlr.press/v180/brazdil22a.html.

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