Robust Solutions for Multi-Defender Stackelberg Security Games

Dolev Mutzari, Yonatan Aumann, Sarit Kraus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Multi-defender Stackelberg Security Games (MSSG) have recently gained increasing attention in the literature. However, the solutions offered to date are highly sensitive, wherein even small perturbations in the attacker's utility or slight uncertainties thereof can dramatically change the defenders' resulting payoffs and alter the equilibrium. In this paper, we introduce a robust model for MSSGs, which admits solutions that are resistant to small perturbations or uncertainties in the game's parameters. First, we formally define the notion of robustness, as well as the robust MSSG model. Then, for the non-cooperative setting, we prove the existence of a robust approximate equilibrium in any such game, and provide an efficient construction thereof. For the cooperative setting, we show that any such game admits a robust approximate α-core, provide an efficient construction thereof, and prove that stronger types of the core may be empty. Interestingly, the robust solutions can substantially increase the defenders' utilities over those of the non-robust ones.

Original languageEnglish
Title of host publicationProceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
EditorsLuc De Raedt, Luc De Raedt
PublisherInternational Joint Conferences on Artificial Intelligence
Pages433-439
Number of pages7
ISBN (Electronic)9781956792003
DOIs
StatePublished - 2022
Event31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria
Duration: 23 Jul 202229 Jul 2022

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference31st International Joint Conference on Artificial Intelligence, IJCAI 2022
Country/TerritoryAustria
CityVienna
Period23/07/2229/07/22

Bibliographical note

Publisher Copyright:
© 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

Funding

∗This research has been partly supported by the Israel Science Foundation under grant 1958/20 and the EU Project TAILOR under grant 992215.

FundersFunder number
European Commission992215
Israel Science Foundation1958/20

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