Robust Solutions for Multi-Defender Stackelberg Security Games

Authors: Dolev Mutzari, Yonatan Aumann, Sarit Kraus

IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical 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.
Researcher Affiliation Academia Dolev Mutzari , Yonatan Aumann , Sarit Kraus Department of Computer Science, Bar Ilan University, Ramat Gan, Israel dolevmu@gmail.com, aumann@cs.biu.ac.il, sarit@cs.biu.ac.il
Pseudocode Yes Algorithm 1 ALLOC Input: value c, c [0, 1], G = (N, T , K, P, R) Output: a strategy profile X = (xi,t)
Open Source Code No The paper mentions 'Complete proofs can be found at [Mutzari et al., 2022]', but does not state that the code for the methodology is open-source or provide a link.
Open Datasets No The paper is theoretical and does not describe using datasets for training or evaluation. Therefore, no information about public datasets is provided.
Dataset Splits No The paper is theoretical and does not describe using datasets for training or evaluation. Therefore, no information about dataset splits is provided.
Hardware Specification No The paper is theoretical and does not describe any hardware used for experiments.
Software Dependencies No The paper is theoretical and does not describe any specific software dependencies or versions for experimental replication.
Experiment Setup No The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings.