Repeated Stackelberg security games: Learning with incomplete state information

Alcantara-Jiménez, Guillermo and Julio B. Clempner

Reliability Engineering and System Safety, 2020, vol. 195, issue C

Abstract: Existing applications of Stackelberg Security Games (SSGs) have make use of Reinforcement Learning (RL) approaches to learn and adapt defenders-attackers behavior. The learning process for defenders-attackers is represented by randomized strategies for the defenders applied against adversarial strategies of the attackers, which acquire feedback on their strategies observing the target that was defended-attacked. However, must of the existing SSGs RL models feature strong assumptions including that the defenders and attackers have perfect information about the behavioral model, producing inconsistencies.

Keywords: Reinforcement learning; Incomplete information; Security games. (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:195:y:2020:i:c:s0951832019304478

DOI: 10.1016/j.ress.2019.106695

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